We investigated the use of accelerated 3D readouts to obtain whole-brain, high-SNR ASL perfusion maps and reduce SAR deposition. Parallel imaging was implemented along the partition-encoding direction in a pseudo-continuous ASL sequence with background-suppression and 3D RARE Stack-Of-Spirals readout, and its performance was evaluated in three small cohorts. First, both non-accelerated and two-fold accelerated single-shot versions of the sequence were evaluated in healthy volunteers during a motor-photic task, and the performance was compared in terms of temporal SNR, GM-WM contrast, and statistical significance of the detected activation. Secondly, single-shot 1D-accelerated imaging was compared to a two-shot accelerated version to assess benefits of SNR and spatial resolution for applications in which temporal resolution is not paramount. Third, the efficacy of this approach in clinical populations was assessed by applying the single-shot 1D-accelerated version to a larger cohort of elderly volunteers.
\r\n\r\n\r\n","sample_size":4,"scanner_type":"3T Siemens Prisma D13D","acknowledgements":"This work was supported by the National Institutes of Health (http://www.nih.gov/), grants no. P41EB015893 and MH080729, by National Natural Science Foundation of China (http://www.nsfc.gov.cn/), grant no. 81471644, and Hangzhou Innovation Seed Fund.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4c8a834779883","number":1,"name":"rest eyes open","url":"http://www.cognitiveatlas.org/id/trm_4c8a834779883"}],"revision_set":[{"revision_number":"1.0.0","notes":"-- Initial Release","date_set":"2017-07-10"}],"investigator_set":[{"investigator":"John A. Detre"},{"investigator":"María A. Fernández-Seara"},{"investigator":"Yulin V. Chang"},{"investigator":"Ze Wang"},{"investigator":"Marta Vidorreta"}],"link_set":[{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000235/ds000235_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Raw Data for all subjects ","url":"https://s3.amazonaws.com/openneuro/ds000235/ds000235_R1.0.0/compressed/ds000235_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"","name":"Marta Vidorreta","website":""}]},{"accession_number":"ds000051","project_name":"Cross-language repetition priming","summary":"
Native Spanish speakers who were proficient in English performed an abstract-concrete judgment with single Spanish or English words. Each item was repeated once, either in the same language or in the other language.
\r\n","sample_size":13,"scanner_type":"Siemens Allegra","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Society Of Neuroscience Abstract ","url":"http://www.sfn.org/Annual-Meeting/Past-and-Future-Annual-Meetings/Abstract-Archive/Abstract-Archive-Detail?AbsYear=2002&AbsID=2706"},{"title":"Building Memories in Two Languages: An FMRI Study of Episodic Encoding in Bilnguals.","url":"http://www.sfn.org/Annual-Meeting/Past-and-Future-Annual-Meetings/Abstract-Archive/Abstract-Archive-Detail?AbsYear=2002&AbsID=2706"}],"task_set":[{"cogat_id":"trm_4ebd44cd88360","number":1,"name":"abstract/concrete judgment: bilingual","url":"http://www.cognitiveatlas.org/id/trm_4ebd44cd88360"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2011-11-04"},{"revision_number":"2.0.2","notes":"- Edited authors string in dataset_description.json","date_set":"2016-10-28"},{"revision_number":"2.0.1","notes":"-added authors to dataset_description.json","date_set":"2016-10-01"},{"revision_number":"2.0.0","notes":"- Converted to BIDS standard ","date_set":"2016-09-28"}],"investigator_set":[{"investigator":"Poldrack, R.A."},{"investigator":"Alvarez, R."}],"link_set":[{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000051/ds000051_R1.0.0/compressed/ds051_raw.tgz","revision":"1.0.0"},{"title":"Raw data on AWS","url":"http://s3.amazonaws.com/openneuro/ds000051/ds000051_R2.0.2/compressed/ds000051_R2.0.2_raw.zip","revision":"2.0.2"},{"title":"Raw data on AWS","url":"http://s3.amazonaws.com/openneuro/ds000051/ds000051_R2.0.1/compressed/ds000051_R2.0.1_raw.tgz","revision":"2.0.1"},{"title":"Raw data on AWS","url":"http://s3.amazonaws.com/openneuro/ds000051/ds000051_R2.0.0/compressed/ds000051_R2.0.0_raw.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000052","project_name":"Classification learning and reversal","summary":"Subjects performed two blocks of an event-related probabilistic classification learning task. They then performed two more blocks of the same task with the reward contingencies reversed.
\r\n","sample_size":13,"scanner_type":"Siemens Allegra","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Interactive memory systems in the human brain","url":"http://www.ncbi.nlm.nih.gov/pubmed/11734855"}],"task_set":[{"cogat_id":"trm_4cacf22a22d80","number":1,"name":"Probabilistic classification task","url":"http://www.cognitiveatlas.org/id/trm_4cacf22a22d80"},{"cogat_id":"trm_4cacf22a22d80","number":2,"name":"Probabilistic classification task","url":"http://www.cognitiveatlas.org/id/trm_4cacf22a22d80"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2011-11-04"},{"revision_number":"2.0.0","notes":"Repackaged in BIDS format","date_set":"2016-05-11"}],"investigator_set":[{"investigator":"Gluck, M."},{"investigator":"Myers, C."},{"investigator":"Creso Moyano, J."},{"investigator":"Shohamy, D."},{"investigator":"Pare-Blagoev, E. J."},{"investigator":"Clark, J."},{"investigator":"Poldrack, R.A."}],"link_set":[{"title":"Metadata and derivatives","url":"https://s3.amazonaws.com/openneuro/ds000052/ds000052_R2.0.0/compressed/ds052_R2.0.0_metadata_derivatives.tgz","revision":"2.0.0"},{"title":"Models","url":"https://s3.amazonaws.com/openneuro/ds000052/ds000052_R1.0.0/compressed/ds052_models.tgz","revision":"1.0.0"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000052/ds000052_R1.0.0/compressed/ds052_raw.tgz","revision":"1.0.0"},{"title":"Data for all subjects","url":"https://s3.amazonaws.com/openneuro/ds000052/ds000052_R2.0.0/compressed/ds052_R2.0.0_01-14.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000223","project_name":"Magnitude Effect","summary":"This is an fMRI study of the magnitude effect in intertemporal choice, which refers to the phenomenon that people become more patient as the magnitude of all of their options increases. We scanned participants in 2 blocked sessions each of low and high magnitude decisions. The study was conducted at two different sites with different scanners and data acquisition specifications.
\r\n
\r\nTask Instruction video
Participants were randomly assigned to two experimental groups. The first group (n=23, mean age = 23.43, SD=2.33, range: 21–28) was recruited to taste 8 commercially available drinks (referred to as regular products) during a morning session. The second group (n=22, mean age = 24.67, SD=3.37, range: 21–33) was recruited to taste 6 ONS (oral nutritional supplements) products in the afternoon. These two groups were independent (i.e. no participant participated in both groups).
\r\nParticipants engaged in a tasting task containing 48 or 36 trials for the regular products and ONS group, respectively. During the course of the experiment, participants received visual cues and instructions in Dutch via a paradigm constructed in E-prime (Psychology Software Tools Inc., Pittsburgh). Every flavor stimulus was delivered 6 times balanced over all imaging runs and counterbalanced between participants. The paradigm was presented during four and three imaging runs, for the regular products and ONS group, respectively. Each imaging run lasted for approximately 15 minutes (depending on reaction times) and contained a series of 12 trials. During each trial, participants were warned for an upcoming taste delivery by an asterisk appearing centered on the screen (duration: 2s.). Subsequently, 2 ml of a taste stimulus was delivered in the mouth and participants were instructed to taste this stimulus with the cue "Taste" (in Dutch: "Proeven", duration: 3.5s.). After tasting, participants were instructed to swallow the solution, cued as "Swallow" (in Dutch: "Slikken", duration: 4s.), followed by a period in which they needed to passively "Judge" the taste (in Dutch: "Beoordelen", duration: 22.5s.). We chose this long period to assure that BOLD responses associated with rating and tasting had minimal overlap. Finally, a 7-point Likert scale appeared on the screen, ranging from "very unpleasant" to "very pleasant". Participants were instructed to express perceived pleasantness of the taste on the scale by using a button box held in their right hand. Every trial ended with a rinsing procedure, in which participants received a 2 ml bolus of a 5% artificial saliva solution (Saliva Orthana, TM) twice. The entire paradigm lasted for approximately 90 minutes, in which either 288 ml or 216 ml of liquid was consumed, for the regular products and ONS group, respectively. As baseline, we included four 15-second periods in each imaging run within both data sets, during which the participant was looking at a black screen with a red cross centered in the middle.
\r\nMRI scans were performed using a 3-Tesla MR scanner (Philips Intera, Best, the Netherlands) equipped with a 32-channel head coil. A T1-weighted 3D fast field echo (FFE) whole brain image was obtained in transverse orientation for anatomical reference. Acquisition parameters: field of view (FOV) 256 × 232 × 170 mm3 (rl, ap, fh); voxel size 1 mm isotropic; TR = 9 ms; TE = 3.5 ms; flip angle 8º; SENSE factors: 2.5, 1 (ap, fh); 170 slices, scan duration = 246.3s. Functional brain images were acquired in sagittal orientation using the Principles of Echo-Shifting with a Train of Observations (PRESTO) sequence. Acquisition parameters: FOV 153 × 230 × 230 mm3 (rl, ap, fh); voxel size 2.87 × 2.87 × 3 mm3; TR = 20 ms; TE = 30 ms; flip angle 7º; SENSE factors: 1.9, 1.9 (rl, ap); scan time per volume 1.532s. As the experiment was self-paced, the number of volumes per imaging run ranged between 580 and 600.
\r\nDue to technical difficulties with the gustometer or scanner, data was missing for several trials. We removed participants missing more than 25% of their data (3 ONS group, 1 regular drinks group). Furthermore, 1 participant in the regular drinks group was removed due to a brain abnormality. Therefore, fMRI analysis was performed on data from 19 and 21 participants for the ONS and regular drinks groups, respectively.
\r\nWe experienced technical difficulties with the PRESTO sequence. As a result, several PRESTO images were missing at the start of imaging runs for 10 participants (on average 0.054% per data set).
NOTE: This study has two datasets asscoiated with it. One for ONS products(ds000218) and other for RP products (ds000219)
\r\n\r\nThe paradigm is similar to http://www.sciencedirect.com/science/article/pii/S1053811915005674 but with some adjustments.
\r\n","sample_size":19,"scanner_type":"Philips Intera 3T","acknowledgements":"Funding source: TIFN SL001","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":false,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Flavor pleasantness processing in the ventral emotion network","url":"https://www.ncbi.nlm.nih.gov/pubmed/?term=Flavor+pleasantness+processing+in+the+ventral+emotion+network"}],"task_set":[],"revision_set":[{"revision_number":"1.0.1","notes":"- Corrected dataset_description.json to be BIDS compatible\r\n- Added mriqc","date_set":"2017-09-15"},{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-02-01"}],"investigator_set":[{"investigator":"Gert J. ter Horst"},{"investigator":"L. Nanetti"},{"investigator":"Remco J. Renken"},{"investigator":"Liselore Weitkamp"},{"investigator":"Jelle R. Dalenberg"}],"link_set":[{"title":"MRIQC Func group report","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.1/uncompressed/derivatives/reports/bold_group.html","revision":"1.0.1"},{"title":"MRIQC T1w group report","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.1/uncompressed/derivatives/reports/T1w_group.html","revision":"1.0.1"},{"title":"Data for Subjects 17-22 (3.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.1/compressed/ds000218_R1.0.1_sub17-22.zip","revision":"1.0.1"},{"title":"Data for Subjects 09-15 (4.1 GB)","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.1/compressed/ds000218_R1.0.1_sub09-15.zip","revision":"1.0.1"},{"title":"Data for subjects 01-08 (3.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.1/compressed/ds000218_R1.0.1_sub01-08.zip","revision":"1.0.1"},{"title":"Metadata and MRIQC","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.1/compressed/ds000218_R1.0.1_metadata_derivatives.zip","revision":"1.0.1"},{"title":"Raw data for Subjects 17-22","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.0/compressed/ds000218_R1.0.0_sub17-22.zip","revision":"1.0.0"},{"title":"Raw data for Subjects 9-15","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.0/compressed/ds000218_R1.0.0_sub09-15.zip","revision":"1.0.0"},{"title":"Raw data for Subjects 1-8","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.0/compressed/ds000218_R1.0.0_sub01-08.zip","revision":"1.0.0"},{"title":"Metadata","url":"https://s3.amazonaws.com/openneuro/ds000218/ds000218_R1.0.0/compressed/ds000218_R1.0.0_metadata.zip","revision":"1.0.0"}],"contacts":[{"email":"j.r.dalenberg@gmail.com","name":"Jelle R. Dalenberg","website":""}]},{"accession_number":"ds000224","project_name":"The Midnight Scan Club (MSC) dataset","summary":"The potential long-term effects of Traumatic Brain Injury (TBI) are poorly understood. Repeated concussions have been associated with an elevated incidence of Alzheimer’s disease (AD) as well as chronic traumatic encephalopathy (CTE). There are growing concerns about the long-term neurologic consequences of head impact exposure from routine participation in contact sports (e.g., boxing, football). Brain autopsies of athletes with confirmed CTE have demonstrated tau-immunoreactive neurofibrillary tangles and neuropil threads (known as tauopathy). The relationship between exposure to repetitive head impact and the subsequent development of chronic neurodegenerative disease has not been established. Further, as the diagnosis of CTE (defined by the presence of tauopathy) is presently made at autopsy, clinical tools and biomarkers for detecting it remain to be defined. We aim to determine whether these individuals are on the same trajectory of neurodegenerative disease seen in AD or in CTE. Our study will utilize both [18F]-AV-1451 and [18F]-T807 PET imaging to investigate amyloid and tau accumulation in subjects with a history of concussions. We will obtain MRI, PET, and neurocognitive data in a cohort of 25 subjects with a history of TBIs and a cohort of 25 controls. This dataset currently contains data acquired from two sessions (3.9 years apart) from a single subject, including two T1w scans and two PET scans.
\r\n","sample_size":1,"scanner_type":"Siemens","acknowledgements":"The Alzheimer’s Disease Drug Foundation; NIH grants NINDS 5U01NS086625 and NICHD K01HD074651-01A; The Werber Family Foundation ","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Cerebral [18 F]T807/AV1451 retention pattern in clinically probable CTE resembles pathognomonic distribution of CTE tauopathy","url":"http://www.nature.com/tp/journal/v6/n9/full/tp2016175a.html"}],"task_set":[],"revision_set":[{"revision_number":"1.0.2","notes":"- Changed funding into string in dataset_description.json","date_set":"2017-09-16"},{"revision_number":"1.0.1","notes":" - Added fields to dataset_description.json\r\n - Fixed formatting issues in pet json files\r\n - Added CHANGES.txt","date_set":"2016-07-06"},{"revision_number":"1.0.0","notes":"Initial release","date_set":"2016-06-28"}],"investigator_set":[{"investigator":"Sam Gandy"},{"investigator":"Patrick R. Hof"},{"investigator":"Mary Sano"},{"investigator":"Robert C. Cantu"},{"investigator":"James R. Stone"},{"investigator":"Steven T. DeKosky"},{"investigator":"Cheuk Y. Tang"},{"investigator":"Edmund Wong"},{"investigator":"Bradley N. Delman"},{"investigator":"Kristen Dams-O’Connor"},{"investigator":"Wayne Gordon"},{"investigator":"Barry Jordan"},{"investigator":"Laili Soleimani"},{"investigator":"Karin Knesaurek"},{"investigator":"Lale Kostakoglu"},{"investigator":"Jennifer A. Short"},{"investigator":"Corey Fernandez"},{"investigator":"Mariel Y. Pullman"},{"investigator":"Dara Dickstein"}],"link_set":[{"title":"Raw data on AWS ","url":"https://s3.amazonaws.com/openneuro/ds000204/ds000204_R1.0.2/compressed/ds000204_R1.0.2.zip","revision":"1.0.2"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openfmri/tarballs/ds204_R1.0.1.tar","revision":"1.0.1"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openfmri/tarballs/ds204.tar","revision":"1.0.0"}],"contacts":[{"email":"samuel.gandy@mssm.edu","name":"","website":""}]},{"accession_number":"ds000205","project_name":"Affective Videos","summary":"The goal of this study was to determine whether affective states can be similarly identified when participants view dynamic naturalistic audiovisual stimuli. Eleven participants viewed 5s audiovisual clips in a passive viewing task in the scanner. Valence and arousal for individual trials were identified both within and across participants based on distributed patterns of activity in areas selectively responsive to audiovisual naturalistic stimuli while controlling for lower level features of the stimuli. In addition, the brain regions identified by searchlight analyses to represent valence and arousal were consistent with previously identified regions associated with emotion processing.
\r\n","sample_size":11,"scanner_type":"Siemens Magnetom Trio 3.0T whole-body scanner (Siemens, Erlangen, Germany)","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Identifying Core Affect in Individuals from fMRI Responses to Dynamic Naturalistic Audiovisual Stimuli","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012606/"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"-Initial release","date_set":"2016-08-16"}],"investigator_set":[{"investigator":"Douglas H. Wedell"},{"investigator":"Svetlana V. Shinkareva"},{"investigator":"Jing Wang"},{"investigator":"Jongwan Kim"}],"link_set":[{"title":"Data for All Subjects (1-11)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds205_R1.0.0_sub-01-11.tgz","revision":"1.0.0"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds205_R1.0.0_metadata.tgz","revision":"1.0.0"}],"contacts":[{"email":"kim253@email.sc.edu","name":"Jongwan Kim","website":""}]},{"accession_number":"ds000208","project_name":"Brain connectivity predicts placebo response across chronic pain clinical trials","summary":"Placebo response is extensively studied in healthy subjects and for experimental manipulations. However, in the clinical setting it has been primarily relegated to statistical confounds. Here, for the first time we examine predictability of future placebo response in the clinical setting in patients with chronic osteoarthritis pain. We examine resting state fMRI brain connectivity prior to start of clinical trial, and in the setting of neutral instructions regarding treatment. Our results show that clinical placebo pill ingestion shows stronger analgesia than no treatment, is predictable from resting state BOLD fMRI, and right mid-frontal gyrus degree count (extent of functional connectivity) identifies placebo pill responders in one trial and can be validated (95% correct) in the placebo group of a second trial but not in the active drug treatment (duloxetine) group. By modeling the expected placebo response in subjects receiving active drug treatment, we uncover a placebo-corrected drug response predictive brain signal, and show that in some subjects active drug tends to enhance, while in others interferes, with predicted placebo response. Together, these results provide evidence for clinical placebo being predetermined by brain biology, and show that brain imaging may also identify a placebo-corrected prediction of response to active treatment.
\r\n","sample_size":76,"scanner_type":"3T Siemens Trio whole-body scanner with echo-planar imaging (EPI) capability","acknowledgements":"Contributors:\r\nP.T. collected, analyzed and interpreted the data and prepared and wrote the manuscript, A.M. and E.V.-P., contributed to data collection and analysis. T.J.S. designed the study, assessed patients for eligibility, and interpreted the data. A.V.A. designed the study, interpreted the data and wrote the manuscript. M.N.B analyzed and interpreted the data and wrote the manuscript. All authors reviewed and edited the manuscript.\r\n\r\nAcknowledgements:\r\nWe thank the staff and faculty of Center of Translational Imaging at Northwestern University and students and staff of Apkarian Lab and all study participants.\r\n\r\nFunding:\r\n\r\nEli Lilly Pharmaceuticals funded study 2 (IIT number: F1J-US-XO61). This research was partially also supported by grants from National Institute of Neurological Disorders and Stroke (NS035115), and National Center for Complementary and Integrative Health (AT007987) of U.S. National Institutes of Health. P.T. and E.V.-P. were supported by the Canadian Institutes of Health Research.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Brain Connectivity Predicts Placebo Response across Chronic Pain Clinical Trials","url":"https://www.ncbi.nlm.nih.gov/pubmed/27788130"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":" - Initial release","date_set":"2016-09-22"}],"investigator_set":[{"investigator":"Marwan N. Baliki"},{"investigator":"A. Vania Apkarian"},{"investigator":"Thomas J. Schnitzer"},{"investigator":"Etienne Vachon-Presseau"},{"investigator":"Ali Mansour"},{"investigator":"Pascal Tétreault"}],"link_set":[{"title":"Data for Subjects 39-76","url":"http://openfmri.s3.amazonaws.com/tarballs/ds208_R1.0.0_sub39-76.tgz","revision":"1.0.0"},{"title":"Data for Subjects 1-38","url":"http://openfmri.s3.amazonaws.com/tarballs/ds208_R1.0.0_sub01-38.tgz","revision":"1.0.0"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds208_R1.0.0_metadata.tgz","revision":"1.0.0"}],"contacts":[{"email":"tetreault.pascal@gmail.com","name":"Pascal Tetreault","website":""}]},{"accession_number":"ds000158","project_name":"The human Voice Areas: spatial organisation and inter-individual variability in temporal and extra-temporal cortices","summary":"FMRI studies increasingly examine functions and properties of non-primary areas of human auditory cortex. However there is currently no standardized localization procedure to reliably identify specific areas across individuals such as the standard 'localizers' available in the visual domain. Here we present an fMRI 'voice localizer' scan allowing rapid and reliable localization of the voice-sensitive 'temporal voice areas' (TVA) of human auditory cortex. We describe results obtained using this standardized localizer scan in a large cohort of normal adult subjects. Most participants (94%) showed bilateral patches of significantly greater response to vocal than non-vocal sounds along the superior temporal sulcus/gyrus (STS/STG). Individual activation patterns, although reproducible, showed high inter-individual variability in precise anatomical location. Cluster analysis of individual peaks from the large cohort highlighted three bilateral clusters of voice-sensitivity, or "voice patches" along posterior (TVAp), mid (TVAm) and anterior (TVAa) STS/STG, respectively. A series of extra-temporal areas including bilateral inferior prefrontal cortex and amygdalae showed small, but reliable voice sensitivity as part of a large-scale cerebral voice network. Stimuli for the voice localizer scan and probabilistic maps in MNI space are available for download.
\r\n","sample_size":218,"scanner_type":"Siemens Tim Trio 3T","acknowledgements":"Data collection was supported by BBSRC grants BB/E003958/1, BBJ003654/1 and BB/I022287/1, ESRC-MRC large grant RES-060-25-0010.","license_title":"CC-BY 4.0","license_url":"http://creativecommons.org/licenses/by/4.0/legalcode","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"The human voice areas: Spatial organization and inter-individual variability in temporal and extra-temporal cortices.","url":"http://www.ncbi.nlm.nih.gov/pubmed/26116964"}],"task_set":[],"revision_set":[{"revision_number":"1.0.1","notes":" - Converted to BIDS format\r\n - Subtracted 1 from each subject number to allow subject numbers to start at 001\r\n - Added events file","date_set":"2016-12-22"},{"revision_number":"1.0.0","notes":" - Initial release","date_set":"2016-08-23"}],"investigator_set":[{"investigator":"Fleming, D"},{"investigator":"Watson, RH"},{"investigator":"Bestelmeyer, PEG"},{"investigator":"Charest, I"},{"investigator":"Latinus, M"},{"investigator":"Valdes-Sosa, M"},{"investigator":"Gorgolewski, KJ"},{"investigator":"McAleer, P"},{"investigator":"Belin, P"},{"investigator":"Pernet, CR"}],"link_set":[{"title":"Data for subjects 112-167 (3.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000158/ds000158_R1.0.1/compressed/ds000158_R1.0.1_sub112-167.zip","revision":"1.0.1"},{"title":"Data for subjects 056-111 (3.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000158/ds000158_R1.0.1/compressed/ds000158_R1.0.1_sub056-111.zip","revision":"1.0.1"},{"title":"Stimuli (11 MB)","url":"https://s3.amazonaws.com/openneuro/ds000158/ds000158_R1.0.1/compressed/ds000158_R1.0.1_stimuli.zip","revision":"1.0.1"},{"title":"Workflow (30.9 MB)","url":"https://s3.amazonaws.com/openneuro/ds000158/ds000158_R1.0.1/compressed/ds000158_R1.0.1_workflow.zip","revision":"1.0.1"},{"title":"Data for subjects 168-217 (3.2 GB)","url":"https://s3.amazonaws.com/openneuro/ds000158/ds000158_R1.0.1/compressed/ds000158_R1.0.1_sub168-217.zip","revision":"1.0.1"},{"title":"Data for subjects 001-055 (3.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000158/ds000158_R1.0.1/compressed/ds000158_R1.0.1_sub001-055.zip","revision":"1.0.1"},{"title":"Metadata (3.3 KB)","url":"https://s3.amazonaws.com/openneuro/ds000158/ds000158_R1.0.1/compressed/ds000158_R1.0.1_metadata.zip","revision":"1.0.1"},{"title":"Workflow","url":"http://openfmri.s3.amazonaws.com/tarballs/ds158_R1.0.0_workflow.zip","revision":"1.0.0"},{"title":"Voice Localizer","url":"http://openfmri.s3.amazonaws.com/tarballs/ds158_R1.0.0_voice_localizer.zip","revision":"1.0.0"},{"title":"Data for subjects 100-218","url":"http://openfmri.s3.amazonaws.com/tarballs/ds158_R1.0.0_sub100-218.tar","revision":"1.0.0"},{"title":"Data for subjects 1-99","url":"http://openfmri.s3.amazonaws.com/tarballs/ds158_R1.0.0_sub001-099.tar","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000107","project_name":"Word and object processing","summary":"Subjects performed a visual one-back with four categories of items: written words, objects, scrambled objects and consonant letter strings.
\r\n","sample_size":49,"scanner_type":"Siemens 1.5T","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Consistency and variability in functional localisers","url":"http://www.ncbi.nlm.nih.gov/pubmed/19289173"}],"task_set":[{"cogat_id":"trm_4ebd482eba5b1","number":1,"name":"word one-back task","url":"http://www.cognitiveatlas.org/id/trm_4ebd482eba5b1"},{"cogat_id":"trm_4ebd47b8bab6b","number":2,"name":"object one-back task","url":"http://www.cognitiveatlas.org/id/trm_4ebd47b8bab6b"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2011-11-04"},{"revision_number":"2.0.2","notes":"- Added note about subjects in readme where T1 images are corrupted/stripped during defacing.","date_set":"2017-01-10"},{"revision_number":"2.0.1","notes":"- Edited authors string in dataset_description.json","date_set":"2016-10-28"},{"revision_number":"2.0.0","notes":"- converted to BIDS standard ","date_set":"2016-10-02"}],"investigator_set":[{"investigator":"Devlin, J."},{"investigator":"Knierim, I."},{"investigator":"Pattamadilok, C."},{"investigator":"Duncan, K."}],"link_set":[{"title":"Models","url":"http://openfmri.s3.amazonaws.com/tarballs/ds107_models.tgz","revision":"1.0.0"},{"title":"Raw data checksums","url":"http://openfmri.s3.amazonaws.com/tarballs/ds107_raw_checksums.txt","revision":"1.0.0"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds107_raw.tgz","revision":"1.0.0"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000107/ds000107_R2.0.2/compressed/ds000107_R2.0.2.zip","revision":"2.0.2"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000107_R2.0.1_raw.zip","revision":"2.0.1"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000107_R2.0.0_raw.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000110","project_name":"Incidental encoding task (Posner Cueing Paradigm)","summary":"Subjects were scanned while incidentally encoding a series of visually presented real objects and greebles (meaningless objects) in a variant of the Posner cueing paradigm. Subjects covertly shifted their attention to the left or right of fixation, as cued by a centrally-presented arrow prior to item onset, and made a real object versus greeble judgment about the stimulus appearing in the cued or uncued location. Items appeared in the uncued location with a probability of .18. Subjects performed an unscanned memory test following encoding, in which they indicated their memory for old and new real objects using the following four responses: high confident old, low confident old, low confident new, high confident new. For trials in which subjects responded with one of the two old responses, a source memory judgment about the location (left or right side of the screen) of the object at study followed the recognition judgment.
\r\n","sample_size":18,"scanner_type":"3 T Signa MR scanner","acknowledgements":"This research was supported by National Institute of Mental Health Grants 5R01-MH080309 and F32-MH084475.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Dissociable effects of top-down and bottom-up attention during episodic encoding","url":"http://www.ncbi.nlm.nih.gov/pubmed/21880922"}],"task_set":[{"cogat_id":"trm_50df0dd9d0b6f","number":1,"name":"Incidental encoding task","url":"http://www.cognitiveatlas.org/id/trm_50df0dd9d0b6f"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2013-04-16"},{"revision_number":"2.0.1","notes":"- Edited authors string in dataset_description.json","date_set":"2016-10-28"},{"revision_number":"2.0.0","notes":"- Converted to BIDS Standard","date_set":"2016-10-07"}],"investigator_set":[{"investigator":"Anthony D. Wagner"},{"investigator":"J. Benjamin Hutchinson"},{"investigator":"Melina R. Uncapher"}],"link_set":[{"title":"Release History","url":"http://openfmri.s3.amazonaws.com/tarballs/ds110_release_history.txt","revision":"1.0.0"},{"title":"Raw data checksums","url":"http://openfmri.s3.amazonaws.com/tarballs/ds110_raw_checksums.txt","revision":"1.0.0"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds110_raw.tgz","revision":"1.0.0"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000110_R2.0.1_raw.zip","revision":"2.0.1"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds110_R2.0.0_raw.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000109","project_name":"False belief task","summary":"Participants read stories and answered questions that referred to either a person's false belief (mental trials) or to outdated physical representations, such as an old photograph (physical trials). Participants saw twelve stories of each type across two functional runs.
\r\n","sample_size":33,"scanner_type":"3T Tim Trio MRI scanner (Siemens).","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Social-Cognitive Deficits in Normal Aging","url":"http://www.ncbi.nlm.nih.gov/pubmed/22514317"}],"task_set":[{"cogat_id":"trm_4f2456027809f","number":1,"name":"false belief task","url":"http://www.cognitiveatlas.org/id/trm_4f2456027809f"}],"revision_set":[{"revision_number":"2.0.2","notes":"- Added participants.tsv","date_set":"2017-04-24"},{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2013-04-16"},{"revision_number":"2.0.1","notes":"- Edited authors string in dataset_description.json","date_set":"2016-10-28"},{"revision_number":"2.0.0","notes":"- converted to BIDS standard ","date_set":"2016-10-02"}],"investigator_set":[{"investigator":"Jason P. Mitchell"},{"investigator":"Eshin Jolly"},{"investigator":"Joseph M. Moran"}],"link_set":[{"title":"Raw Data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000109/ds000109_R2.0.2/compressed/ds000109_R2.0.2_raw.zip","revision":"2.0.2"},{"title":"Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds109_metadata.tgz","revision":"1.0.0"},{"title":"Raw data checksums","url":"http://openfmri.s3.amazonaws.com/tarballs/ds109_raw_checksums.txt","revision":"1.0.0"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds109_raw.tgz","revision":"1.0.0"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000109_R2.0.1_raw.zip","revision":"2.0.1"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000109_R2.0.0_raw.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000239","project_name":"Maclaren test-retest brain volume dataset","summary":"Retest T1-weighted images from 3 participants, each scanned 40 times.
\r\n","sample_size":3,"scanner_type":"GE MR750, DV22.0_V02_1122.a, XRMB gradient set","acknowledgements":"Data reformatted to BIDS and published on openfmri.org by Gustav Nilsonne with assistance by Daniel Samsami","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Reliability of brain volume measurements: a test-retest dataset.","url":"https://www.ncbi.nlm.nih.gov/pubmed/25977792"}],"task_set":[],"revision_set":[{"revision_number":"1.0.1","notes":"-- Corrected dataset_description.json file","date_set":"2017-08-11"},{"revision_number":"1.0.0","notes":"-- Initial Release","date_set":"2017-08-09"}],"investigator_set":[{"investigator":"Roland Bammer"},{"investigator":"Nancy Fischbein"},{"investigator":"Sjoerd B. Vos"},{"investigator":"Zhaoying Han"},{"investigator":"Julian Maclaren"}],"link_set":[{"title":"Data for all Subjects (857 MB)","url":"https://s3.amazonaws.com/openneuro/ds000239/ds000239_R1.0.1/compressed/ds000239_R1.0.1.zip","revision":"1.0.1"},{"title":"Data for all Subjects (857 MB)","url":"https://s3.amazonaws.com/openneuro/ds000239/ds000239_R1.0.0/compressed/ds000239_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"jmacl@stanford.edu","name":"Julian Maclaren","website":""}]},{"accession_number":"ds000201","project_name":"A multimodal brain imaging dataset on sleep deprivation in young and old humans: The Sleepy Brain Project I","summary":"The Stockholm Sleepy Brain Study I is a functional brain imaging study where 48 younger (20-30 years) and 36 older (65-75 years) healthy participants underwent magnetic resonance imaging after normal sleep and partial sleep deprivation in a crossover design. We performed three experiments investigating emotional mimicry, empathy for pain, and cognitive reappraisal, as well as resting state functional magnetic resonance imaging (fMRI). We also acquired T1- and T2-weighted structural images and diffusion tensor images. On the night before imaging, participants were monitored with ambulatory polysomnography and were instructed to sleep either as usual or only three hours. Participants came to the scanner the following evening. Besides MRI scanning, participants underwent behavioral tests and contributed blood samples, which have been stored in a biobank and used for DNA analyses. Participants also completed a variety of self-report measures. The resulting multimodal dataset may be useful for hypothesis generation or independent validation of effects of sleep deprivation and aging, as well as investigation of cross-sectional associations between our different outcomes.
\r\n\r\nThe faces and arrows task-based fMRI experiment data will be published at a later time.
\r\n\r\nRaw polysomnography data are available upon request.
\r\n\r\nThe currently published data set includes:
\r\n\r\nHands task-based fMRI data
\r\n\r\nResting state fMRI data
\r\n\r\nDemographics, surveys, questionnaire data
\r\n\r\nEye tracking data
\r\n\r\nHigh resolution T1-weighted and T2-weighted structural scans
\r\n\r\nB0 field map data
\r\n\r\nDiffusion-weighted imaging scans
\r\n\r\nDNA analysis results
\r\n\r\nData Descriptor Manuscript
\r\n\r\nA preprint of the corresponding data descriptor manuscript (submitted) is available at the Karolinska Institutet open archive.
\r\n","sample_size":84,"scanner_type":"GE Discovery 3T ","acknowledgements":"We are grateful to Diana Cortes and Roberta Nagai for assistance with polysomnography recordings, to Birgitta Mannerstedt Fogelfors for assistance with screening, instructions to participants, and blood sampling, to Rouslan Sitnikov and Jonathan Berrebi for assistance with MRI sequences and auxiliary equipment, and to Hannes Ingre for entering sleep diary data into a spreadsheet.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Effects of late-night short-sleep on in-home polysomnography: relation to adult age and sex","url":"http://onlinelibrary.wiley.com/doi/10.1111/jsr.12626/abstract;jsessionid=BC846ABD692347FF833D5848F2BDF3CC.f04t02"},{"title":"The effect of sleep restriction on empathy for pain: An fMRI study in younger and older adults","url":"https://www.nature.com/articles/s41598-017-12098-9"},{"title":"Intrinsic brain connectivity after partial sleep deprivation in young and older adults: results from the Stockholm Sleepy Brain study","url":"https://www.nature.com/articles/s41598-017-09744-7"}],"task_set":[{"cogat_id":"trm_4c8a834779883","number":1,"name":"rest eyes open","url":"http://www.cognitiveatlas.org/id/trm_4c8a834779883"}],"revision_set":[{"revision_number":"1.0.5","notes":"- Rearranged data in the zip archive \r\n","date_set":"2017-12-02"},{"revision_number":"1.0.4","notes":" - Added resting state data and hands task data\r\n - Fixed dataset_description.json and task-PVT_beh.json formatting issues\r\n - In participants.tsv, replaced empty cells and 'NA' with 'n/a'\r\n - Replaced participants_codebook.tsv with participants.json\r\n - Moved extra particpant data to a separate file in sourcedata\r\n - Renamed sessions.tsv files to comply with BIDS\r\n - Updated README\r\n - Updated and renamed task-workingmemorytest.json","date_set":"2017-10-09"},{"revision_number":"1.0.3","notes":"- Added a note to README mentioning that polysomnography data is available upon request\r\nfrom the submitter","date_set":"2016-10-17"},{"revision_number":"1.0.0","notes":" - Initial publishing","date_set":"2016-05-19"},{"revision_number":"1.0.2","notes":"- Removed polysomnography recording data for all subjects. This was requested by the submitter.","date_set":"2016-10-13"},{"revision_number":"1.0.1","notes":" - Updated participants.tsv with more fields\r\n - Added PVT data","date_set":"2016-09-10"}],"investigator_set":[{"investigator":"Torbjörn Åkerstedt"},{"investigator":"Mats Lekander"},{"investigator":"Håkan Fischer"},{"investigator":"Göran Kecklund"},{"investigator":"Peter Fransson"},{"investigator":"Predrag Petrovic"},{"investigator":"John Axelsson"},{"investigator":"Tina Sundelin"},{"investigator":"Kristoffer NT Månsson"},{"investigator":"Jia Jia Liu"},{"investigator":"Catharina Lavebratt"},{"investigator":"Johanna Schwarz"},{"investigator":"Hanna Å Thuné"},{"investigator":"Paolo d’Onofrio"},{"investigator":"Sandra Tamm"},{"investigator":"Gustav Nilsonne"}],"link_set":[{"title":"Data for subjects 9090-9100","url":"https://s3.amazonaws.com/openneuro/ds000201/ds000201_R1.0.5/compressed/ds000201_R1.0.5_sub9090-9100.zip","revision":"1.0.5"},{"title":"Data for subjects 9080-9089","url":"https://s3.amazonaws.com/openneuro/ds000201/ds000201_R1.0.5/compressed/ds000201_R1.0.5_sub9080-9089.zip","revision":"1.0.5"},{"title":"Data for subjects 9070-9079","url":"https://s3.amazonaws.com/openneuro/ds000201/ds000201_R1.0.5/compressed/ds000201_R1.0.5_sub9070-9079.zip","revision":"1.0.5"},{"title":"Data for subjects 9060-9069","url":"https://s3.amazonaws.com/openneuro/ds000201/ds000201_R1.0.5/compressed/ds000201_R1.0.5_sub9060-9069.zip","revision":"1.0.5"},{"title":"Data for subjects 9050-9059","url":"https://s3.amazonaws.com/openneuro/ds000201/ds000201_R1.0.5/compressed/ds000201_R1.0.5_sub9050-9059.zip","revision":"1.0.5"},{"title":"Data for subjects 9040-9049","url":"https://s3.amazonaws.com/openneuro/ds000201/ds000201_R1.0.5/compressed/ds000201_R1.0.5_sub9040-9049.zip","revision":"1.0.5"},{"title":"Data for subjects 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subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds201_R1.0.0_dwi.tar","revision":"1.0.0"},{"title":"B0 field mapping data for all subjects (522 MB)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds201_R1.0.0_fieldmap.tar","revision":"1.0.0"},{"title":"High-resolution T2-weighted imaging data for all subjects (6.6 GB)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds201_R1.0.0_T2-weighted.tar","revision":"1.0.0"}],"contacts":[{"email":"gustav.nilsonne@ki.se","name":"Gustav Nilsonne","website":""}]},{"accession_number":"ds000119","project_name":"Maturational Changes in Anterior Cingulate and Frontoparietal Recruitment Support the Development of Error Processing and Inhibitory Control (Antistate)","summary":"Documenting the development of the functional anatomy underlying error processing is critically important for understanding age-related improvements in cognitive performance. Here we used functional magnetic resonance imaging to examine time courses of brain activity in 73 individuals aged 8–27 years during correct and incorrect performance of an oculomotor task requiring inhibitory control. Canonical eye-movement regions showed increased activity for correct versus error trials but no differences between children, adolescents and young adults, suggesting that core task processes are in place early in development. Anterior cingulate cortex (ACC) was a central focus. In rostral ACC all age groups showed significant deactivation during correct but not error trials, consistent with the proposal that such deactivation reflects suspension of a “default mode” necessary for effective controlled performance. In contrast, dorsal ACC showed increased and extended modulation for error versus correct trials in adults, which, in children and adolescents, was significantly attenuated. Further, younger age groups showed reduced activity in posterior attentional regions, relying instead on increased recruitment of regions within prefrontal cortex. This work suggests that functional changes in dorsal ACC associated with error regulation and error-feedback utilization, coupled with changes in the recruitment of “long-range” attentional networks, underlie age-related improvements in performance.
\r\n","sample_size":73,"scanner_type":"3T Siemens Allegra MRI scanner","acknowledgements":"We thank Mark McAvoy and Abraham Snyder for support and development of functional data analysis procedures. Enami Yasui provided assistance with data collection.\r\nNational Institutes of Mental Health (NIMH RO1 MH067924).","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Maturational Changes in Anterior Cingulate and Frontoparietal Recruitment Support the Development of Error Processing and Inhibitory Control.","url":"http://www.ncbi.nlm.nih.gov/pubmed/18281300"}],"task_set":[{"cogat_id":"tsk_4a57abb949869","number":1,"name":"antisaccade/prosaccade task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949869"}],"revision_set":[{"revision_number":"2.0.1","notes":"- Deleted duplicate files from derivatives/mriqc","date_set":"2016-10-29"},{"revision_number":"2.0.0","notes":"Repackaged in BIDS format.","date_set":"2016-03-29"},{"revision_number":"1.0.0","notes":"Raw data initially published","date_set":"2016-01-29"}],"investigator_set":[{"investigator":"B. Luna"},{"investigator":"M. E. Wheeler"},{"investigator":"Velanova, K."}],"link_set":[{"title":"Raw data for Subjects 70 - 78","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000119_R2.0.1_sub70-78.zip","revision":"2.0.1"},{"title":"Raw data for Subjects 40 - 69","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000119_R2.0.1_sub40-69.zip","revision":"2.0.1"},{"title":"Raw data for Subjects 1 - 39","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000119_R2.0.1_sub01-39.zip","revision":"2.0.1"},{"title":"Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000119_R2.0.1_metadata.zip","revision":"2.0.1"},{"title":"Raw data for Subjects 60 - 78","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_R2.0.0_60-78.tgz","revision":"2.0.0"},{"title":"Raw data for Subjects 32 - 59","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_R2.0.0_33-59.tgz","revision":"2.0.0"},{"title":"Raw data for Subjects 01 - 31","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_R2.0.0_01-31.tgz","revision":"2.0.0"},{"title":"Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_R2.0.0_metadata_derivatives.tgz","revision":"2.0.0"},{"title":"Subjects 070 - 078","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_sub070-078.tgz","revision":"1.0.0"},{"title":"Subjects 061 - 069","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_sub061-069.tgz","revision":"1.0.0"},{"title":"Subjects 052 - 060","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_sub052-060.tgz","revision":"1.0.0"},{"title":"Subjects 043 - 051","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_sub043-051.tgz","revision":"1.0.0"},{"title":"Subjects 033 - 042","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_sub033-042.tgz","revision":"1.0.0"},{"title":"Subjects 019 - 031","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_sub019-031.tgz","revision":"1.0.0"},{"title":"Subjects 010 - 018","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_sub010-018.tgz","revision":"1.0.0"},{"title":"Subjects 001 - 009","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_sub001-009.tgz","revision":"1.0.0"},{"title":"Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds119_metadata.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000249","project_name":"Value generalization in human avoidance learning","summary":"Generalization during aversive decision-making allows us to avoid a broad range of potential threats following experience with a limited set of exemplars. However, over-generalization, resulting in excessive and inappropriate avoidance, has been implicated in a variety of psychological disorders. Here, we use reinforcement learning modelling to dissect out different contributions to the generalization of instrumental avoidance in two groups of human volunteers (N=26, N=482). We found that generalization of avoidance could be parsed into perceptual and value-based processes, and further, that value-based generalization could be subdivided into that relating to aversive and neutral feedback — with corresponding circuits including primary sensory cortex, anterior insula, and ventromedial prefrontal cortex, respectively. Further, generalization from aversive, but not neutral, feedback was associated with self-reported anxiety and intrusive thoughts. These results reveal a set of distinct mechanisms that mediate generalization in avoidance learning, and show how specific individual differences within them can yield anxiety.
\r\n","sample_size":26,"scanner_type":"Siemens Magnetom Skyra Fit (3T)","acknowledgements":"This study was funded by the Wellcome Trust. The authors declare no relevant conflicts of interest.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Value generalization in human avoidance learning","url":"https://www.biorxiv.org/content/early/2017/11/21/223149"}],"task_set":[{"cogat_id":"tsk_BuPIiFcjBo2aX","number":1,"name":"Generalization of Instrumental Avoidance Task","url":"http://www.cognitiveatlas.org/id/tsk_BuPIiFcjBo2aX"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-12-11"}],"investigator_set":[{"investigator":"Ben Seymour"},{"investigator":"Agnes Norbury"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000249/ds000249_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC T1w group report","url":"https://s3.amazonaws.com/openneuro/ds000249/ds000249_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for all subjects","url":"https://s3.amazonaws.com/openneuro/ds000249/ds000249_R1.0.0/compressed/ds000249_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"aen31@cam.ac.uk","name":"Agnes Norbury","website":""}]},{"accession_number":"ds000115","project_name":"Working memory in healthy and schizophrenic individuals","summary":"Individuals diagnosed with schizophrenia, their unaffected siblings, and healthy controls performed three levels of an n-back task (0, 1, and 2-back).
\r\n\r\nWarning: The events files in this dataset do not seem to be correct. We cannot guarantee their accuracy and thus recommend that you disregard them entirely.
\r\n","sample_size":99,"scanner_type":"3T Siemens Trio","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Brain Network Connectivity in Individuals with Schizophrenia and Their Siblings","url":"http://www.ncbi.nlm.nih.gov/pubmed/21193174"},{"title":"Working Memory Related Brain Network Connectivity in Individuals with Schizophrenia and Their Siblings","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3358772/"}],"task_set":[{"cogat_id":"tsk_4a57abb949b1c","number":1,"name":"letter n-back task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949b1c"},{"cogat_id":"tsk_4a57abb949b1c","number":2,"name":"letter n-back task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949b1c"},{"cogat_id":"tsk_4a57abb949b1c","number":3,"name":"letter n-back 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Subjects 001-005 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds115_sub001-005_nodti.tgz","revision":"1.0.2"},{"title":"Raw Data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000115_R2.0.0_raw.zip","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000219","project_name":"Flavour Pleasantness (Regular Products)","summary":"Participants were randomly assigned to two experimental groups. The first group (n=23, mean age = 23.43, SD=2.33, range: 21–28) was recruited to taste 8 commercially available drinks (referred to as regular products) during a morning session. The second group (n=22, mean age = 24.67, SD=3.37, range: 21–33) was recruited to taste 6 ONS (oral nutritional supplements) products in the afternoon. These two groups were independent (i.e. no participant participated in both groups).
\r\nParticipants engaged in a tasting task containing 48 or 36 trials for the regular products and ONS group, respectively. During the course of the experiment, participants received visual cues and instructions in Dutch via a paradigm constructed in E-prime (Psychology Software Tools Inc., Pittsburgh). Every flavor stimulus was delivered 6 times balanced over all imaging runs and counterbalanced between participants. The paradigm was presented during four and three imaging runs, for the regular products and ONS group, respectively. Each imaging run lasted for approximately 15 minutes (depending on reaction times) and contained a series of 12 trials. During each trial, participants were warned for an upcoming taste delivery by an asterisk appearing centered on the screen (duration: 2s.). Subsequently, 2 ml of a taste stimulus was delivered in the mouth and participants were instructed to taste this stimulus with the cue "Taste" (in Dutch: "Proeven", duration: 3.5s.). After tasting, participants were instructed to swallow the solution, cued as "Swallow" (in Dutch: "Slikken", duration: 4s.), followed by a period in which they needed to passively "Judge" the taste (in Dutch: "Beoordelen", duration: 22.5s.). We chose this long period to assure that BOLD responses associated with rating and tasting had minimal overlap. Finally, a 7-point Likert scale appeared on the screen, ranging from "very unpleasant" to "very pleasant". Participants were instructed to express perceived pleasantness of the taste on the scale by using a button box held in their right hand. Every trial ended with a rinsing procedure, in which participants received a 2 ml bolus of a 5% artificial saliva solution (Saliva Orthana, TM) twice. The entire paradigm lasted for approximately 90 minutes, in which either 288 ml or 216 ml of liquid was consumed, for the regular products and ONS group, respectively. As baseline, we included four 15-second periods in each imaging run within both data sets, during which the participant was looking at a black screen with a red cross centered in the middle.
\r\nMRI scans were performed using a 3-Tesla MR scanner (Philips Intera, Best, the Netherlands) equipped with a 32-channel head coil. A T1-weighted 3D fast field echo (FFE) whole brain image was obtained in transverse orientation for anatomical reference. Acquisition parameters: field of view (FOV) 256 × 232 × 170 mm3 (rl, ap, fh); voxel size 1 mm isotropic; TR = 9 ms; TE = 3.5 ms; flip angle 8º; SENSE factors: 2.5, 1 (ap, fh); 170 slices, scan duration = 246.3s. Functional brain images were acquired in sagittal orientation using the Principles of Echo-Shifting with a Train of Observations (PRESTO) sequence. Acquisition parameters: FOV 153 × 230 × 230 mm3 (rl, ap, fh); voxel size 2.87 × 2.87 × 3 mm3; TR = 20 ms; TE = 30 ms; flip angle 7º; SENSE factors: 1.9, 1.9 (rl, ap); scan time per volume 1.532s. As the experiment was self-paced, the number of volumes per imaging run ranged between 580 and 600.
\r\nDue to technical difficulties with the gustometer or scanner, data was missing for several trials. We removed participants missing more than 25% of their data (3 ONS group, 1 regular drinks group). Furthermore, 1 participant in the regular drinks group was removed due to a brain abnormality. Therefore, fMRI analysis was performed on data from 19 and 21 participants for the ONS and regular drinks groups, respectively.
\r\nWe experienced technical difficulties with the PRESTO sequence. As a result, several PRESTO images were missing at the start of imaging runs for 10 participants (on average 0.054% per data set).
NOTE: This study has two datasets asscoiated with it. One for ONS products(ds000218) and other for RP products (ds000219)
\r\n\r\nThe paradigm is similar to http://www.sciencedirect.com/science/article/pii/S1053811915005674 but with some adjustments.
\r\n","sample_size":21,"scanner_type":"Philips Intera 3T","acknowledgements":"Funding source: TIFN SL001","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Flavor pleasantness processing in the ventral emotion network.","url":"https://www.ncbi.nlm.nih.gov/pubmed/?term=Flavor+pleasantness+processing+in+the+ventral+emotion+network"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-02-01"}],"investigator_set":[{"investigator":"Gert J. ter Horst"},{"investigator":"L. Nanetti"},{"investigator":"Remco J. Renken"},{"investigator":"Liselore Weitkamp"},{"investigator":"Jelle R. Dalenberg"}],"link_set":[{"title":"Raw data for Subjects 18-22","url":"https://s3.amazonaws.com/openneuro/ds000219/ds000219_R1.0.0/compressed/ds000219_R1.0.0_sub18-22.zip","revision":"1.0.0"},{"title":"Raw data for Subjects 12-17","url":"https://s3.amazonaws.com/openneuro/ds000219/ds000219_R1.0.0/compressed/ds000219_R1.0.0_sub12-17.zip","revision":"1.0.0"},{"title":"Raw data for Subjects 7-11","url":"https://s3.amazonaws.com/openneuro/ds000219/ds000219_R1.0.0/compressed/ds000219_R1.0.0_sub07-11.zip","revision":"1.0.0"},{"title":"Raw data for Subjects 1-6","url":"https://s3.amazonaws.com/openneuro/ds000219/ds000219_R1.0.0/compressed/ds000219_R1.0.0_sub01-06.zip","revision":"1.0.0"},{"title":"Metadata","url":"https://s3.amazonaws.com/openneuro/ds000219/ds000219_R1.0.0/compressed/ds000219_R1.0.0_metadata.zip","revision":"1.0.0"}],"contacts":[{"email":"j.r.dalenberg@gmail.com","name":"Jelle R. Dalenberg","website":""}]},{"accession_number":"ds000011","project_name":"Classification learning and tone-counting","summary":"Fourteen participants were trained on two different classification problems while they were scanned by using fMRI. Participants were trained on one problem under single-task (ST) conditions and on the other problem while performing a concurrent tone-counting task. During training, subjects learned the categories based on trial-by-trial feedback. After training, subjects received an additional block of probe trials using a mixed event-related (ER) fMRI paradigm, during which they classified items that had been trained under either ST or dual-task (DT) conditions. To measure how well participants had learned under each condition, no feedback was presented during the probe block, and all items were presented under ST conditions. An additional tone-counting localizer scan presented blocks of the tone counting task (followed by a probe at the end of each block) compared to rest.
\r\n","sample_size":14,"scanner_type":"3T Siemens Allegra head-only MR scanner","acknowledgements":"We thank Michael Mitchell and staff (Statistical Consulting Group, Academic Technology Services, University of California, Los Angeles) for help with regression analyses and Sabrina M. Tom for help with fMRI data acquisition. This work was supported by National Science Foun- dation Grant BCS-0223843, a Whitehall Foundation grant (to R.A.P.), and a National Science Foundation Graduate Fellowship (to K.F.).","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Modulation of competing memory systems by distraction","url":"http://www.ncbi.nlm.nih.gov/pubmed/16868087"}],"task_set":[{"cogat_id":"trm_4ebc6a6b75ebf","number":1,"name":"tone counting","url":"http://www.cognitiveatlas.org/id/trm_4ebc6a6b75ebf"},{"cogat_id":"trm_4ebc728326a13","number":2,"name":"single-task weather prediction","url":"http://www.cognitiveatlas.org/id/trm_4ebc728326a13"},{"cogat_id":"trm_4ebc98cc77e7b","number":3,"name":"dual-task weather prediction","url":"http://www.cognitiveatlas.org/id/trm_4ebc98cc77e7b"},{"cogat_id":"trm_4ebc9d2e397f2","number":4,"name":"classification probe without feedback","url":"http://www.cognitiveatlas.org/id/trm_4ebc9d2e397f2"}],"revision_set":[{"revision_number":"1.0.0","notes":"","date_set":"2011-10-06"},{"revision_number":"2.0.1","notes":"- Edited authors string in dataset_description.json","date_set":"2016-10-27"},{"revision_number":"2.0.0","notes":"- converted to BIDS standard ","date_set":"2016-09-30"}],"investigator_set":[{"investigator":"Poldrack, R.A."},{"investigator":"Knowlton, B.J."},{"investigator":"Foerde, K."}],"link_set":[{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000011/ds000011_R1.0.0/compressed/ds011_raw.tgz","revision":"1.0.0"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000011/ds000011_R2.0.1/compressed/ds000011_R2.0.1_raw.zip","revision":"2.0.1"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000011/ds000011_R2.0.0/compressed/ds000011_R2.0.0_raw.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000210","project_name":"Multi-echo fMRI replication sample of autobiographical memory, prospection and theory of mind reasoning tasks","summary":"We collected a replication sample of Spreng and Grady (J Cogn. Neurosci. 22, 1112-1123, 2010). Here we provide the resulting dataset of multi-echo fMRI data in 31 young adults during autobiographical remembering, imagining, and mentalizing; we also provide an additional resting-state scan for each subject.
\r\n","sample_size":31,"scanner_type":"GE Discovery MR750 3.0T","acknowledgements":"This project was supported in part by NIH grant 1S10RR025145. We thank Prantik Kundu for guidance on multi-echo image processing, as well as Amber Lockrow and Emily Qualls for assistance with data collection.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Multi-echo fMRI replication sample of autobiographical memory, prospection and theory of mind reasoning tasks","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5170594/"}],"task_set":[],"revision_set":[{"revision_number":"1.0.1","notes":" - Converted dataset to BIDS Spec 1.0.2\r\n","date_set":"2017-11-03"},{"revision_number":"1.0.0","notes":"-- Initial Release ","date_set":"2016-10-05"}],"investigator_set":[{"investigator":"R. Nathan Spreng"},{"investigator":"Wen-Ming Luh"},{"investigator":"Elizabeth DuPre"}],"link_set":[{"title":"Data for sub30-31","url":"https://s3.amazonaws.com/openneuro/ds000210/ds000210_R1.0.1/compressed/ds000210_R1.0.1_sub30-31.zip","revision":"1.0.1"},{"title":"Data for sub20-29","url":"https://s3.amazonaws.com/openneuro/ds000210/ds000210_R1.0.1/compressed/ds000210_R1.0.1_sub20-29.zip","revision":"1.0.1"},{"title":"Data for sub10-19","url":"https://s3.amazonaws.com/openneuro/ds000210/ds000210_R1.0.1/compressed/ds000210_R1.0.1_sub10-19.zip","revision":"1.0.1"},{"title":"Data for sub01-09","url":"https://s3.amazonaws.com/openneuro/ds000210/ds000210_R1.0.1/compressed/ds000210_R1.0.1_sub01-09.zip","revision":"1.0.1"},{"title":"Dataset Metadata on AWS","url":"https://s3.amazonaws.com/openneuro/ds000210/ds000210_R1.0.1/compressed/ds000210_R1.0.1_metadata.zip","revision":"1.0.1"},{"title":"Subjects 30-31","url":"https://s3.amazonaws.com/openfmri/tarballs/ds000210_R1.0.0_sub30-31.tgz","revision":"1.0.0"},{"title":"Subjetcs 20-29","url":"https://s3.amazonaws.com/openfmri/tarballs/ds000210_R1.0.0_sub20-29.tgz","revision":"1.0.0"},{"title":"Subjects 10-19","url":"https://s3.amazonaws.com/openfmri/tarballs/ds000210_R1.0.0_sub10-19.tgz","revision":"1.0.0"},{"title":"Subjects 01-09","url":"https://s3.amazonaws.com/openfmri/tarballs/ds000210_R1.0.0_sub01-09.tgz","revision":"1.0.0"},{"title":"Dataset Metadata on AWS ","url":"https://s3.amazonaws.com/openfmri/tarballs/ds000210_R1.0.0_metadata.tgz","revision":"1.0.0"}],"contacts":[{"email":"emd222@cornell.edu","name":"Elizabeth DuPre","website":""}]},{"accession_number":"ds000243","project_name":"Washington University 120","summary":"These 120 MRI datasets are being released to the public along as part of the materials for “Temporal interpolation alters motion in fMRI scans: magnitudes and consequences for artifact detection” by Power et al. in PLOS ONE.
\r\n\r\nIncluded for each subject is a T1-weighted anatomical image (MP-RAGE) and one or more T2*-weighted scans (resting state BOLD scans)
\r\n\r\nAll subjects
\r\n - were “typical” young adults that reported no significant neurological or psychiatric history
\r\n - were right-handed and reported that English was their first language
\r\n - were scanned at Washington University in Saint Louis on a Siemens MAGNETOM Tim Trio 3T scanner with a Siemens 12-channel head coil
\r\n - were scanned using interleaved ascending product sequences for T2* data
\r\n - were scanned in the eyes-open resting state fixating a white crosshair on a black background
The data have been described in multiple publications from the Petersen/Schlaggar group,
\r\n - beginning with Power et al., 2013 “Evidence for hubs in human brain networks” in Neuron
\r\n - and most comprehensively in Power et al., 2014 “Methods to detect, characterize, and remove motion artifact in resting state fMRI” in Neuroimage
\r\n - as well as several other publications
\r\n
\r\n
We investigated the use of accelerated 3D readouts to obtain whole-brain, high-SNR ASL perfusion maps and reduce SAR deposition. Parallel imaging was implemented along the partition-encoding direction in a pseudo-continuous ASL sequence with background-suppression and 3D RARE Stack-Of-Spirals readout, and its performance was evaluated in three small cohorts. First, both non-accelerated and two-fold accelerated single-shot versions of the sequence were evaluated in healthy volunteers during a motor-photic task, and the performance was compared in terms of temporal SNR, GM-WM contrast, and statistical significance of the detected activation. Secondly, single-shot 1D-accelerated imaging was compared to a two-shot accelerated version to assess benefits of SNR and spatial resolution for applications in which temporal resolution is not paramount. Third, the efficacy of this approach in clinical populations was assessed by applying the single-shot 1D-accelerated version to a larger cohort of elderly volunteers.
\r\n","sample_size":18,"scanner_type":"3T Siemens Prisma","acknowledgements":"This work was supported by the National Institutes of Health (http://www.nih.gov/), grants no. P41EB015893 and MH080729, by National Natural Science Foundation of China (http://www.nsfc.gov.cn/), grant no. 81471644, and Hangzhou Innovation Seed Fund.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4c8a834779883","number":1,"name":"rest eyes open","url":"http://www.cognitiveatlas.org/id/trm_4c8a834779883"}],"revision_set":[{"revision_number":"1.0.0","notes":"-- Initial Release","date_set":"2017-07-10"}],"investigator_set":[{"investigator":"John A. Detre"},{"investigator":"María A. Fernández-Seara"},{"investigator":"Yulin V. Chang"},{"investigator":"Ze Wang"},{"investigator":"Marta Vidorreta"}],"link_set":[{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000236/ds000236_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for all Subjects ","url":"https://s3.amazonaws.com/openneuro/ds000236/ds000236_R1.0.0/compressed/ds000236_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"","name":"Marta Vidorreta","website":""}]},{"accession_number":"ds000200","project_name":"Pre-adolescents Exposure to Manganese","summary":"The data included in this pilot study are a collection of 14 pre-adolescents recruited in one of the Italian sites exposed to Manganese. The mean age of the two groups was respectively 14.7 years (s.d.=2.4) and 14.6 years (s.d.= 0.5) with 4 girls in the first group and 2 in the second.
\r\n\r\nThe fMRI paradigm included an olfactory stimulation which consisted of 12 alternating task-rest blocks of 25 seconds, 10 volumes/block (120 volumes) for a total acquisition time of 5 minutes. The MRI acquisition protocol included a Coronal T2 FSE (TR/TE 5000/102 slice thickness 2 mm, no gap) use for the volumetric measurement of the olfactory bulb; a time series of 2D echo planar imaging (EPI) (TR/TE 2500/50 ms, 28 axial slices, 3.3 mm thickness, 1.1 mm inter-slice gap, matrix size 64x64).
\r\n\r\nNote: Please see https://openfmri.org/dataset/ds000220/ if you are looking for data associated with the following paper: Roy A, Bernier RA, Wang J, Benson M, French JJ Jr, et al. (2017) The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury. PLOS ONE 12(4): e0170541. https://doi.org/10.1371/journal.pone.0170541
\r\n","sample_size":14,"scanner_type":"1.5T Aera, Siemens, Erlangen, Germany","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Effects of Manganese Exposure on Olfactory Functions in Teenagers: A Pilot Study","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4713423/"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2016-01-05"}],"investigator_set":[{"investigator":"Emilia Iannilli"}],"link_set":[{"title":"Download Dataset - ds200_R1.0.0","url":"http://openfmri.s3.amazonaws.com/tarballs/ds200_R1.0.0.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000117","project_name":"A multi-subject, multi-modal human neuroimaging dataset","summary":"This dataset contains data acquired with multiple functional and structural neuroimaging modalities on the same nineteen healthy volunteers. The functional data include Electroencephalography (EEG), Magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) data, recorded while the volunteers performed multiple runs of hundreds of trials of a simple perceptual task on pictures of familiar, unfamiliar and scrambled faces during two visits to the laboratory. The structural data include T1-weighted MPRAGE, Multi-Echo FLASH and Diffusion-weighted MR sequences. Though only from a small sample of volunteers, these data can be used to develop methods for integrating multiple modalities from multiple runs on multiple participants, with the aim of increasing the spatial and temporal resolution above that of any one modality alone. They can also be used to integrate measures of functional and structural connectivity, and as a benchmark dataset to compare results across the many neuroimaging analysis packages.
\r\n","sample_size":19,"scanner_type":"Siemens 3T TIM TRIO","acknowledgements":"","license_title":"Creative Commons Attribution 4.0 International Public License","license_url":"https://www.creativecommons.org/licenses/by/4.0/legalcode","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"A multi-subject, multi-modal human neuroimaging dataset","url":"http://www.ncbi.nlm.nih.gov/pubmed/25977808"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":" - Converted to BIDS format\r\n - Changed subject order (see README)","date_set":"2017-11-11"},{"revision_number":"0.1.1","notes":" - Replaced ds117_R0.1.0/sub004/MEG/run_02_sss.fif and ds117_R0.1.0/sub006/MEG/run_03_sss.fif since the original versions were corrupted","date_set":"2016-09-17"},{"revision_number":"0.1.0","notes":"- Initial release","date_set":"2014-11-03"}],"investigator_set":[{"investigator":"Henson, RN"},{"investigator":"Wakeman, DG"}],"link_set":[{"title":"MRIQC bold group report","url":"https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC T1w group report","url":"https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for subjects 01-04 (16 GB)","url":"https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/compressed/ds000117_R1.0.0_sub01-04.zip","revision":"1.0.0"},{"title":"Data for subjects 13-16 (15.2 GB)","url":"https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/compressed/ds000117_R1.0.0_sub13-16.zip","revision":"1.0.0"},{"title":"Data for subjects 09-12 (15.3 GB)","url":"https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/compressed/ds000117_R1.0.0_sub09-12.zip","revision":"1.0.0"},{"title":"Data for subjects 05-08 (15.1 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While the reliability of anatomical images has been established by a number of groups, the reliability of DTI data has not been studied as extensively. In this study, five healthy controls were recruited and imaged at eight imaging centers. Repeated measures were obtained across two imaging protocols allowing intra-subject and inter-site variability to be assessed. Regional measures within white matter were obtained for standard rotationally invariant measures: fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity. Intra-subject coefficient of variation (CV) was typically <1% for all scalars and regions. Inter-site CV increased to *1%–3%. Inter-vendor variation was similar to inter-site variability. This variability includes differences in the actual implementation of the sequence.
\r\n","sample_size":5,"scanner_type":"Siemens 3T TIM Trio, Philips 3T Achieva","acknowledgements":"This work was supported, in part, by awards from CHDI Foundation, Inc.; NIH R01NS050568; NINDS NS40068 Neurobiological Predictors of Huntington’s Disease; and NINDS R01 NS054893 Cognitive and Functional Brain Changes in Preclinical HD. Dr. Turner was supported by NCRR 1 U24 RR021992 from the Functional Imaging Biomedical Informatics Network (S.G. Potkin, PI) for her work on this project while at the University of California, Irvine. This publication was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1RR024979. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Multicenter reliability of diffusion tensor imaging.","url":"http://www.ncbi.nlm.nih.gov/pubmed/23075313"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial Release","date_set":"2016-09-01"}],"investigator_set":[{"investigator":"Laura A. Flashma"},{"investigator":"Elizabeth Aylward"},{"investigator":"Mark J. Lowe"},{"investigator":"Sarah Reading"},{"investigator":"Jessica A. Turner"},{"investigator":"Karl G. Helmer"},{"investigator":"Susumu Mori"},{"investigator":"Kelvin Lim"},{"investigator":"Bryon A. Mueller"},{"investigator":" Bradley D. Bolster Jr."},{"investigator":"Jeffrey D. Long"},{"investigator":"Hans J. Johnson"},{"investigator":"Dawei Liu"},{"investigator":"Joy T. Matsui"},{"investigator":"Vincent A. Magnotta"}],"link_set":[{"title":"Sourcedata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds206_R1.0.0_sourcedata.tgz","revision":"1.0.0"},{"title":"Data for All Subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds206_R1.0.0_data_all_subjects.tgz","revision":"1.0.0"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds206_R1.0.0_metadata.tgz","revision":"1.0.0"}],"contacts":[{"email":"hans-johnson@uiowa.edu","name":"Hans J. Johnson","website":""}]},{"accession_number":"ds000031","project_name":"MyConnectome","summary":"This dataset contains the MRI data from the MyConnectome study. The data are broken into several parts:
\r\n\r\nSessions 14-104 are from the original acquisition period of the study performed at the University of Texas using a Siemens Skyra 3T scanner. All resting data were collected with eyes closed.
\r\n\r\nSession 105 is a follow-up session performed at Washington University using a Siemens Trio 3T scanner in which data were collected manipulating eyes open/closed across sessions.
\r\n\r\nSession 106 is a follow-up session performed at Stanford University using a GE MR750 3T scanner, in which a high angular resolution diffusion imaging acquisition was performed.
\r\n\r\nThis data was collected to investigate experimental design optimization for pattern-information approaches to fMRI data analysis. Participants were scanned while encoding images of animals and tools. There were 5 different stimulus presentation designs, and each participant completed to runs under each design. The designs varied in the number of trials and trial timing within fixed duration scans. Trial timing conditions with fixed onset-to-onset timing ranged from slow 12-s trials with two repetitions of each item to quick 6-s trials with four repetitions per item. We also tested a jittered version of the quick design with 4–8 s trials. After the scans, participants completed a memory test.
\r\n","sample_size":35,"scanner_type":"Siemens MAGNETOM Skyra","acknowledgements":"This work was made possible through generous support from the Lewis Family Endowment to the UO which supports the Robert and Beverly Lewis Center for Neuroimaging.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Trial timing and pattern-information analyses of fMRI data.","url":"https://www.ncbi.nlm.nih.gov/pubmed/28411155"}],"task_set":[],"revision_set":[{"revision_number":"1.0.1","notes":"- Corrected events files","date_set":"2018-02-01"},{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-10-05"}],"investigator_set":[{"investigator":"Adke A"},{"investigator":"De Araujo Sanchez M A"},{"investigator":"Zeithamova D"}],"link_set":[{"title":"MRIQC functional group report (580.9 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\r\n","sample_size":320,"scanner_type":"Siemens Verio 3T","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4c8a834779883","number":1,"name":"rest eyes open","url":"http://www.cognitiveatlas.org/id/trm_4c8a834779883"}],"revision_set":[{"revision_number":"1.0.0","notes":"-- Initial Release","date_set":"2017-05-17"}],"investigator_set":[{"investigator":"Daniel S. Margulies"},{"investigator":"Jonathan Smallwood"},{"investigator":"Jared Pool"},{"investigator":"Anastasia Osoianu"},{"investigator":"Katharina Ohrnberger"},{"investigator":"Yelyzaveta Kramarenko"},{"investigator":"Rebecca Jost"},{"investigator":"Philipp Haueis "},{"investigator":"Krzysztof J. 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GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010100-010109.zip","revision":"1.0.0"},{"title":"Data for Subjects 010090-010099 (14.7 GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010090-010099.zip","revision":"1.0.0"},{"title":"Data for Subjects 010080-010089 (13.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010080-010089.zip","revision":"1.0.0"},{"title":"Data for Subjects 010070-010079 (17.3 GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010070-010079.zip","revision":"1.0.0"},{"title":"Data for Subjects 010060-010069 (14.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010060-010069.zip","revision":"1.0.0"},{"title":"Data for Subjects 010050-010059 (15.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010050-010059.zip","revision":"1.0.0"},{"title":"Data for Subjects 010040-010049 (16.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010040-010049.zip","revision":"1.0.0"},{"title":"Data for Subjects 010030-010039 (15.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010030-010039.zip","revision":"1.0.0"},{"title":"Data for Subjects 010020-010029 (14.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010020-010029.zip","revision":"1.0.0"},{"title":"Data for Subjects 010010-010019 (11.2 GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010010-010019.zip","revision":"1.0.0"},{"title":"Data for Subjects 010001-010009 (13.7GB)","url":"https://s3.amazonaws.com/openneuro/ds000221/ds000221_R1.0.0/compressed/ds000221_R1.0.0_sub010001-010009.zip","revision":"1.0.0"}],"contacts":[{"email":"mfalkiewicz@cbs.mpg.de","name":"Marcel Falkiewicz","website":""},{"email":"margulies@cbs.mpg.de","name":"Daniel Margulies","website":""}]},{"accession_number":"ds000247","project_name":"MEG-BIDS OMEGA sample","summary":"This dataset is a subset from the OMEGA (Open MEG Archives) free and open data repository for MEG data (Niso et al. 2015). The present sample has been organized according to the specifications of MEG-BIDS to obtain feedback from the user community.
\r\n\r\nNiso G., Rogers C., Moreau J.T., Chen L.Y., Madjar C., Das S., Bock E., Tadel F., Evans A.C., Jolicoeur P., Baillet S. (2015). OMEGA: The Open MEG Archive. NeuroImage 124, 1182-1187. doi:10.1016/j.neuroimage.2015.04.028
\r\n","sample_size":5,"scanner_type":"MEG, CTF Inc. 275 channels","acknowledgements":"Quebec Bioimaging Network Strategic Initiative (QBIN 5886)","license_title":"CC0","license_url":"https://creativecommons.org/publicdomain/zero/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"OMEGA: The Open MEG Archive.","url":"https://www.ncbi.nlm.nih.gov/pubmed/25896932"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-11-12"}],"investigator_set":[{"investigator":"Sylvain Baillet"},{"investigator":"François Tadel"},{"investigator":"Jeremy Moreau"},{"investigator":"Elizabeth Bock"},{"investigator":"Guiomar Niso"}],"link_set":[{"title":"Full dataset (5.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000247/ds000247_R1.0.0/compressed/ds000247_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"sylvain.baillet@mcgill.ca","name":"Sylvain Baillet","website":""}]},{"accession_number":"ds000248","project_name":"MNE Sample Data","summary":"
\r\nThis dataset has been acquired for educational purposes. It contains simultaneous MEG/EEG data. For many years it has been used to train MNE users and to serve as illustration in the tutorials of the MNE software. The present dataset has been organized according to the specifications of MEG-BIDS to facilitate future data analysis.
Thirty female subjects performed a passive viewing task with blocks of food and nonfood images. More procedures can be found in the publication" Allured or alarmed: counteractive control responses to food temptations in the brain.
\r\n\r\n"During scanning, subjects alternately viewed 24 s blocks of palatable food images (8 blocks) and non-food images (i.e., office utensils; 8 blocks), interspersed with 8–16 s rest blocks showing a crosshair (12 s on average). Halfway the task there was a 10 s break. In the image blocks, 8 images were presented for 2.5 s each with a 0.5 s inter-stimulus interval. All pictures were of equal size and displayed the (food) object on a white background. Food pictures were selected to represent foods that are both attractive and ‘forbidden’ (i.e., fattening), congruent with our definition of temptations."
\r\n\r\nDataset Contains: BOLD-contrast fMRI data and T1-weighted high resolution structural scans
\r\n","sample_size":30,"scanner_type":"Philips Achieva 3 Tesla","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Allured or alarmed: Counteractive control responses to food temptations in the brain","url":"http://www.ncbi.nlm.nih.gov/pubmed/23578759"}],"task_set":[{"cogat_id":"trm_4c899211a965c","number":1,"name":"","url":"http://www.cognitiveatlas.org/id/trm_4c899211a965c"}],"revision_set":[{"revision_number":"1.0.3","notes":"- Updated participants.tsv column header","date_set":"2018-02-07"},{"revision_number":"1.0.2","notes":"Updated particpants.tsv and particpants.json to include menstrual data","date_set":"2016-07-06"},{"revision_number":"1.0.1","notes":"Updated participants.tsv to include BMI and diet score data and added participants.json","date_set":"2016-04-28"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2016-04-12"}],"investigator_set":[{"investigator":"D. T. D. de Ridder"},{"investigator":"Catherine Evers"},{"investigator":"Floor M. Kroese"},{"investigator":"Paul A. M. Smeets"}],"link_set":[{"title":"Data for all subjects (1.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000157/ds000157_R1.0.3/compressed/ds000157_R1.0.3.zip","revision":"1.0.3"},{"title":"Imaging data for all subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds157_R1.0.1_01-30.tgz","revision":"1.0.2"},{"title":"Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds157_R1.0.2_metadata_derivatives.tgz","revision":"1.0.2"},{"title":"Imaging data for all subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds157_R1.0.1_01-30.tgz","revision":"1.0.1"},{"title":"Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds157_R1.0.1_metadata_derivatives.tgz","revision":"1.0.1"},{"title":"Imaging data for all subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds157_R1.0.0_01-30.tgz","revision":"1.0.0"},{"title":"Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds157_R1.0.0_metadata_derivatives.tgz","revision":"1.0.0"}],"contacts":[{"email":"paul75smeets@gmail.com","name":"Paul Smeets","website":""}]},{"accession_number":"ds000220","project_name":"Cost Analysis TBI","summary":"Resting state connectivity and graph theory at 3 time points during recovery after traumatic brain injury
\r\n","sample_size":14,"scanner_type":"3T Siemens, 3T Philips","acknowledgements":"Acknowledgements: Social Science Research Institute; Penn State University Park\r\n\r\n|| Funding: National Center for Advancing Translational Sciences, NIHUL Tr000127","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury.","url":"https://www.ncbi.nlm.nih.gov/pubmed/28422992"}],"task_set":[{"cogat_id":"trm_4c8a834779883","number":1,"name":"rest eyes open","url":"http://www.cognitiveatlas.org/id/trm_4c8a834779883"}],"revision_set":[{"revision_number":"1.0.0","notes":" - Initial release","date_set":"2017-08-29"}],"investigator_set":[{"investigator":"Frank Hillary"},{"investigator":"Arnab Roy"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000220/ds000220_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000220/ds000220_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Metadata (2 KB)","url":"https://s3.amazonaws.com/openneuro/ds000220/ds000220_R1.0.0/compressed/ds000220_R1.0.0_metadata.zip","revision":"1.0.0"},{"title":"MRIQC results (971 MB)","url":"https://s3.amazonaws.com/openneuro/ds000220/ds000220_R1.0.0/compressed/ds000220_R1.0.0_mriqc.zip","revision":"1.0.0"},{"title":"Data for all subjects (3.1 GB)","url":"https://s3.amazonaws.com/openneuro/ds000220/ds000220_R1.0.0/compressed/ds000220_R1.0.0_data.zip","revision":"1.0.0"}],"contacts":[{"email":"fhillary@psu.edu","name":"Frank Hillary","website":""}]},{"accession_number":"ds000030","project_name":"UCLA Consortium for Neuropsychiatric Phenomics LA5c Study","summary":"The Consortium for Neuropsychiatric Phenomics (CNP) is a large study funded by the NIH Roadmap Initiative that aims to facilitate discovery of the genetic and environmental bases of variation in psychological and neural system phenotypes, to elucidate the mechanisms that link the human genome to complex psychological syndromes, and to foster breakthroughs in the development of novel treatments for neuropsychiatric disorders.
\r\n\r\nThe study includes imaging of a large group of healthy individuals from the community (138 subjects), as well as samples of individuals diagnosed with schizoprenia (58), bipolar disorder (49), and ADHD (45).
\r\n\r\nThe participants, ages 21-50, were recruited by community advertisements from the Los Angeles area and completed extensive neuropsychogical testing, in addition to fMRI scanning. To be included individuals had to be either "White, Not of Hispanic or Latino Origin" or "Hispanic or Latino, of Any Race" following NIH designations of racial and ethnic minority groups, and have completed at least 8 years of education (other racial and ethnic minority groups were excluded because this was thought to increase risk of confounding planned genetic studies). For participants who spoke both English and Spanish, language for testing was determined by a verbal fluency test. Participants were screened for neurological disease, history of head injury with loss of consciousness or cognitive sequelae, use of psychoactive medications, substance dependence within past 6 months, history of major mental illness or ADHD, and current mood or anxiety disorder. Self-reported history of psychopathology was verified with the SCID-IV (First, Spitzer, Gibbon, & Williams, 1995). Urinalysis was used to screen for drugs of abuse (cannabis, amphetamine, opioids, cocaine, benzodiazepines) on the day of testing and excluded if results were positive.
\r\n\r\nA portion of this large sample took part in two separate fMRI sessions, which each included one-hour of behavioral testing and a one-hour scan on the same day. Participants were recruited from the parent study to participate in the fMRI portion if they successfully completed all previous testing sessions, and did not meet the following additional exclusion criteria: history of significant medical illness, contraindications for MRI (including pregnancy), any mood-altering medication on scan day (based on self-report), vision that was insufficient to see task stimuli, and left-handedness.
\r\n\r\nAfter receiving a thorough explanation, all participants gave written informed consent according to the procedures approved by the University of California Los Angeles Institutional Review Board.
\r\n\r\n\r\nModalities Include:
\r\n\r\nT1-weighted Anatomical MPRAGE
\r\n\r\n64 Direction DWI
\r\n\r\nBOLD contrast fMRI
\r\n\r\nResting State (with physiological monitoring)
\r\n\r\nBreath Hold fMRI (with physiological monitoring)
\r\n\r\nBalloon Analog Risk Task (BART) fMRI
\r\n\r\nStopsignal Task fMRI
\r\n\r\nTaskswitching fMRI
\r\n\r\nSpatial Working Memory Capacity Tasks (SCAP) fMRI
\r\n\r\nPaired Associates Memory Task - Encoding/Retrieval (PAMenc/PAMret)
\r\n\r\nNote: Some of the T1-weighted images included within this dataset (around 20%) show an aliasing artifact potentially generated by a headset. The artifact renders as a ghost that may overlap the cortex through one or both temporal lobes. A list of participants showing the artifact has will be added to the dataset in upcoming revision 1.0.5.
\r\n","sample_size":273,"scanner_type":"Siemens Trio (2 Imaging Sites)","acknowledgements":"This work was supported by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410).","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Preprocessed Consortium for Neuropsychiatric Phenomics dataset","url":"https://f1000research.com/articles/6-1262/v2"},{"title":"A phenome-wide examination of neural and cognitive function","url":"https://www.nature.com/articles/sdata2016110"}],"task_set":[],"revision_set":[{"revision_number":"1.0.5","notes":"- Added a note to Readme about artifact of T1w images\r\n- Added a column to participants.stv with ghost/no-ghost info\r\n- Removed index column added by mistake to phenotypes/*.tsv files ","date_set":"2017-12-05"},{"revision_number":"1.0.4","notes":"- Added preprocessed data to /derivatives","date_set":"2017-06-26"},{"revision_number":"1.0.3","notes":"- Removed few columns from tsv files in phenotype data to protect subject identification","date_set":"2017-01-26"},{"revision_number":"1.0.2","notes":"- Added mriqc results","date_set":"2016-10-05"},{"revision_number":"1.0.1","notes":"Additional phenotype information\r\nRemoved 17 subjects inadvertently included in previous release","date_set":"2016-09-26"},{"revision_number":"1.0.0","notes":"Initial raw data publish","date_set":"2016-01-29"}],"investigator_set":[{"investigator":"Sabb, F"},{"investigator":"Karlsgodt, K"},{"investigator":"Congdon, E"},{"investigator":"Freimer, N"},{"investigator":"London, E"},{"investigator":"Cannon, T"},{"investigator":"Poldrack, R"},{"investigator":"Bilder, R"}],"link_set":[{"title":"Subjects 50060-50085","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub50060-50085.zip","revision":"1.0.5"},{"title":"Subjects 50043-50059","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub50043-50059.zip","revision":"1.0.5"},{"title":"Subjects 50010-50038","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub50010-50038.zip","revision":"1.0.5"},{"title":"Subjects 50004-50008","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub50004-50008.zip","revision":"1.0.5"},{"title":"Subjects 11104-11156","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub11104-11156.zip","revision":"1.0.5"},{"title":"Subjects 11019-11098","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub11019-11098.zip","revision":"1.0.5"},{"title":"Subjects 10912-10998","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub10912-10998.zip","revision":"1.0.5"},{"title":"Subjects 10704-10893","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub10704-10893.zip","revision":"1.0.5"},{"title":"Subjects 10624-10697","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub10624-10697.zip","revision":"1.0.5"},{"title":"Subjects 10428-10575","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub10428-10575.zip","revision":"1.0.5"},{"title":"Subjects 10304-10388","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub10304-10388.zip","revision":"1.0.5"},{"title":"Subjects 10159-10299","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_sub10159-10299.zip","revision":"1.0.5"},{"title":"Task pipelines (group) - 2","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_taskgroup2.zip","revision":"1.0.5"},{"title":"Task pipelines (group) - 1 ","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_taskgroup1.zip","revision":"1.0.5"},{"title":"Task pipelines for subjects 70051-70086","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_task_sub70051-70086.zip","revision":"1.0.5"},{"title":"Task pipelines for subjects 70010-70049","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_task_sub70010-70049.zip","revision":"1.0.5"},{"title":"Task pipelines for subjects 70001-70008","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_task_sub70001-70008.zip","revision":"1.0.5"},{"title":"Task pipelines for subjects 60070-60089","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_task_sub60070-60089.zip","revision":"1.0.5"},{"title":"Task pipelines for subjects 60042-60068","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_task_sub60042-60068.zip","revision":"1.0.5"},{"title":"Task pipelines for subjects 60010-60038","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_task_sub60010-60038.zip","revision":"1.0.5"},{"title":"Task pipelines for subjects 60001-60008","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_task_sub60001-60008.zip","revision":"1.0.5"},{"title":"Task pipelines for subjects 50060-50085","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.5/compressed/ds000030_R1.0.5_derivatives_task_sub50060-50085.zip","revision":"1.0.5"},{"title":"Task pipelines for 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10159-10299","url":"https://s3.amazonaws.com/openneuro/ds000030/ds000030_R1.0.2/compressed/ds000030_R1.0.2_sub10159-10299.tgz","revision":"1.0.2"}],"contacts":[]},{"accession_number":"ds000253","project_name":"Female action video game players.","summary":"Functional MRI was used to examine brain activity in experienced, female action video game players and in females who do not play action video games. Participants performed two visually-guided reaching conditions: 1) Standard condition in which the eyes and hand moved to the same cued spatial location, 2) Non-Standard/Dissociated condition in which the eyes moved to the cued spatial location but the hand moved 180 degrees in the opposite direction. A slow event-related protocol was used with each trial containing a cue period, an instructed-delay period, and a movement period. Four imaging runs were collected containing both conditions (in random order). An independent localizer run was also collected using a similar trial structure.
\r\n","sample_size":20,"scanner_type":"Siemens TrioTim","acknowledgements":"Thank you to Joy Williams for excellent MR image acquisition.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"-- Initial Release","date_set":"2017-12-28"}],"investigator_set":[{"investigator":"Lauren Sergio"},{"investigator":"Diana Gorbet"}],"link_set":[{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000253/ds000253_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000253/ds000253_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"Data for Subjects 10-20","url":"https://s3.amazonaws.com/openneuro/ds000253/ds000253_R1.0.0/compressed/ds000253_R1.0.0_sub10-20.zip","revision":"1.0.0"},{"title":"Data for Subjects 01-09","url":"https://s3.amazonaws.com/openneuro/ds000253/ds000253_R1.0.0/compressed/ds000253_R1.0.0_sub01-09.zip","revision":"1.0.0"},{"title":"Metadata ","url":"https://s3.amazonaws.com/openneuro/ds000253/ds000253_R1.0.0/compressed/ds000253_R1.0.0_mriqc_metadata.zip","revision":"1.0.0"}],"contacts":[{"email":"gorbetd@yorku.ca","name":"Diana Gorbet","website":""}]},{"accession_number":"ds000113b","project_name":"High-resolution 7-Tesla fMRI data on the perception of musical genres","summary":"This is an extension to the studyforrest dataset (http://studyforrest.org, see also ds113). This release adds more high-resolution, ultra high-field (7 Tesla) fMRI data from the same individuals. The twenty participants were repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm. The data release includes raw fMRI data, as well as pre-computed structural alignments for within-subject and group analysis. In addition to fMRI, simultaneously recorded cardiac and respiratory traces, as well the complete implementation of the stimulation paradigm, including stimuli, and extracted auditory features are provided.
\r\n","sample_size":20,"scanner_type":"7 Tesla Siemens MAGNETOM","acknowledgements":"This research was supported by the German Federal Ministry of Education\r\nand Research (BMBF) as part of a US-German collaboration in\r\ncomputational neuroscience (CRCNS; awarded to James Haxby, Peter\r\nRamadge, and Michael Hanke), co-funded by the BMBF and the US National\r\nScience Foundation (BMBF 01GQ1112; NSF 1129855). Michael Hanke was\r\nsupported by funds from the German federal state of Saxony-Anhalt,\r\nProject: Center for Behavioral Brain Sciences.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":false,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[],"revision_set":[{"revision_number":"2.0.1","notes":"- Added authors string to dataset_description.json","date_set":"2016-11-09"},{"revision_number":"2.0.0","notes":"- Converted to BIDS standard ","date_set":"2016-10-03"}],"investigator_set":[{"investigator":"Jörg Stadler"},{"investigator":"Falko R. Kaule"},{"investigator":"Michael Casey"},{"investigator":"J. Swaroop Guntupalli"},{"investigator":"Christian Häusler"},{"investigator":"Richard Dinga"},{"investigator":"Michael Hanke"}],"link_set":[{"title":"Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113b_R2.0.1_metadata.zip","revision":"2.0.1"},{"title":"Raw Data for Sub13-16","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113b_R2.0.1_sub13-16.zip","revision":"2.0.1"},{"title":"Raw Data for Sub10-12","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113b_R2.0.1_sub10-12.zip","revision":"2.0.1"},{"title":"Raw Data for Sub07-09","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113b_R2.0.1_sub07-09.zip","revision":"2.0.1"},{"title":"Raw Data for Sub04-06","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113b_R2.0.1_sub04-06.zip","revision":"2.0.1"},{"title":"Raw Data for Sub01-03","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113b_R2.0.1_sub01-03.zip","revision":"2.0.1"},{"title":"Data for Sub17-20","url":"http://openfmri.s3.amazonaws.com/tarballs/ds00113b_R2.0.0_sub17-20.tgz","revision":"2.0.0"},{"title":"Data for Sub13-16","url":"http://openfmri.s3.amazonaws.com/tarballs/ds00113b_R2.0.0_sub13-16.tgz","revision":"2.0.0"},{"title":"Data for Sub10-12","url":"http://openfmri.s3.amazonaws.com/tarballs/ds00113b_R2.0.0_sub10-12.tgz","revision":"2.0.0"},{"title":"Data for Sub07-09","url":"http://openfmri.s3.amazonaws.com/tarballs/ds00113b_R2.0.0_sub07-09.tgz","revision":"2.0.0"},{"title":"Data for Sub04-06","url":"http://openfmri.s3.amazonaws.com/tarballs/ds00113b_R2.0.0_sub04-06.tgz","revision":"2.0.0"},{"title":"Data for Sub01-03","url":"http://openfmri.s3.amazonaws.com/tarballs/ds00113b_R2.0.0_sub01-03.tgz","revision":"2.0.0"},{"title":"Metdata for dataset","url":"http://openfmri.s3.amazonaws.com/tarballs/ds00113b_R2.0.0_metadata.tgz","revision":"2.0.0"},{"title":"Raw data in AWS (uncurated)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113b_raw.tgz","revision":null}],"contacts":[]},{"accession_number":"ds000174","project_name":"T1-weighted structural MRI study of cannabis users at baseline and 3 years follow up","summary":"Heavy cannabis users (N=20, age baseline M=20.5, SD=2.1) and non-cannabis using healthy controls (N=22, age baseline M=21.6, SD=2.45) underwent a comprehensive psychological assessment and a T1-weighted structural MRI scan at baseline and 3 years follow-up.
\r\n\r\nAll structural MRI scans were acquired using a 3T MRI scanner (Intera, Philips Healthcare, Best, The Netherlands) with a phased array SENSE eight-channel receiver head coil. For each participant, a T1-weighted structural MRI image was acquired (T1 turbo field echo, TR 9.6 s, TE 4.6 s, 182 slices, slice thickness 1.2 mm, FOV 256x256 mm, in-plane resolution 256x256 mm, flip angle 8 degrees).
\r\n\r\nData in this dataset: T1-weighted high-resolution structural
\r\n","sample_size":42,"scanner_type":"Philips Intera 3T","acknowledgements":"This study is supported through funding received from the Academic Medical Center of the University of Amsterdam and the Netherlands Organization for Scientific research - Health Research and Development, ZonMW grant #31180002 awarded to A.E. Goudriaan and W. van den Brink and grant #31160007 awarded to L. de Haan. Additional funding was obtained from Vici grant #453.008.001 awarded to R.W. Wiers by the Dutch Organization for Scientific Research (NWO) and from the Amsterdam Brain Imaging Center (BIC) for MRI scans. ","license_title":"CC BY-NC","license_url":"http://creativecommons.org/licenses/by-nc/4.0/legalcode","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Grey Matter Changes Associated with Heavy Cannabis Use: A Longitudinal sMRI Study.","url":"https://www.ncbi.nlm.nih.gov/pubmed/27224247"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2016-04-13"}],"investigator_set":[{"investigator":"Lieuwe de Haan"},{"investigator":"Anneke E. Goudriaan"},{"investigator":"Dick J. Veltman"},{"investigator":"Marise W. J. Machielsen"},{"investigator":"Carin J. Meijer"},{"investigator":"Reinout W. Wiers"},{"investigator":"Wim van den Brink"},{"investigator":"Wilhelmina A.M. Vingerhoets"},{"investigator":"Janna Cousijn"},{"investigator":"Laura Koenders"}],"link_set":[{"title":"All data for this dataset","url":"http://openfmri.s3.amazonaws.com/tarballs/ds174_R1.0.0_all_data.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000203","project_name":"Visual imagery and false memory for pictures","summary":"Visual mental imagery might be critical in the ability to discriminate imagined from perceived pictures. Our aim was to investigate the neural bases of this specific type of reality-monitoring process in individuals with high visual imagery abilities. Methods: A reality-monitoring task was administered to twenty-six healthy participants using functional magnetic resonance imaging. During the encoding phase, 45 words designating common items, and 45 pictures of other common items, were presented in random order. During the retrieval phase, participants were required to remember whether a picture of the item had been presented, or only a word. Two subgroups of participants with a propensity for high vs. low visual imagery were contrasted.
\r\n","sample_size":26,"scanner_type":"1.5T General Electric Signa HDe scanner","acknowledgements":"This work was supported by a Miguel Servet contract (CP09/00292) and a grant PI10/02479 from the Instituto de Salud Carlos III – Subdirección General de Evaluación y Fomento de la Investigación Sanitaria – co-funded by Fondo Europeo de Desarrollo Regional (FEDER), both to GB; a grant PRRMAB-A2011-19251 from the Sardinia Region to SS; and a contract PTA2011-4983-I from the Ministerio de Ciencia e Innovación, Spain to CS-O. The study was also supported by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Visual imagery and false memory for pictures: A functional magnetic resonance imaging study in healthy participants","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5207728/"}],"task_set":[{"cogat_id":"trm_4f240edf92865","number":1,"name":"mental imagery task","url":"http://www.cognitiveatlas.org/id/trm_4f240edf92865"}],"revision_set":[{"revision_number":"1.0.2","notes":"- Added anatomical data\r\n- Reran MRIQC\r\n- Fixed bugs in several top-level metadata files","date_set":"2017-08-28"},{"revision_number":"1.0.1","notes":"- Fixed events files, which were all identical to one another in R1.0.0","date_set":"2016-10-17"},{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2016-07-17"}],"investigator_set":[{"investigator":"Gildas Brébion"},{"investigator":"Ana María Sánchez Laforga"},{"investigator":"Susana Ochoa"},{"investigator":"Daniel Muñoz-Samons"},{"investigator":"Carl Senior"},{"investigator":"Sara Siddi"},{"investigator":"Christian Stephan-Otto"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000203/ds000203_R1.0.2/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.2"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000203/ds000203_R1.0.2/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.2"},{"title":"MRIQC results (813 MB)","url":"https://s3.amazonaws.com/openneuro/ds000203/ds000203_R1.0.2/compressed/ds000203_R1.0.2_mriqc.zip","revision":"1.0.2"},{"title":"Metadata (5.7 KB)","url":"https://s3.amazonaws.com/openneuro/ds000203/ds000203_R1.0.2/compressed/ds000203_R1.0.2_metadata.zip","revision":"1.0.2"},{"title":"Data for All Subjects (2 GB)","url":"https://s3.amazonaws.com/openneuro/ds000203/ds000203_R1.0.2/compressed/ds000203_R1.0.2_data.zip","revision":"1.0.2"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000203_R1.0.1_metadata.zip","revision":"1.0.1"},{"title":"Data for All Subjects (1-26)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000203_R1.0.1_data.zip","revision":"1.0.1"},{"title":"Dataset Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000203_R1.0.1_derivatives.zip","revision":"1.0.1"},{"title":"Dataset Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds203_R1.0.0_derivatives.tgz","revision":"1.0.0"},{"title":"Data for All Subjects (1-26)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds203_R1.0.0_data.tgz","revision":"1.0.0"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds203_R1.0.0_metadata.tgz","revision":"1.0.0"}],"contacts":[{"email":"","name":"Christian Stephan-Otto","website":""}]},{"accession_number":"ds000232","project_name":"Adjudicating between face-coding models with individual-face fMRI responses","summary":"The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli. In the fusiform face area, a face-space coding model with sigmoidal ramp tuning provided a better account of the data than one based on exemplar tuning. However, an image-processing model with weighted banks of Gabor filters performed similarly. Accounting for the data required the inclusion of a measurement-level population averaging mechanism that approximates how fMRI voxels locally average distinct neuronal tunings. Our study demonstrates the importance of comparing multiple models and of modeling the measurement process in computational neuroimaging.
\r\n","sample_size":10,"scanner_type":"3T MRI (Siemens Tim Trio)","acknowledgements":"We are grateful to Jenna Parker for assistance with data collection and to Marta Correia for assistance with 3D EPI protocols. ","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[],"revision_set":[{"revision_number":"1.0.1","notes":"-- changed path for scripts in code directory","date_set":"2017-08-02"},{"revision_number":"1.0.0","notes":"-- Initial Release","date_set":"2017-07-11"}],"investigator_set":[{"investigator":"Nikolaus Kriegeskorte"},{"investigator":"Johan D Carlin"}],"link_set":[{"title":"MRIQC Functional group report","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.1"},{"title":"MRIQC Anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.1"},{"title":"Metadata, Derivatives and MRIQC","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.1/compressed/ds000232_R1.0.1_metadata.zip","revision":"1.0.1"},{"title":"Data for subject 10 (2.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.1/compressed/ds000232_R1.0.1_sub010.zip","revision":"1.0.1"},{"title":"Data for subjects 07-09 (8.6GB)","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.1/compressed/ds000232_R1.0.1_sub07-09.zip","revision":"1.0.1"},{"title":"Data for subjects 04-06 (8.1GB)","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.1/compressed/ds000232_R1.0.1_sub04-06.zip","revision":"1.0.1"},{"title":"Data for subjects 01-03 (8.1GB)","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.1/compressed/ds000232_R1.0.1_sub01-03.zip","revision":"1.0.1"},{"title":"MRIQC","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/compressed/ds000232_R1.0.0_mriqc.zip","revision":"1.0.0"},{"title":"Metadata, Derivatives and MRIQC","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/compressed/ds000232_R1.0.0_metadata.zip","revision":"1.0.0"},{"title":"Data for subject 10","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/compressed/ds000232_R1.0.0_sub10.zip","revision":"1.0.0"},{"title":"Data for subjects 07-09 (8.6GB)","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/compressed/ds000232_R1.0.0_sub07-09.zip","revision":"1.0.0"},{"title":"Data for subjects 04-06 (8.0 GB)","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/compressed/ds000232_R1.0.0_sub04-06.zip","revision":"1.0.0"},{"title":"Data for subjects 01-03 (8.1GB)","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/compressed/ds000232_R1.0.0_sub01-03.zip","revision":"1.0.0"},{"title":"MRIQC Functional group report","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC Anatomical group report ","url":"https://s3.amazonaws.com/openneuro/ds000232/ds000232_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"}],"contacts":[{"email":"johan.carlin@gmail.com","name":"Johan Carlin","website":""}]},{"accession_number":"ds000148","project_name":"Who can afford self-control? The neural efficiency mechanism explains effective self-regulation of behavior","summary":"Dataset related to "Who can afford self-control? The neural efficiency mechanism explains effective self-regulation of behavior" (under revision). The task used was object 2-back, with targets at n-2 and lures at n-1 position. The subjects were required to press a response button when the target was present and refrain from reacting when a lure was presented. Each subject completed three runs. Psychometric assessment included Raven's Advanced Progressive Matrices and TAO and trait self-control questionnaire, AS36.
\r\n","sample_size":49,"scanner_type":"GE Discovery MR 750","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4ebd477ab5a11","number":1,"name":"object n-back","url":"http://www.cognitiveatlas.org/id/trm_4ebd477ab5a11"}],"revision_set":[{"revision_number":"1.0.0","notes":"--Initial publishing of curated data","date_set":"2016-08-04"}],"investigator_set":[{"investigator":"Marcel Falkiewicz"},{"investigator":"Bartłomiej Kucharzyk"},{"investigator":"Magdalena Senderecka"},{"investigator":"Edward Nęcka"}],"link_set":[{"title":"Data for All Subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds148_R1.0.0_data.tgz","revision":"1.0.0"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds148_R1.0.0_metadata.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000002","project_name":"Classification learning","summary":"Subjects performed a classification learning task with two different problems (across different runs), using a "weather prediction" task. In one (probabilistic) problem, the labels were probabilistically related to each set of cards. In another (deterministic) problem, the labels were deterministically related to each set of cards. After learning, subjects participated in an event-related block of judgment only (no feedback) in which they were presented with stimuli from both of the training problems.
\r\n\r\n\r\n\r\n
Note (30 Jan 2016): It was reported that the anatomical volumes for subjects sub016 and sub017 are identical. Updated data downloads will be available soon.
\r\n\r\nNote (22 Feb 2016): In addition to the above note, it was found that some subjects highres001 and inplane data did not seem to be from the same person. We advise using this dataset with caution until these issues are corrected.
\r\n\r\n\r\n","sample_size":17,"scanner_type":"3 T Siemens Allegra MRI scanner","acknowledgements":"Whitehall Foundation and NSF grant BCS-0223843 to R.A.P. The authors thank Allan J. Tobin and Robert Bilder for helpful discussion and encouragement, Sabrina Tom for scanning and Catherine Myers and Daphna Shohamy for help with task design.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Long-term test-retest reliability of fMRI","url":"http://www.ncbi.nlm.nih.gov/pubmed/16139527"}],"task_set":[{"cogat_id":"trm_4cacf22a22d80","number":1,"name":"Probabilistic classification task","url":"http://www.cognitiveatlas.org/id/trm_4cacf22a22d80"},{"cogat_id":"trm_4e8dd3831f0cc","number":2,"name":"deterministic classification","url":"http://www.cognitiveatlas.org/id/trm_4e8dd3831f0cc"},{"cogat_id":"trm_4ebc9d2e397f2","number":3,"name":"classification probe without feedback","url":"http://www.cognitiveatlas.org/id/trm_4ebc9d2e397f2"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2011-10-06"},{"revision_number":"2.0.5","notes":"- Removed anat/sub-**_inplaneT2.nii.gz files for sub-01 and sub-02 as they were same","date_set":"2016-11-04"},{"revision_number":"2.0.4","notes":"- Removed T1's for sub-16 and sub-17 as they were duplicate. ","date_set":"2016-10-24"},{"revision_number":"2.0.3","notes":"- Corrected Authors field in dataset_description.json ","date_set":"2016-10-13"},{"revision_number":"2.0.2","notes":"- corrected dataset_description.json\r\n- removed .DS_store files","date_set":"2016-10-13"},{"revision_number":"2.0.1","notes":"- added authors to dataset_description.json","date_set":"2016-09-30"},{"revision_number":"2.0.0","notes":"- converted to BIDS format","date_set":"2016-09-28"}],"investigator_set":[{"investigator":"Gluck, M.A. "},{"investigator":"Poldrack, R.A. "},{"investigator":"Aron, A.R."}],"link_set":[{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000002/ds000002_R1.0.0/compressed/ds002_raw.tgz","revision":"1.0.0"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000002/ds000002_R2.0.5/compressed/ds000002_R2.0.5_raw.zip","revision":"2.0.5"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000002/ds000002_R2.0.4/compressed/ds000002_R2.0.4_raw.zip","revision":"2.0.4"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000002/ds000002_R2.0.3/compressed/ds000002_R2.0.3_raw.zip","revision":"2.0.3"},{"title":"Raw Data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000002/ds000002_R2.0.2/compressed/ds000002_R2.0.2_raw.zip","revision":"2.0.2"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000002/ds000002_R2.0.1/compressed/ds000002_R2.0.1_raw.tgz","revision":"2.0.1"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000002/ds000002_R2.0.0/compressed/ds000002_R2.0.0_raw.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000102","project_name":"Flanker task (event-related)","summary":"
The "NYU Slow Flanker" dataset comprises data collected from 26 healthy adults while they performed a slow event-related Eriksen Flanker task. **Please note that all data have been uploaded regardless of quality- it is up to the user to check for data quality (movement etc).
\r\n\r\nOn each trial (inter-trial interval (ITI) varied between 8 s and 14 s; mean ITI=12 s),participants used one of two buttons on a response pad to indicate the direction of a central arrow in an array of 5 arrows. In congruent trials the flanking arrows pointed in the same direction as the central arrow (e.g., < < < < <), while in more demanding incongruent trials the flanking arrows pointed in the opposite direction (e.g., < < > < <).
\r\n\r\nSubjects performed two 5-minute blocks, each containing 12 congruent and 12 incongruent trials, presented in a pseudorandom order.
\r\n\r\nFunctional imaging data were acquired using a research dedicated Siemens Allegra 3.0 T scanner, with a standard Siemens head coil, located at theNYU Center for Brain Imaging. We obtained 146 contiguous echo planar imaging (EPI) whole-brain functional volumes (TR=2000 ms; TE=30 ms; flip angle=80, 40 slices, matrix=64x64; FOV=192 mm; acquisition voxel size=3x3x4mm) during each of the two flanker task blocks. A high-resolution T1-weighted anatomical image was also acquired using a magnetization prepared gradient echo sequence (MPRAGE, TR=2500 ms; TE= 3.93 ms; TI=900 ms; flip angle=8; 176 slices, FOV=256 mm).
\r\n\r\nPlease cite one of these papers listed below if you use these data.
\r\n","sample_size":26,"scanner_type":"Siemens Allegra","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics","url":"http://www.ncbi.nlm.nih.gov/pubmed/20974260"},{"title":"Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity","url":"http://www.ncbi.nlm.nih.gov/pubmed/20079856"},{"title":"Competition between functional brain networks mediates behavioral variability","url":"http://www.ncbi.nlm.nih.gov/pubmed/17919929"}],"task_set":[{"cogat_id":"tsk_4a57abb949a4f","number":1,"name":"Eriksen flanker task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949a4f"}],"revision_set":[{"revision_number":"2.0.0","notes":" - Repackaged in BIDS format. \r\n - TRs corrected in imaging NIFTI headers.","date_set":"2016-05-27"},{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2011-10-06"}],"investigator_set":[{"investigator":"Milham MP"},{"investigator":"Castellanos FX"},{"investigator":"Biswal BB"},{"investigator":"Uddin LQ"},{"investigator":"Kelly AMC"}],"link_set":[{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000102/ds000102_R1.0.0/compressed/ds102_raw.tgz","revision":"1.0.0"},{"title":"Data for all subjects","url":"https://s3.amazonaws.com/openneuro/ds000102/ds000102_R2.0.0/compressed/ds102_R2.0.0_all_data.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000237","project_name":"Phonological memory in sign language relies on the visuomotor neural system outside the left hemisphere language network","summary":"Using functional magnetic resonance imaging, the present study investigated neural activation while bilinguals of spoken and signed language were engaged in a sequence memory span task. On each trial, participants viewed a nonsense syllable sequence presented either as written letters or as fingerspelling (4-7 syllables in length) and then held the syllable sequence for 12 s. Behavioral analysis revealed that participants relied on phonological memory while holding verbal information regardless of the type of input modality. At the neural level, this maintenance stage broadly activated the left-hemisphere language network, including the inferior frontal gyrus, supplementary motor area, superior temporal gyrus and inferior parietal lobule, for both letter and fingerspelling conditions.
\r\n","sample_size":13,"scanner_type":"Siemens Trio 3 T head scanner with 32 channel phased-array head coil","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial Release","date_set":"2017-08-10"}],"investigator_set":[{"investigator":"Yuji Kanazawa"}],"link_set":[{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000237/uncompressed/derivatives/mriqc/T1w_group.html","revision":"1.0.0"},{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000237/uncompressed/derivatives/mriqc/bold_group.html","revision":"1.0.0"},{"title":"Metadata ","url":"https://s3.amazonaws.com/openneuro/ds000237/compressed/ds000237_R1.0.0_metadata.zip","revision":"1.0.0"},{"title":"Data for Subjects 08-13 (4.0 GB)","url":"https://s3.amazonaws.com/openneuro/ds000237/compressed/ds000237_R1.0.0_sub08-13.zip","revision":"1.0.0"},{"title":"Data for Subjects 01-07 (4.7 GB)","url":"https://s3.amazonaws.com/openneuro/ds000237/compressed/ds000237_R1.0.0_sub01-07.zip","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000228","project_name":"MRI data of 3-12 year old children and adults during viewing of a short animated film","summary":"Participants watched a silent version of Disney Pixar's "Partly Cloudy," a 5.6-minute animated movie. The movie was preceded by 10s of rest, and participants were instructed to watch the movie and remain still.
\r\n","sample_size":155,"scanner_type":"3-Tesla Siemens Tim Trio; Syngo MR B17A","acknowledgements":"We thank the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain Research at MIT, Jorie Koster-Hale, Natalia Velez-Alicea, Mika Asaba, and Nir Jacoby for help with data collection. We thank Todd Thompson for his work making this dataset publicly available.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4c898da401420","number":1,"name":"film viewing","url":"http://www.cognitiveatlas.org/id/trm_4c898da401420"}],"revision_set":[{"revision_number":"1.0.1","notes":" - Corrected participants.tsv","date_set":"2017-11-17"},{"revision_number":"1.0.0","notes":" - Initial release","date_set":"2017-09-06"}],"investigator_set":[{"investigator":"Rebecca Saxe"},{"investigator":"Alexa Riobueno-Naylor"},{"investigator":"Grace Lisandrelli"},{"investigator":"Hilary Richardson"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.1/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.1"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.1/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.1"},{"title":"Data for all subjects (4.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.1/compressed/ds000228_R1.0.1_sub001-155.zip","revision":"1.0.1"},{"title":"Metadata (7.2 KB)","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.1/compressed/ds000228_R1.0.1_metadata.zip","revision":"1.0.1"},{"title":"Derivatives (14.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.1/compressed/ds000228_R1.0.1_derivatives.zip","revision":"1.0.1"},{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for all subjects (4.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.0/compressed/ds000228_R1.0.0_sub001-155.zip","revision":"1.0.0"},{"title":"Metadata (7.2 KB)","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.0/compressed/ds000228_R1.0.0_metadata.zip","revision":"1.0.0"},{"title":"Derivatives (14.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000228/ds000228_R1.0.0/compressed/ds000228_R1.0.0_derivatives.zip","revision":"1.0.0"}],"contacts":[{"email":"hlrich@mit.edu","name":"Hilary Richardson","website":""}]},{"accession_number":"ds000003","project_name":"Rhyme judgment","summary":"Subjects were presented with pairs of either words or pseudowords, and made rhyming judgments for each pair.
\r\n","sample_size":13,"scanner_type":"TBA","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4d949c5b0e380","number":1,"name":"rhyme verification task","url":"http://www.cognitiveatlas.org/id/trm_4d949c5b0e380"}],"revision_set":[{"revision_number":"2.0.2","notes":"- edited authors field in dataset_description.json file\r\n","date_set":"2016-10-20"},{"revision_number":"2.0.1","notes":"- added authors in dataset description","date_set":"2016-09-30"},{"revision_number":"2.0.0","notes":"- converted to bids format","date_set":"2016-09-28"},{"revision_number":"1.1.0","notes":"Update orientation information in nifti headers for better left/right determination.","date_set":"2016-02-18"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2011-10-06"}],"investigator_set":[{"investigator":"Poldrack, R.A."},{"investigator":"Xue, G."}],"link_set":[{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000003/ds000003_R2.0.1/compressed/ds000003_R2.0.1_raw.tgz","revision":"2.0.1"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000003/ds000003_R2.0.2/compressed/ds000003_R2.0.2_raw.zip","revision":"2.0.2"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000003/ds000003_R2.0.0/compressed/ds000003_R2.0.0_raw.tgz","revision":"2.0.0"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000003/ds000003_R1.1.0/compressed/ds003_R1.1.0_raw.tgz","revision":"1.1.0"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000003/ds000003_R1.0.0/compressed/ds003_raw.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000113d","project_name":"Simultaneous fMRI/eyetracking while movie watching, plus visual localizers","summary":"Extension of the studyforrest.org dataset published in Hanke et al. (2014; doi:10.1038/sdata.2014.3) with additional acquisitions for 15 of the original 20 particpants. These additions include: retinotopic mapping, a localizer paradigm for higher visual areas (FFA, EBA, PPA), and another 2 hour movie recording with 3T full-brain BOLD fMRI with simultaneous 1000 Hz eyetracking.
\r\n\r\nThis dataset may also be accessed using git/git-annex. Please refer to the github project page: studyforrest-data-phase2 for more information.
\r\n","sample_size":31,"scanner_type":"Philips Achieva 3T","acknowledgements":"Only open-source software was employed in this study. We thank their respective authors for making it publicly available. We acknowledge the support of the Combinatorial NeuroImaging Core Facility at the Leibniz Institute for Neurobiology in Magdeburg, and the German federal state of Saxony-Anhalt, Project: Center for Behavioral Brain Sciences. This research was, in part, also supported by the German Federal Ministry of Education and Research (BMBF) as part of a US-German collaboration in computational neuroscience (CRCNS), co-funded by the BMBF and the US National Science Foundation (BMBF 01GQ1112; NSF 1129855). Work on the data-sharing technology employed for this research was supported by US-German CRCNS project, co-funded by the BMBF and the US National Science Foundation (BMBF 01GQ1411; NSF 1429999).","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4c898da401420","number":1,"name":"film viewing","url":"http://www.cognitiveatlas.org/id/trm_4c898da401420"},{"cogat_id":"trm_4c898f72228f3","number":2,"name":"fixation task","url":"http://www.cognitiveatlas.org/id/trm_4c898f72228f3"},{"cogat_id":"tsk_4a57abb949bcd","number":3,"name":"n-back task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949bcd"},{"cogat_id":"trm_558c4d3105abf","number":4,"name":"retinotopic mapping task","url":"http://www.cognitiveatlas.org/id/trm_558c4d3105abf"}],"revision_set":[{"revision_number":"2.0.0","notes":"- Converted to BIDS with updated dataset as per github","date_set":"2016-11-09"},{"revision_number":"1.1.0","notes":"Addition of visual stimulus implementation files in the code/stimulus directory of the Dataset Metadata and Code archive.","date_set":"2016-05-27"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2016-04-05"}],"investigator_set":[{"investigator":"Jörg Stadler"},{"investigator":"Daniel Kottke"},{"investigator":"Vittorio Iacovella"},{"investigator":"Michael Hoffmann"},{"investigator":"Christian Häusler"},{"investigator":"J. Swaroop Guntupalli"},{"investigator":"Florian J. Baumgartner"},{"investigator":"Ayan Sengupta"},{"investigator":"Falko R. Kaule"},{"investigator":"Michael Hanke"}],"link_set":[{"title":"Data for Subjects 30-36","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113d_R2.0.0_sub30-36.zip","revision":"2.0.0"},{"title":"Data for Subjects 17-29","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113d_R2.0.0_sub17-29.zip","revision":"2.0.0"},{"title":"Data for Subjects 06-16","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113d_R2.0.0_sub06-16.zip","revision":"2.0.0"},{"title":"Data for Subjects 01 - 05","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113d_R2.0.0_sub01-05.zip","revision":"2.0.0"},{"title":"Dataset Metadata and Code","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000113d_R2.0.0_metadata.zip","revision":"2.0.0"},{"title":"Data for Subjects 31 - 36","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.1.0_31-36.tgz","revision":"1.1.0"},{"title":"Data for Subjects 17 - 30","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.1.0_17-30.tgz","revision":"1.1.0"},{"title":"Data for Subjects 06 - 16","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.1.0_06-16.tgz","revision":"1.1.0"},{"title":"Data for Subjects 01 - 05","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.1.0_01-05.tgz","revision":"1.1.0"},{"title":"Dataset Metadata and Code","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.1.0_metadata.tgz","revision":"1.1.0"},{"title":"Data for Subjects 31 - 36","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.0.0_31-36.tgz","revision":"1.0.0"},{"title":"Data for Subjects 17 - 30","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.0.0_17-30.tgz","revision":"1.0.0"},{"title":"Data for Subjects 06 - 16","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.0.0_06-16.tgz","revision":"1.0.0"},{"title":"Data for Subjects 01 - 05","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.0.0_01-05.tgz","revision":"1.0.0"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113d_R1.0.0_metadata.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000005","project_name":"Mixed-gambles task","summary":"Subjects were presented with mixed (gain/loss) gambles, and decided whether they would accept each gamble. No outcomes of these gambles were presented during scanning, but after the scan three gambles were selected at random and played for real money.
\r\n","sample_size":16,"scanner_type":"3T Siemens AG (Erlangen, Germany) Allegra MRI scanner","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"The neural basis of loss aversion in decision making under risk","url":"http://www.ncbi.nlm.nih.gov/pubmed/17255512"}],"task_set":[{"cogat_id":"trm_4cacee4a1d875","number":1,"name":"mixed gambles task","url":"http://www.cognitiveatlas.org/id/trm_4cacee4a1d875"}],"revision_set":[{"revision_number":"2.0.1","notes":"- Added authors to dataset_description.json","date_set":"2016-10-21"},{"revision_number":"2.0.0","notes":"Repackaged in BIDS format.","date_set":"2016-05-20"},{"revision_number":"1.0.0","notes":"Initial release","date_set":"2011-01-01"},{"revision_number":"1.1.0","notes":"Updated orientation information in nifti headers for improved left-right determination.","date_set":"2016-02-18"}],"investigator_set":[{"investigator":"Poldrack R.A."},{"investigator":"Trepel C."},{"investigator":"Fox C.R."},{"investigator":"Tom S.M."}],"link_set":[{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000005/ds000005_R2.0.1/compressed/ds000005_R2.0.1_raw.zip","revision":"2.0.1"},{"title":"Data for all subjects","url":"https://s3.amazonaws.com/openneuro/ds000005/ds000005_R2.0.0/compressed/ds000005_R2.0.0.tgz","revision":"2.0.0"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000005/ds000005_R1.1.0/compressed/ds005_R1.1.0_raw.tgz","revision":"1.1.0"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000005/ds000005_R1.0.0/compressed/ds005_raw.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000007","project_name":"Stop-signal task with spoken & manual responses","summary":"Subjects performed a stop-signal task with one of three response types: manual response, spoken letter naming, and spoken pseudoword naming.
\r\n","sample_size":20,"scanner_type":"3T Siemens Allegra MRI scanner","acknowledgements":"Acknowledgements: We thank Sabrina Tom for help with data analysis. Conflict of Interest: None declared. \r\nFunding: James S. McDonnell Foundation 21st Century Science Program Grant (to R.P); Foundation for Psychological Research, University of California-Los Angeles Center for Culture, Brain and Development (to G.X.).","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Common Neural Substrates for Inhibition of Spoken and Manual Responses","url":"http://www.ncbi.nlm.nih.gov/pubmed/18245044"}],"task_set":[{"cogat_id":"tsk_4a57abb949e1a","number":1,"name":"stop signal task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949e1a"},{"cogat_id":"trm_5181f83b77fa4","number":2,"name":"stop signal task with letter naming","url":"http://www.cognitiveatlas.org/id/trm_5181f83b77fa4"},{"cogat_id":"trm_5181f863d24f4","number":3,"name":"stop signal task with pseudo word naming","url":"http://www.cognitiveatlas.org/id/trm_5181f863d24f4"}],"revision_set":[{"revision_number":"1.0.1","notes":"Removed sub021 after realizing that it was a duplicate of sub011","date_set":"2013-05-06"},{"revision_number":"2.0.1","notes":"- Added task names in '_bold.json' files.","date_set":"2016-10-27"},{"revision_number":"2.0.0","notes":"Repackaged in BIDS format","date_set":"2016-05-04"},{"revision_number":"1.0.0","notes":"Original Release","date_set":"2011-10-06"},{"revision_number":"1.1.0","notes":"Fixed orientation information in NIFTI headers. \r\nReplaced zero'd-out orientation information with information extracted from original DICOM. ","date_set":"2016-02-17"}],"investigator_set":[{"investigator":"Poldrack RA"},{"investigator":"Aron AR"},{"investigator":"Xue G"}],"link_set":[{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000007/ds000007_R1.0.1/compressed/ds007_raw.tgz","revision":"1.0.1"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000007/ds000007_R2.0.1/compressed/ds000007_R2.0.1_raw.zip","revision":"2.0.1"},{"title":"Data for all subjects","url":"https://s3.amazonaws.com/openneuro/ds000007/ds000007_R2.0.0/compressed/ds007_R2.0.0_01-20.tgz","revision":"2.0.0"},{"title":"Metadata and derivatives","url":"https://s3.amazonaws.com/openneuro/ds000007/ds000007_R2.0.0/compressed/ds007_R2.0.0_metadata_derivatives.tgz","revision":"2.0.0"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000007/ds000007_R1.1.0/compressed/ds007_R1.1.0_raw.tgz","revision":"1.1.0"}],"contacts":[]},{"accession_number":"ds000001","project_name":"Balloon Analog Risk-taking Task","summary":"Subjects perform the Balloon Analog Risk-taking Task in an event-related design.
\r\n\r\nNote: The original highres image for sub004 was not available, so the skull-stripped version is included as highres001.nii.gz
\r\n","sample_size":16,"scanner_type":"Siemens Allegra 3T","acknowledgements":"This work was supported by NSF DMI-0433693 (R. Poldrack and C. Fox, principal investigators, PIs). We would like to thank Elena Stover for assistance with data collection and for helpful comments on an earlier version of this manuscript.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Decreasing ventromedial prefrontal cortex activity during sequential risk-taking: an FMRI investigation of the balloon analog risk task.","url":"http://www.ncbi.nlm.nih.gov/pubmed/22675289"}],"task_set":[{"cogat_id":"trm_4d559bcd67c18","number":1,"name":"Balloon Analogue Risk Task (BART)","url":"http://www.cognitiveatlas.org/id/trm_4d559bcd67c18"}],"revision_set":[{"revision_number":"2.0.4","notes":"- Corrected Events files related to Revision R2.0.1","date_set":"2016-11-02"},{"revision_number":"2.0.3","notes":"- set origins for all anatomical and functional images.","date_set":"2016-10-20"},{"revision_number":"2.0.2","notes":"- Added Authors to dataset_description.json","date_set":"2016-10-13"},{"revision_number":"2.0.1","notes":"- Fixed events file which was affected during openfmri to bids conversion.","date_set":"2016-09-07"},{"revision_number":"2.0.0","notes":"- Converted to BIDS standard.","date_set":"2016-05-24"},{"revision_number":"1.1.0","notes":"Updated orientation information in NIFTI headers for better left-right determination.","date_set":"2016-02-18"},{"revision_number":"1.0.0","notes":"","date_set":"2012-07-10"}],"investigator_set":[{"investigator":"Russell A. Poldrack"},{"investigator":"Craig Fox"},{"investigator":"Christopher Trepel"},{"investigator":"Tom Schonberg"}],"link_set":[{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000001/ds000001_R2.0.3/compressed/ds000001_R2.0.3_raw.zip","revision":"2.0.3"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000001/ds000001_R2.0.2/compressed/ds000001_R2.0.2_raw.zip","revision":"2.0.2"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000001/ds000001_R2.0.1/compressed/ds001_R2.0.1_raw.tgz","revision":"2.0.1"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000001/ds000001_R1.1.0/compressed/ds001_R1.1.0_raw.tgz","revision":"1.1.0"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000001/ds000001_R2.0.4/compressed/ds000001_R2.0.4_raw.zip","revision":"2.0.4"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000001/ds000001_R2.0.0/compressed/ds001_R2.0.0_raw.tgz","revision":"2.0.0"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000001/ds000001_R1.0.0/compressed/ds001_raw.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000006","project_name":"Living-nonliving decision with plain or mirror-reversed text","summary":"Subjects performed a living-nonliving decision on items presented in either plain or mirror-reversed text. ds000006A represents the first session and ds000006B represents the second session.
\r\n","sample_size":14,"scanner_type":"Siemens Allegra 3T","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_5176cf9d3d512","number":1,"name":"living/nonliving judgment on mirror-reversed and plain-text words","url":"http://www.cognitiveatlas.org/id/trm_5176cf9d3d512"}],"revision_set":[{"revision_number":"2.0.1","notes":"- Added Authors to dataset_description.json","date_set":"2016-10-24"},{"revision_number":"2.0.0","notes":"Converted to BIDS. Added missing retest session (former known as ds006B). Added a data dictionary for trial types.","date_set":"2016-05-24"},{"revision_number":"1.1.0","notes":"Updated orientation information in NIFTI headers for improved left-right determination.","date_set":"2016-02-19"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2013-03-12"}],"investigator_set":[{"investigator":"R Poldrack"},{"investigator":"F Cazalis"},{"investigator":"E Stover"},{"investigator":"K Jimura"}],"link_set":[{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000006/ds000006_R2.0.0/compressed/ds006_R2.0.0_raw.tgz","revision":"2.0.0"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000006/ds000006_R2.0.1/compressed/ds000006_R2.0.1_raw.zip","revision":"2.0.1"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000006/ds000006_R1.1.0/compressed/ds006A_R1.1.0_raw.tgz","revision":"1.1.0"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000006/ds000006_R1.0.0/compressed/ds006A_raw.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000017","project_name":"Classification learning and stop-signal (1 year test-retest)","summary":"A group of eight subjects performed two tasks (selective stop-signal and probabilistic classification) on two different occasions separated by about one year. ds000017A reflects data from timepoint 1 and ds000017B reflects data from timepoint 2.
\r\n","sample_size":8,"scanner_type":"3T Siemens Allegra MRI scanner","acknowledgements":"High-Q Foundation","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4cacf22a22d80","number":1,"name":"Probabilistic classification task","url":"http://www.cognitiveatlas.org/id/trm_4cacf22a22d80"},{"cogat_id":"trm_4cacf3fbc503b","number":2,"name":"selective stop signal task","url":"http://www.cognitiveatlas.org/id/trm_4cacf3fbc503b"}],"revision_set":[{"revision_number":"2.0.1","notes":"- Updated orientation information in NIFTI headers for better left-right determination.","date_set":"2017-07-31"},{"revision_number":"2.0.0","notes":"- Converted to BIDS standard\r\n- Removed sub-6_ses-timepoint2_task-probabilisticclassification_run-01_bold.nii.gz, sub-6_ses-timepoint2_task-probabilisticclassification_run-02_bold.nii.gz as there are no events files for these runs","date_set":"2016-11-07"},{"revision_number":"1.0.0","notes":"","date_set":"2011-10-06"},{"revision_number":"1.1.0","notes":"Updated orientation information in NIFTI headers for better left-right determination","date_set":"2016-02-19"}],"investigator_set":[{"investigator":"Poldrack"},{"investigator":"Aron"},{"investigator":"Rizk-Jackson"}],"link_set":[{"title":"MRIQC Bold group report","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R2.0.1/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"2.0.1"},{"title":"MRIQC T1w group report","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R2.0.1/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"2.0.1"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R2.0.1/compressed/ds000017_R2.0.1.zip","revision":"2.0.1"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R2.0.0/compressed/ds000017_R2.0.0_raw.zip","revision":"2.0.0"},{"title":"Raw data for ds000017B on AWS","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R1.1.0/compressed/ds017B_R1.1.0_raw.tgz","revision":"1.1.0"},{"title":"Raw data for ds000017A on AWS","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R1.1.0/compressed/ds017A_R1.1.0_raw.tgz","revision":"1.1.0"},{"title":"Models for ds000017B on AWS","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R1.0.0/compressed/ds017B_models.tgz","revision":"1.0.0"},{"title":"Models for ds000017A on AWS","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R1.0.0/compressed/ds017A_models.tgz","revision":"1.0.0"},{"title":"Raw data for ds000017B on AWS","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R1.0.0/compressed/ds017B_raw.tgz","revision":"1.0.0"},{"title":"Raw data for ds000017A on AWS","url":"https://s3.amazonaws.com/openneuro/ds000017/ds000017_R1.0.0/compressed/ds017A_raw.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000009","project_name":"The generality of self-control","summary":"This study examined four different forms of self-control in a single context to determine whether multiple forms were related in a single sample of healthy adults. Participants performed four different tasks within a single scanning session.
\r\n","sample_size":24,"scanner_type":"Siemens Trio","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Writeup on Gradworks","url":"http://gradworks.umi.com/34/01/3401764.html"}],"task_set":[{"cogat_id":"trm_4d559bcd67c18","number":1,"name":"Balloon Analogue Risk Task (BART)","url":"http://www.cognitiveatlas.org/id/trm_4d559bcd67c18"},{"cogat_id":"tsk_4a57abb949e1a","number":2,"name":"stop signal task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949e1a"},{"cogat_id":"trm_4da890594742a","number":3,"name":"emotional regulation task","url":"http://www.cognitiveatlas.org/id/trm_4da890594742a"},{"cogat_id":"tsk_4a57abb949e98","number":4,"name":"temporal discounting task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949e98"}],"revision_set":[{"revision_number":"2.0.3","notes":" - Added summary scores and individual item scores on all questionnaires under sourcedata\r\n","date_set":"2017-09-12"},{"revision_number":"2.0.2","notes":"- Fixed particpants.tsv\r\n- Added to README\r\n- Updated CogAtlasIDs","date_set":"2017-05-09"},{"revision_number":"2.0.1","notes":"Fixed NA values in events.tsv","date_set":"2016-05-08"},{"revision_number":"2.0.0","notes":"Dataset has been re-created and organized in BIDS format.","date_set":"2016-03-25"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2014-02-03"},{"revision_number":"1.1.0","notes":"Update orientation information in NIFTI headers for better left/right determination","date_set":"2016-02-21"}],"investigator_set":[{"investigator":"Russell Poldrack"},{"investigator":"Jessica Cohen"}],"link_set":[{"title":"Metadata and MRIQC","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.3/compressed/ds000009_R2.0.3_metadata_MRIQC.zip","revision":"2.0.3"},{"title":"MRIQC Anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.3/uncompressed/derivatives/mriqc/anatomical_group.pdf","revision":"2.0.3"},{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.3/uncompressed/derivatives/mriqc/functional_group.pdf","revision":"2.0.3"},{"title":"Data for subjects 01-29 (5.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.3/compressed/ds000009_R2.0.3_sub01-29.zip","revision":"2.0.3"},{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.2/uncompressed/derivatives/mriqc/functional_group.pdf","revision":"2.0.2"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.2/uncompressed/derivatives/mriqc/anatomical_group.pdf","revision":"2.0.2"},{"title":"Metadata (17.9 KB)","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.2/compressed/ds000009_R2.0.2_metadata.zip","revision":"2.0.2"},{"title":"MRIQC (119.2 MB)","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.2/compressed/ds000009_R2.0.2_mriqc.zip","revision":"2.0.2"},{"title":"Data for subjects 016-29 (2.7 GB)","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.2/compressed/ds000009_R2.0.2_sub16-29.zip","revision":"2.0.2"},{"title":"Data for subjects 01-15 (3.2 GB)","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.2/compressed/ds000009_R2.0.2_sub01-15.zip","revision":"2.0.2"},{"title":"Subjects 18 through 29 Data","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.1/compressed/ds009_R2.0.1_18-29.tgz","revision":"2.0.1"},{"title":"Subjects 01 through 17 Data","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.1/compressed/ds009_R2.0.1_01-17.tgz","revision":"2.0.1"},{"title":"Top Level Derivatives and Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds009_R2.0.1_metadata_derivatives.tgz","revision":"2.0.1"},{"title":"Subjects 18 through 29 Data","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.0/compressed/ds009_R2.0.0_18-29.tgz","revision":"2.0.0"},{"title":"Subjects 01 through 17 Data","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.0/compressed/ds009_R2.0.0_01-17.tgz","revision":"2.0.0"},{"title":"Top Level Derivatives and Metadata","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R2.0.0/compressed/ds009_R2.0.0_toplevel_metadata.tgz","revision":"2.0.0"},{"title":"Raw data in AWS - Subject Data Only","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R1.1.0/compressed/ds009_R1.1.0_raw.tgz","revision":"1.1.0"},{"title":"Raw data in AWS - Subject Data Only","url":"https://s3.amazonaws.com/openneuro/ds000009/ds000009_R1.0.0/compressed/ds009_raw.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000113","project_name":"A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie","summary":"This is a high-resolution functional magnetic resonance (fMRI) dataset — 20 participants recorded at high field strength (7 Tesla) during prolonged stimulation with an auditory feature film ("Forrest Gump''). In addition, a comprehensive set of auxiliary data (T1w, T2w, DTI, susceptibility-weighted image, angiography) as well as measurements to assess technical and physiological noise components have been acquired. An initial analysis confirms that these data can be used to study common and idiosyncratic brain response pattern to complex auditory stimulation. Among the potential uses of this dataset is the study of auditory attention and cognition, language and music perception as well as social perception. The auxiliary measurements enable a large variety of additional analysis strategies that relate functional response patterns to structural properties of the brain. Alongside the acquired data, we provide source code and detailed information on all employed procedures — from stimulus creation to data analysis. The total size of dataset is more than 350 GB. Therefore files for individual modalities are made available below. README.dataset_content provides an overview of the dataset and a description of the content for all available downloads. Note, access to individual files is possible via openfmri.org's XNAT server.
\r\n\r\n\r\n\r\n
Additional resources:
\r\n\r\nMore information and updates are made available at: http://www.studyforrest.org
\r\n\r\nSource code repository: http://github.com/hanke/gumpdata
\r\n\r\nDocumentation for the source code: http://gumpdata.readthedocs.org
\r\n","sample_size":20,"scanner_type":"7 Tesla Siemens MAGNETOM and 3 Tesla Philips Achieva","acknowledgements":"We are grateful to the authors of the German \"Forrest Gump'' audio description\r\nthat made this study possible and especially Bernd Benecke for his support.\r\nWe also want to thank Schweizer Radio und Fernsehen and Paramount Home\r\nEntertainment Germany for their permission to use the movie and audio\r\ndescription for this study. Thanks also go to Andreas Fügner and Marko Dombach\r\nfor their help with developing the audio stimulation equipment,\r\nRenate Körbs for helping with scanner operations. Furthermore, we thank Michael Casey for\r\nproviding us with a questionnaire to assess musical background.\r\nOnly open-source software was employed in this study. We thank their respective\r\nauthors for making it publicly available.\r\nThis research was funded by the German Federal Ministry of Education and\r\nResearch (BMBF) as part of a US-German collaboration in computational\r\nneuroscience (CRCNS), co-funded by the BMBF and the US National Science\r\nFoundation (BMBF 01GQ1112; NSF 1129855).\r\nMichael Hanke was supported by funds from the German federal state of\r\nSaxony-Anhalt, Project: Center for Behavioral Brain Sciences.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"A pre-print of the data descriptor manuscript is available here.","url":"http://www.nature.com/articles/sdata20143"}],"task_set":[],"revision_set":[],"investigator_set":[{"investigator":"Jörg Stadler"},{"investigator":"Wolf Zinke"},{"investigator":"Oliver Speck"},{"investigator":"Stefan Pollmann"},{"investigator":"Falko R. Kaule"},{"investigator":"Pierre Ibe"},{"investigator":"Florian J. Baumgartner"},{"investigator":"Michael Hanke"}],"link_set":[{"title":"Stimulus material, participant demographics and protocol descriptions [~200KB]","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_study_description.tar","revision":null},{"title":"README","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_README","revision":null},{"title":"Raw data for subject 20 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub020.tgz","revision":null},{"title":"Raw data for subject 19 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub019.tgz","revision":null},{"title":"Raw data for subject 18 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub018.tgz","revision":null},{"title":"Raw data for subject 17 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub017.tgz","revision":null},{"title":"Raw data for subject 16 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub016.tgz","revision":null},{"title":"Raw data for subject 15 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub015.tgz","revision":null},{"title":"Raw data for subject 14 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub014.tgz","revision":null},{"title":"Raw data for subject 13 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub013.tgz","revision":null},{"title":"Raw data for subject 12 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub012.tgz","revision":null},{"title":"Raw data for subject 11 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub011.tgz","revision":null},{"title":"Raw data for subject 10 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub010.tgz","revision":null},{"title":"Raw data for subject 9 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub009.tgz","revision":null},{"title":"Raw data for subject 8 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub008.tgz","revision":null},{"title":"Raw data for subject 7 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub007.tgz","revision":null},{"title":"Raw data for subject 6 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub006.tgz","revision":null},{"title":"Raw data for subject 5 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub005.tgz","revision":null},{"title":"Raw data for subject 4 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub004.tgz","revision":null},{"title":"Raw data for subject 3 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub003.tgz","revision":null},{"title":"Raw data for subject 2 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub002.tgz","revision":null},{"title":"Raw data for subject 1 in AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113_sub001.tgz","revision":null}],"contacts":[]},{"accession_number":"ds000108","project_name":"Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation","summary":"Although prefrontal cortex has been implicated in the cognitive regulation of emotion, the cortical-subcortical interactions that mediate this ability remain poorly understood. To address this issue, we identified a right ventrolateral prefrontal region (vlPFC) whose activity correlated with reduced negative emotional experience during cognitive reappraisal of aversive images. We then applied a pathway-mapping analysis on subcortical regions to locate mediators of the association between vlPFC activity and reappraisal success (i.e., reductions in reported emotion). Results identified two separable pathways that together explained approximately 50% of the reported variance in self-reported emotion: (1) a path through nucleus accumbens that predicted greater reappraisal success, and (2) a path through ventral amygdala that predicted reduced reappraisal success (i.e., more negative emotion). These results provide direct evidence that vlPFC is involved in both the generation and regulation of emotion through different subcortical pathways, suggesting a general role for this region in appraisal processes.
\r\n","sample_size":34,"scanner_type":"1.5T GE Signa Twin Speed Excite HD scanner (GE Medical Systems)","acknowledgements":"We would like to thank Niall Bolger for helpful discussion on path analysis, and the authors of SPM software for making it freely available. This paper was made possible with the support of NIH Grant MH076137 (K.O.), NIH Grant MH076136 (T.W.), and NSF 0631637 (T.W.)","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Neural mechanisms of emotion regulation: Evidence for two independent prefrontal-subcortical pathways","url":"http://www.ncbi.nlm.nih.gov/pubmed/18817740"}],"task_set":[{"cogat_id":"trm_4da890594742a","number":1,"name":"emotional regulation task","url":"http://www.cognitiveatlas.org/id/trm_4da890594742a"}],"revision_set":[{"revision_number":"1.0.1","notes":"- Revised release to fix problem with initial tarball","date_set":"2013-05-01"},{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2013-04-16"},{"revision_number":"2.0.0","notes":"- Converted to BIDS Standard","date_set":"2016-10-18"}],"investigator_set":[{"investigator":"Ochsner KN"},{"investigator":"Lindquist MA"},{"investigator":"Hughes BL"},{"investigator":"Davidson ML"},{"investigator":"Wager TD"}],"link_set":[{"title":"Raw data checksums","url":"http://openfmri.s3.amazonaws.com/tarballs/ds108_raw_checksums.txt","revision":"1.0.1"},{"title":"Raw data part3 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds108_raw_part3.tgz","revision":"1.0.1"},{"title":"Raw data part2 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds108_raw_part2.tgz","revision":"1.0.1"},{"title":"Raw data part1 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds108_raw_part1.tgz","revision":"1.0.1"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000108_R2.0.0_raw.zip","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000231","project_name":"Integration of sweet taste and metabolism determines carbohydrate reward-study 3","summary":"Non-caloric beverages were mixed from novel flavors, citric acid, sucralose and food coloring. Participants, with 3 similarly liked but differently flavored and colored beverages who were unable to detect maltodextrin participated in 6 exposure sessions during which each beverage was consumed 6 times consistently paired with one of 3 caloric loads (0,112.5 and 150 kcal). An fMRI session followed in which participants sampled the non-caloric versions of the 3 exposed beverage (CS-, CS112.5, and CS150), as well as a tasteless and odorless control solution.
\r\n","sample_size":9,"scanner_type":"Siemens TrioTim Syngo MR B17","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Behavioral data on Mendeley Data","url":"http://dx.doi.org/10.17632/94tc9t3txs.1"}],"task_set":[{"cogat_id":"trm_5887c029d46f4","number":1,"name":"Gustatory stimulation with liquid tastes or flavors ","url":"http://www.cognitiveatlas.org/id/trm_5887c029d46f4"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial Release","date_set":"2017-07-21"}],"investigator_set":[{"investigator":"Dana M Small"},{"investigator":"Martin Yeomans"},{"investigator":"Elizabeth Garcia"},{"investigator":"Nils B Kroemer"},{"investigator":"Wambura Fobbs"},{"investigator":"Barkha Patel"},{"investigator":"Richard Keith Babbs"},{"investigator":"Maria Geraldine Veldhuizen"}],"link_set":[{"title":"Metadata with MRIQC Reports ","url":"https://s3.amazonaws.com/openneuro/ds000231/ds000231_R1.0.0/compressed/ds000231_R1.0.0_metadata.zip","revision":"1.0.0"},{"title":"Data for subjects 06-09 (3.0 GB)","url":"https://s3.amazonaws.com/openneuro/ds000231/ds000231_R1.0.0/compressed/ds000231_R1.0.0_sub06-09.zip","revision":"1.0.0"},{"title":"Data for subjects 01-05 (4.0 GB)","url":"https://s3.amazonaws.com/openneuro/ds000231/ds000231_R1.0.0/compressed/ds000231_R1.0.0_sub01-05.zip","revision":"1.0.0"},{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000231/ds000231_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000231/ds000231_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Behavioral data on Mendeley Data","url":"http://dx.doi.org/10.17632/94tc9t3txs.1","revision":"1.0.0"}],"contacts":[{"email":"mveldhuizen@jbpierce.org","name":"Maria Veldhuizen","website":""}]},{"accession_number":"ds000241","project_name":"AK6","summary":"For validating the sensitivity of the proposed PICSO index (a new quality-assurance index for resting state fMRI) to functional connectivity, both fMRI dataset of phantom and human during resting state were acquired. The resting-state human dataset (n=12, age: 26.4 ± 2.1 y, females/males: 6/6) was acquired at four image resolutions:
\r\n\r\n1.3 × 1.3 × 2 mm3
\r\n\r\n2 × 2 × 2 mm3
\r\n\r\n3 × 3 × 3 mm3
\r\n\r\n5 × 5 × 5 mm3
\r\n\r\nMoreover, a spherical water phantom was scanned using identical imaging protocols. Besides, T1-weighted structural images and B0 field map were acquired as well.
\r\n\r\nCode and same data are included as well.
\r\n","sample_size":12,"scanner_type":"Siemens 3T Trio system with a 12-channel head coil.","acknowledgements":"This study was supported in part by Ministry of Science and Technology (MOST 104- 2321-B-002-040, MOST 103-2320-B-008-001, and MOST 104-2221-E-008-123), Veterans General Hospitals University System of Taiwan Joint Research Program (VGHUST103-G1-4-3), the Chang Gung Memorial Hospital (BMRPC78) and Chang Gung University Research Project (UERPD2D0081).","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Physiological Contribution in Spontaneous Oscillations: An Approximate Quality-Assurance Index for Resting-State fMRI Signals.","url":"http://www.ncbi.nlm.nih.gov/pubmed/26871897"}],"task_set":[{"cogat_id":"trm_4c8a834779883","number":1,"name":"","url":"http://www.cognitiveatlas.org/id/trm_4c8a834779883"}],"revision_set":[{"revision_number":"1.0.1","notes":"- Corrected participants_id in participants.tsv\r\n- Added mriqc reports","date_set":"2017-10-31"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2016-03-04"}],"investigator_set":[{"investigator":"Chen, JH"},{"investigator":"Wu, CW"},{"investigator":"Fan, HY"},{"investigator":"Chao, YP"},{"investigator":"Chou, KH"},{"investigator":"Hsu, AL"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000172/ds000172_R1.0.1/uncompressed/derivatives/reports/bold_group.html","revision":"1.0.1"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000172/ds000172_R1.0.1/uncompressed/derivatives/reports/T1w_group.html","revision":"1.0.1"},{"title":"Data for all subjects ","url":"https://s3.amazonaws.com/openneuro/ds000172/ds000172_R1.0.1/compressed/ds000172_R1.0.1.zip","revision":null},{"title":"Download Dataset - ds172_R1.0.0","url":"http://openfmri.s3.amazonaws.com/tarballs/ds172_R1.0.0.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000114","project_name":"A test-retest fMRI dataset for motor, language and spatial attention functions.","summary":"A test-retest dataset was acquired to validate fMRI tasks used in pre-surgical planning. In particular, five task-related fMRI time series (finger, foot and lip movement, overt verb generation, covert verb generation, overt word repetition, and landmark tasks) were used to investigate which protocols gave reliable single-subject results. Ten healthy participants in their fifties were scanned twice using an identical protocol 2-3 days apart. In addition to the fMRI sessions, high-angular resolution diffusion tensor MRI (DTI), and high-resolution 3D T1-weighted volume scans were acquired.
\r\n","sample_size":10,"scanner_type":"GE Signa HDxt 1.5T","acknowledgements":"The study was funded by the Edinburgh Experimental Cancer Medicine Centre.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"A test-retest fMRI dataset for motor, language and spatial attention functions","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3641991/"}],"task_set":[],"revision_set":[{"revision_number":"2.0.1","notes":"- Deleted Dot files in the dataset","date_set":"2016-10-29"},{"revision_number":"1.0.0","notes":"-- Initial publishing","date_set":"2014-11-07"},{"revision_number":"2.0.0","notes":"-- Converted to BIDS ","date_set":"2016-08-19"}],"investigator_set":[{"investigator":"Pernet CR"},{"investigator":"Wardlaw JM"},{"investigator":"Whittle IR"},{"investigator":"Bastin ME"},{"investigator":"Storkey A"},{"investigator":"Gorgolewski KJ"}],"link_set":[{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000114_R2.0.1_raw.zip","revision":"2.0.1"},{"title":"Metadata on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds114_metadata.tgz","revision":"1.0.0"},{"title":"Raw data checksums","url":"http://openfmri.s3.amazonaws.com/tarballs/ds114_raw_checksums.txt","revision":"1.0.0"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds114_raw.tgz","revision":"1.0.0"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds114_R2.0.0_metadata.tgz","revision":"2.0.0"},{"title":"Data for All Subjects (1-10)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds114_R2.0.0_sub-01-10.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000101","project_name":"Simon task","summary":"The "NYU Simon Task" dataset comprises data collected from 21 healthy adults while they performed a rapid event-related Simon task. **Please note that all data have been uploaded regardless of quality- it is up to the user to check for data quality (movement etc).
\r\n\r\nOn each trial (inter-trial interval (ITI) was 2.5 seconds, with null events for jitter), a red or green box appeared on the right or left side of the screen. Participants used their left index finger to respond to the presentation of a green box, and their right index finger to respond to the presentation of a red box.In congruent trials the green box appeared on the left or the red box on the right, while in more demanding incongruent trials the green box appeared on the right and the red on the left.
\r\n\r\nSubjects performed two blocks, each containing 48 congruent and 48 incongruent trials, presented in a pre-determined order (as per OptSeq), interspersed with 24 null trials (fixation only).
\r\n\r\nFunctional imaging data were acquired using a research dedicated Siemens Allegra 3.0 T scanner, with a standard Siemens head coil, located at the NYU Center for Brain Imaging.
\r\n\r\nWe obtained 151 contiguous echo planar imaging (EPI) whole-brain functional volumes (TR=2000 ms; TE=30 ms; flip angle=80, 40 slices, matrix=64x64; FOV=192 mm; acquisition voxel size=3x3x4mm) during each of the two simon task blocks. A high-resolution T1-weighted anatomical image was also acquired using a magnetization prepared gradient echo sequence (MPRAGE, TR=2500 ms; TE= 3.93 ms; TI=900 ms; flip angle=8; 176 slices, FOV=256 mm).
\r\n\r\nThese data have not been published previously.
\r\n","sample_size":21,"scanner_type":"Siemens Allegra 3.0 T scanner","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"tsk_4a57abb949dbb","number":1,"name":"Simon task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949dbb"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2011-10-06"},{"revision_number":"2.0.0","notes":"Repackages in BIDS format (TRs in NIFTI header also corrected)","date_set":"2016-05-13"}],"investigator_set":[{"investigator":"Milham MP"},{"investigator":"Kelly AMC"}],"link_set":[{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000101/ds000101_R1.0.0/compressed/ds101_raw.tgz","revision":"1.0.0"},{"title":"Data for all subjects","url":"https://s3.amazonaws.com/openneuro/ds000101/ds000101_R2.0.0/compressed/ds101_R2.0.0_01-21.tgz","revision":"2.0.0"},{"title":"Metadata and derivatives","url":"https://s3.amazonaws.com/openneuro/ds000101/ds000101_R2.0.0/compressed/ds101_R2.0.0_metadata_derivatives.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000214","project_name":"EUPD Cyberball","summary":"Participants
\r\n\r\nTwenty people with borderline personality disorder were recruited from outpatient and support services from around Edinburgh, Scotland. Diagnoses were confirmed using the Structured Clinical Interview for DSM-IV (SCID-II). Current symptoms were assessed using the Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD). Adverse childhood events were assessed using the Childhood Trauma Questionnaire (CTQ). Fifteen BPD participants were receiving antidepressant medication and twelve were taking antipsychotic medication. Twenty age- and sex-matched controls were recruited from the community, however four were excluded due to technical issues during scanning, leaving sixteen controls. Exclusion criteria for all participants included pregnancy, MRI contraindications, diagnosis of a psychotic disorder, previous head injury or current illicit substance dependence. Controls met the additional criteria of no personal or familial history of major mental illness. Ethical approval was obtained from the Lothian National Health Service Research Ethics Committee, and all participants provided written informed consent before taking part.
\r\n\r\nExperimental task
\r\n\r\nParticipants performed the Cyberball social exclusion task during functional magnetic resonance imaging (fMRI), adapted from a previous implementation by Kumar et al 2009. The task involves playing “catch” with two computer-controlled players, during which the participant can be systematically included or excluded from the game. We used this task as it assesses neural responses to social exclusion, is known to activate a range of social brain regions and is amenable to reinforcement learning modelling. The task was modified such that inclusion was varied parametrically over four levels: 0%, 33%, 66% and 100%, achieved by arranging the task into blocks of nine throws, respectively involving zero, one, two or three throws to the participant. Here, 100% inclusion means the degree to which the participant was included was equal to that of the other two players, with each receiving three throws per nine-throw block. Participants were asked to imagine that the other players were real, as exclusion by both human or simulated players has been previously reported to be similarly distressing. When the participant received the ball, they indicated which computer player they wished to throw the ball to with a button press. There were four repetitions of each inclusion level, providing 16 experimental blocks in total, with the first block being 100% inclusion, and all subsequent blocks being randomised. Each throwing event had a mean duration of 2700ms, with each being preceded by randomised jitter that was in part adjusted to accommodate the participant’s reaction time from the previous trial, when applicable. This was achieved by comparing the total duration of the previous trial, including reaction time, with the ideal trial time of 2700ms: if this value was exceeded, a random jitter between 0 and 1000ms was subtracted from the mean jitter time of 1500ms; otherwise, the random jitter was added to 1500ms. Jitter therefore varied between 500ms and 2500ms. Mean block duration was 24s, with onsets denoted by the appearance of the cartoon figures following rest, and offsets by the conclusion of the final throw animation. Blocks were randomized, and interleaved with 13s rest blocks. Within blocks, throwing events were jittered to permit event disambiguation for reinforcement learning analysis.
\r\n","sample_size":36,"scanner_type":"3T Siemens Magnetom Verio scanner","acknowledgements":"The current study was supported by a Scottish Senior Clinical Fellowship to JH (SCD/10). We thank Stephen Giles for data curation and analysis support. We thank all the participants for their efforts in taking part. ","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Neural correlates of fears of abandonment and rejection in borderline personality disorder [version 1; referees: awaiting peer review]","url":"https://wellcomeopenresearch.org/articles/1-33/v1"}],"task_set":[{"cogat_id":"trm_4f24189031a4a","number":1,"name":"cyberball task","url":"http://www.cognitiveatlas.org/id/trm_4f24189031a4a"}],"revision_set":[{"revision_number":"1.0.0","notes":" - Initial release","date_set":"2017-01-21"}],"investigator_set":[{"investigator":"Jeremy Hall"},{"investigator":"Douglas Steele"},{"investigator":"Katie Nicol"},{"investigator":"Merrick Pope"},{"investigator":"Liana Romaniuk"}],"link_set":[{"title":"Data for all subjects (2.1 GB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000214/ds000214_R1.0.0/compressed/ds000214_R1.0.0_sub001-040.zip","revision":"1.0.0"},{"title":"Metadata (32 KB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000214/ds000214_R1.0.0/compressed/ds000214_R1.0.0_metadata.zip","revision":"1.0.0"}],"contacts":[{"email":"lianaromaniuk@gmail.com","name":"Liana Romaniuk","website":""}]},{"accession_number":"ds000240","project_name":"Resting State Perfusion in Healthy Aging","summary":"The present study combined structural and functional neuroimaging techniques to evaluate aging related brain changes. Changes in brain structure were assessed by means of high resolution T1-weighted anatomical imaging and voxel-based morphometry. Functional changes were measured using ASL fMRI and analyzed by a model-free approach, independent component analysis.
\r\n","sample_size":63,"scanner_type":"Siemenes 3T VB19","acknowledgements":"This work was supported by the Spanish Ministry of Economy and Competitiveness.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4c8a834779883","number":1,"name":"rest eyes open","url":"http://www.cognitiveatlas.org/id/trm_4c8a834779883"}],"revision_set":[{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2017-11-01"}],"investigator_set":[{"investigator":"Maria A. Fernandez-Seara"},{"investigator":"José L. Zubieta"},{"investigator":"Mario Riverol"},{"investigator":"Miriam Recio"},{"investigator":"Marta Vidorreta"},{"investigator":"Reyes Garcia de Eulate"},{"investigator":"Alvaro Galiano"}],"link_set":[{"title":"MRIQC T1w group report","url":"https://s3.amazonaws.com/openneuro/ds000240/ds000240_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for subject 01-63(1.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000240/ds000240_R1.0.0/compressed/ds000240_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"mfseara@unav.es","name":"María A. Fernandez Seara","website":""}]},{"accession_number":"ds000149","project_name":"Associative processing is inherent in scene perception","summary":"How are complex visual entities such as scenes represented in the human brain? More concretely, along what visual and semantic dimensions are scenes encoded in memory? One hypothesis is that global spatial properties provide a basis for categorizing the neural response patterns arising from scenes. In contrast, non-spatial properties, such as single objects, also account for variance in neural responses. The list of critical scene dimensions has continued to grow – sometimes in a contradictory manner – coming to encompass properties such as geometric layout, big/small, crowded/sparse, and three-dimensionality. We demonstrate that these dimensions may be better understood within the more general framework of associative properties. That is, across both the perceptual and semantic domains, features of scene representations are related to one another through learned associations. Critically, the components of such associations are consistent with the dimensions that are typically invoked to account for scene understanding and its neural bases. Using fMRI, we show that non-scene stimuli displaying novel associations across identities or locations recruit putatively scene-selective regions of the human brain (the parahippocampal/lingual region, the retrosplenial complex, and the transverse occipital sulcus/occipital place area). Moreover, we find that the voxel-wise neural patterns arising from these associations are significantly correlated with the neural patterns arising from everyday scenes providing critical evidence whether the same encoding principals underlie both types of processing. These neuroimaging results provide evidence for the hypothesis that the neural representation of scenes is better understood within the broader theoretical framework of associative processing. In addition, the results demonstrate a division of labor that arises across scene-selective regions when processing associations and scenes providing better understanding of the functional roles of each region within the cortical network that mediates scene processing.
\r\n","sample_size":15,"scanner_type":"3T Siemens Verio MR scanner","acknowledgements":"This research was supported by the Office of Naval Research MURI contract N000141010934 and by the National Science Foundation 1439237.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":false,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":null}],"investigator_set":[{"investigator":"Elissa M. Aminoff and Michael J. Tarr"}],"link_set":[{"title":"Raw data in AWS (uncurated)","url":"http://openfmri.s3.amazonaws.com/tarballs/uncurated/ds149_raw.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000246","project_name":"MEG-BIDS Brainstorm data sample","summary":"This dataset is a MEG-BIDS version of a tutorial dataset of Brainstorm, a free and open application for MEG data analysis (Tadel et al. 2011). This sample has been organized according to the specifications of MEG-BIDS to obtain feedback from the user community.
\r\n\r\nTadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM, “Brainstorm: A User-Friendly Application for MEG/EEG Analysis,” Computational Intelligence and Neuroscience, vol. 2011, Article ID 879716, 13 pages, 2011. doi:10.1155/2011/879716
\r\n","sample_size":1,"scanner_type":"MEG, CTF Inc. 275 channels","acknowledgements":"NIH (2R01EB009048-05)","license_title":"CC0","license_url":"https://creativecommons.org/publicdomain/zero/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Brainstorm: A User-Friendly Application for MEG/EEG Analysis","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3090754"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-11-09"}],"investigator_set":[{"investigator":"Sylvain Baillet"},{"investigator":"François Tadel"},{"investigator":"Jeremy Moreau"},{"investigator":"Elizabeth Bock"},{"investigator":"Guiomar Niso"}],"link_set":[{"title":"Full dataset (438 MB)","url":"https://s3.amazonaws.com/openneuro/ds000246/ds000246_R1.0.0/compressed/ds000246_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"sylvain.baillet@mcgill.ca","name":"Sylvain Baillet","website":""}]},{"accession_number":"ds000116","project_name":"Auditory and Visual Oddball EEG-fMRI","summary":"Healthy subjects performed separate but analogous auditory and visual oddball tasks (interleaved), while we recorded simultaneous EEG-fMRI.
\r\n","sample_size":17,"scanner_type":"3T Philips Achieva","acknowledgements":"This work was funded by National Institutes of Health Grant R01-MH085092 and by the National Science Foundation Graduate Research Fellowship Program. We thank Glenn Castillo and Stephen Dashnaw for their assistance with EEG–fMRI data acquisition.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Prestimulus EEG alpha oscillations modulate task-related fMRI BOLD responses to auditory stimuli.","url":"http://www.ncbi.nlm.nih.gov/pubmed/25797833"},{"title":"Fast bootstrapping and permutation testing for assessing reproducibility and interpretability of multivariate FMRI decoding models","url":"http://www.ncbi.nlm.nih.gov/pubmed/24244465"},{"title":"Simultaneous EEG-fMRI reveals a temporal cascade of task-related and default-mode activations during a simple target detection task","url":"http://www.ncbi.nlm.nih.gov/pubmed/23962956"},{"title":"Simultaneous EEG-fMRI Reveals Temporal Evolution of Coupling between Supramodal Cortical Attention Networks and the Brainstem","url":"http://www.ncbi.nlm.nih.gov/pubmed/24305817"}],"task_set":[{"cogat_id":"tsk_4a57abb949bf6","number":1,"name":"oddball task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949bf6"},{"cogat_id":"tsk_4a57abb949bf6","number":2,"name":"oddball task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949bf6"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2014-06-17"},{"revision_number":"2.0.0","notes":"- Converted to BIDS standard ","date_set":"2016-10-10"}],"investigator_set":[{"investigator":"Paul Sajda"},{"investigator":"Truman R Brown"},{"investigator":"Bryan Conroy"},{"investigator":"Jordan Muraskin"},{"investigator":"Robin I Goldman"},{"investigator":"Jennifer M Walz"}],"link_set":[{"title":"Processed data for Subject 17 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub017.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 16 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub016.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 15 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub015.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 14 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub014.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 13 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub013.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 12 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub012.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 11 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub011.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 10 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub010.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 9 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub009.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 8 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub008.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 7 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub007.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 6 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub006.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 5 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub005.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 4 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub004.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 3 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub003.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 2 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub002.tgz","revision":"1.0.0"},{"title":"Processed data for Subject 1 on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_sub001.tgz","revision":"1.0.0"},{"title":"Metadata: Task and Model Information","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_metadata.tgz","revision":"1.0.0"},{"title":"Raw data checksums","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_raw_checksums.txt","revision":"1.0.0"},{"title":"Raw data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds116_raw.tgz","revision":"1.0.0"},{"title":"Curated dataset","url":"http://openfmri.s3.amazonaws.com/tarballs/ds000116_R2.0.0_raw.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000202","project_name":"The heterogeneity in retrieved relations between the personality trait 'Harm avoidance' and gray matter volumes due to variations in the VBM and ROI labeling processing settings","summary":"In this study we tested the heterogeneity in obtained correlations between gray matter morphology and the personality trait 'Harm Avoidance' (HA).
\r\n\r\n95 healthy female volunteers (age: 18-30 years) were recruited to participate in the study. All underwent a T1 weighted anatomical scan of the head (TI/TR/TE=940.4/7.6/3.7ms, flip angle=8 degrees, FOV=240x240x200mm³, resolution=1x1x2mm³) at a Philips 3T Achieva MRI scanner. A score for all personality traits as defined in Cloninger’s psychobiological model of personality was obtained using the 'Revised Temperament and Character Inventory' (TCI).
\r\n","sample_size":95,"scanner_type":"Philips 3T Achieva","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":null,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"The Heterogeneity in Retrieved Relations between the Personality Trait ‘Harm Avoidance’ and Gray Matter Volumes Due to Variations in the VBM and ROI Labeling Processing Settings","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4838261/"}],"task_set":[],"revision_set":[{"revision_number":"1.0.2","notes":"Fixed missing ',' in dataset_description.json","date_set":"2016-01-27"},{"revision_number":"1.0.1","notes":"Updated TR parameters in dataset per submitter clarification.","date_set":"2016-01-05"},{"revision_number":"1.0.0","notes":"Initial Submission","date_set":"2016-01-02"}],"investigator_set":[{"investigator":"Johan De Mey"},{"investigator":"Chris Baeken"},{"investigator":"Peter Van Schuerbeek"}],"link_set":[{"title":"Download Dataset - ds202_R1.0.2","url":"http://openfmri.s3.amazonaws.com/tarballs/ds202_R1.0.2.tgz","revision":"1.0.2"},{"title":"Download Dataset - ds202_R1.0.1","url":"http://openfmri.s3.amazonaws.com/tarballs/ds202_R1.0.1.tgz","revision":"1.0.1"},{"title":"Download Dataset - ds202_R1.0.0","url":"http://openfmri.s3.amazonaws.com/tarballs/ds202_R1.0.0.tgz","revision":"1.0.0"}],"contacts":[{"email":"Peter.VanSchuerbeek@uzbrussel.be","name":"Peter Van Schuerbeek","website":""}]},{"accession_number":"ds000144","project_name":"Preschool Anxiety Disorders","summary":"In this prospective, longitudinal study of young children, we examined whether a history of preschool generalized anxiety, separation anxiety, and/or social phobia is associated with amygdala-prefrontal dysregulation at school-age. As an exploratory analysis, we investigated whether distinct anxiety disorders differ in the patterns of this amygdala-prefrontal dysregulation.
\r\nParticipants were children taking part in a 5-year study of early childhood brain development and anxiety disorders. Preschool symptoms of generalized anxiety, separation anxiety, and social phobia were assessed with the Preschool Age Psychiatric Assessment (PAPA) in the first wave of the study when the children were between 2 and 5 years old. The PAPA was repeated at age 6. We conducted functional MRIs when the children were 5.5 to 9.5 year old to assess neural responses to viewing of angry and fearful faces.
\r\nA history of preschool social phobia predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces. Preschool generalized anxiety predicted less functional connectivity between the amygdala and dorsal prefrontal cortices in response to fearful faces. Finally, a history of preschool separation anxiety predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces and greater school-age functional connectivity between the amygdala and dorsal prefrontal cortices to angry faces.
\r\nOur results suggest that there are enduring neurobiological effects associated with a history of preschool anxiety, which occur over-and-above the effect of subsequent emotional symptoms. Our results also provide preliminary evidence for the neurobiological differentiation of specific preschool anxiety disorders.
\r\nCognitive self-regulation can strongly modulate pain and emotion. However, it is unclear whether self-regulation primarily influences primary nociceptive and affective processes or evaluative ones. In this study, participants engaged in self-regulation to increase or decrease pain while experiencing multiple levels of painful heat during fMRI imaging. Both heat intensity and self-regulation strongly influenced reported pain, but they did so via two distinct brain pathways. The effects of stimulus intensity were mediated by the Neurologic Pain Signature (NPS), an a priori distributed brain network shown to predict physical pain with over 90% sensitivity and specificity across four studies. Self-regulation did not influence NPS responses; instead, its effects were mediated through functional connections between the nucleus accumbens and ventromedial prefrontal cortex. This pathway was unresponsive to noxious input, and has been broadly implicated in valuation, emotional appraisal, and functional outcomes in pain and other types of affect. These findings provide evidence that pain reports are associated with two dissociable functional systems: nociceptive/affective aspects mediated by the NPS, and evaluative/functional aspects mediated by a fronto-striatal system.
\r\n","sample_size":33,"scanner_type":"3T Philips Achieva TX scanner","acknowledgements":"Thanks to Asa Pingree and Namema Amendi for help with data collection. This work was funded by R01 DA027794 and R01 MH076136 (TDW), by a Fulbright Graduate Study Fellowship to CW, and by a post-doctoral scholarship from the Canadian Institutes of Health Research grant (CIHR) to MR. Matlab code implementing all analyses is available at http://wagerlab.colorado.edu/tools.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Distinct Brain Systems Mediate the Effects of Nociceptive Input and Self-Regulation on Pain","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4285399/"}],"task_set":[{"cogat_id":"trm_565a31fa6f444","number":1,"name":"regulated heat stimulation","url":"http://www.cognitiveatlas.org/id/trm_565a31fa6f444"}],"revision_set":[{"revision_number":"2.0.0","notes":" - Converted to BIDS format\r\n - Curated dataset\r\n - Incorporated regulate-up and regulate-down order in events files","date_set":"2016-12-13"}],"investigator_set":[{"investigator":"Tor D. Wager"},{"investigator":"Jason T. Buhle"},{"investigator":"Mathieu Roy"},{"investigator":"Choong-Wan Woo"}],"link_set":[{"title":"Metadata (8 KB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000140/ds000140_R2.0.0/compressed/ds000140_R2.0.0_metadata.zip","revision":"2.0.0"},{"title":"Data for subjects 27-33 (2.9 GB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000140/ds000140_R2.0.0/compressed/ds000140_R2.0.0_sub27-33.zip","revision":"2.0.0"},{"title":"Data for subjects 18-26 (3.7 GB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000140/ds000140_R2.0.0/compressed/ds000140_R2.0.0_sub18-26.zip","revision":"2.0.0"},{"title":"Data for subjects 09-17 (3.7 GB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000140/ds000140_R2.0.0/compressed/ds000140_R2.0.0_sub09-17.zip","revision":"2.0.0"},{"title":"Data for subjects 01-08 (3.3 GB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000140/ds000140_R2.0.0/compressed/ds000140_R2.0.0_sub01-08.zip","revision":"2.0.0"},{"title":"Groupdata (3.2 GB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000140/ds000140_R2.0.0/compressed/ds000140_R2.0.0_groupdata.zip","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000168","project_name":"Offline Processing in Associative Learning","summary":"This is structural and functional MRI data from 35 healthy volunteers that accompanies Bursley et al. (2015), "Awake, Offline Processing During Associative Learning." Subjects encoded paired associates and then performed a distractor task before being probed on associate pairs. Pattern analyses suggest that encoded memories were reactivated during the distractor task, and performance of the distractor task led to superior recall for the associate pairs, compared to a control condition in which no distractor task was performed.
\r\n\r\nIn this dataset: High-resolution T1-weighted structural and BOLD contrast fMRI scans
\r\n","sample_size":35,"scanner_type":"Siemens 3T MAGNETOM Verio","acknowledgements":"This project was supported by a Rothberg Research Award in Human Brain Imaging awarded to J.K.B. and J.D.C.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4da88a2a63d97","number":1,"name":"paired associate learning","url":"http://www.cognitiveatlas.org/id/trm_4da88a2a63d97"}],"revision_set":[{"revision_number":"1.0.1","notes":"- Corrected T1w.json lint","date_set":"2017-09-16"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2016-04-21"}],"investigator_set":[{"investigator":"J. David Creswell"},{"investigator":"Michael J. Tarr"},{"investigator":"Adrian Nestor"},{"investigator":"James K. Bursley"}],"link_set":[{"title":"Data for subjects 01-49 ","url":"https://s3.amazonaws.com/openneuro/ds000168/ds000168_R1.0.1/compressed/ds000168_R1.0.1_sub01-49.zip","revision":"1.0.1"},{"title":"Metadata and Derivatives","url":"https://s3.amazonaws.com/openneuro/ds000168/ds000168_R1.0.1/compressed/ds000168_R1.0.1_metadata_derivatives.zip","revision":"1.0.1"},{"title":"Raw data for all subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds168_R1.0.0_01-49.tgz","revision":"1.0.0"},{"title":"Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds168_R1.0.0_metadata_derivatives.tgz","revision":"1.0.0"}],"contacts":[{"email":"bursley@fas.harvard.edu","name":"James K. Bursley","website":""}]},{"accession_number":"ds000133","project_name":"Modafinil alters intrinsic functional connectivity of the right posterior insula: a pharmacological resting state fMRI study","summary":"Modafinil is employed for the treatment of narcolepsy and has also been, off-label, used to treat cognitive dysfunction in neuropsychiatric disorders. In a previous study, we have reported that single dose administration of modafinil in healthy young subjects enhances fluid reasoning and affects resting state activity in the Fronto Parietal Control (FPC) and Dorsal Attention (DAN) networks. No changes were found in the Salience Network (SN), a surprising result as the network is involved in the modulation of emotional and fluid reasoning. The insula is crucial hub of the SN and functionally divided in anterior and posterior subregions. Using a seed-based approach, we have now analyzed effects of modafinil on the functional connectivity (FC) of insular subregions. Analysis of FC with resting state fMRI (rs-FMRI) revealed increased FC between the right posterior insula and the putamen, the superior frontal gyrus and the anterior cingulate cortex in the modafinil-treated group.Modafinil is considered a putative cognitive enhancer. The rs-fMRI modifications that we have found are consistent with the drug cognitive enhancing properties and indicate subregional targets of action.
\r\n\r\nDataset includes: BOLD contrast fMRI and high-resolution T1-weighted structural scans.
\r\n","sample_size":26,"scanner_type":"Philips Achieva 3T","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Modafinil alters intrinsic functional connectivity of the right posterior insula: a pharmacological resting state fMRI study.","url":"http://www.ncbi.nlm.nih.gov/pubmed/25237810"}],"task_set":[{"cogat_id":"trm_4c8a834779883","number":1,"name":"rest eyes open","url":"http://www.cognitiveatlas.org/id/trm_4c8a834779883"}],"revision_set":[{"revision_number":"1.0.0","notes":"Initial release","date_set":"2016-04-11"}],"investigator_set":[{"investigator":"Stefano L. Sensi"},{"investigator":"Armando Tartaro"},{"investigator":"Nicoletta Cera"}],"link_set":[{"title":"Raw data for all subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds133_R1.0.0_01-26.tgz","revision":"1.0.0"},{"title":"Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds133_R1.0.0_metadata_derivatives.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000170","project_name":"Learning and memory: motor skill consolidation and intermanual transfer","summary":"Participants were scanned during the performance of a finger-to-thumb opposition sequence (5-element), intensively trained a day earlier, and a similarly constructed, but novel, untrained sequence, using either their trained (left) or untrained hand. The imaging session consisted of 3 consecutive runs for each sequence and hand: Th – trained (left) hand, Uh – untrained (right) hand, T – trained sequence, U – untrained sequence. Experimental runs (each 144 seconds long) were separated by a 1.5 – 2 minutes break that included a verbal interaction with the participant. Each run consisted of two performance blocks (8 repetitions of a sequence paced by an auditory signal at a fixed rate of 1.66 Hz, during 24 sec each) separated by a rest interval of 30 seconds. Each run began and ended with a rest period of 36 seconds and 24 seconds, respectively.
\r\n\r\nFunctional magnetic resonance imaging (fMRI) scanning was carried out at the C. Sheba Medical Center, Tel-Hashomer, using a 3 Tesla whole body high definition system (GE EXCITE 3 HD) equipped with an 8-channel head coil. A high-resolution full-brain 3D structural images were acquired in the axial orientation using a T1*-weighted echo-planar sequence (TR = 7.3 ms, TE = 3 ms, flip angle = 20°, FOV = 256 x 256 mm2, matrix size = 256 x 256 voxels, voxel size = 1 x 1 x 1 mm3). BOLD-sensitive functional images were obtained using a gradient echo-planar T2*-sequence (TR = 3000 ms, TE = 35 ms, flip angle=90°, FOV = 220 x 220 mm2, matrix size = 64 x 64 voxels, voxel size = 3.4 x 3.4 x 3.4 mm3, no gap, ascending) with 40 axial oblique slices, covering the whole brain.
\r\n\r\nDataset contains: BOLD contrast fMRI and high resolution T1-weighted structural images.
\r\n","sample_size":15,"scanner_type":"A 3 Tesla whole body high definition system (GE EXCITE 3 HD) equipped with an 8-channel head coil.","acknowledgements":"E.G. was partially supported by a fellowship from the E. J. Safra Brain Research Center for the Study of Learning Disabilities. The research leading to these results has received funding from the European Union Seventh Framework Program (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project). We thank Tammi Kushnir, Ph.D., director of research at the C. Sheba Medical Center, for her technical and administrative support.\r\n","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Done that: short-term repetition related modulations of motor cortex activity as a stable signature for overnight motor memory consolidation.","url":"http://www.ncbi.nlm.nih.gov/pubmed/24893741"},{"title":"Patterns of modulation in the activity and connectivity of motor cortex during the repeated generation of movement sequences.","url":"http://www.ncbi.nlm.nih.gov/pubmed/25390206"},{"title":"Learning from the other limb's experience: sharing the 'trained' M1 representation of the motor sequence knowledge.","url":"http://www.ncbi.nlm.nih.gov/pubmed/26442464"}],"task_set":[{"cogat_id":"trm_4c898f079d05e","number":1,"name":"","url":"http://www.cognitiveatlas.org/id/trm_4c898f079d05e"}],"revision_set":[{"revision_number":"1.0.1","notes":"Added *_events.tsv files containing the onsets, durations, and condition names for the fMRI task. These were previously omitted due to an error in the curation process. For those interested in downloading only the event onsets file, it is included separately (as a single file) in the Links section. No changes were made to any of the other imaging data files.","date_set":"2016-04-16"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2016-02-25"}],"investigator_set":[{"investigator":"Avi Karni "},{"investigator":"David Manor "},{"investigator":"Ella Gabitov"}],"link_set":[{"title":"Previously omitted fMRI task events file (onset times in seconds)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds170_R1.0.1_FOS_cond.tsv","revision":"1.0.1"},{"title":"Dataset metadata and derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds170_R1.0.1_metadata.tgz","revision":"1.0.1"},{"title":"Imaging data only for all subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds170_R1.0.1_1700-1719.tgz","revision":"1.0.1"},{"title":"Dataset metadata and derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds170_R1.0.0_metadata.tgz","revision":"1.0.0"},{"title":"Imaging data only for all subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds170_R1.0.0_1700-1719.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000164","project_name":"Stroop task","summary":"Participants performed the color-word version of the Stroop task with three conditions (congruent, incongruent, and neutral) while in the MR scanner. Participants were instructed to ignore the meaning of the printed word and respond to the ink color in which the word was printed. Each condition was meant to elicit a certain level of attentional demand. Participants responded to ink color by pressing button under the index, middle, and ringe fingers on their right hand. One button for each color (red, green, and blue) on an MR-safe response box. Task begins with a 1,000ms fixation cross followed by the stroop stimulus for 2,000ms. The interstimulus interval between successive trial starts was sampled from an exponential distribution, between 3 and 20 s with a mean of 4 s and a median of 3 s, in order to ensure accurate deconvolution of the hemodynamic response. Conditions were pseudorandomized in an event related fashion. 120 trials were presented to each participant. Stimuli were back projected on a screen located at the back of the MR bore with an MR safe projector. Participants used a mirror attached at the top of the head coil to view. This study using response time as a behavioral variable.
\r\n","sample_size":28,"scanner_type":"Siemen's Verio 3T with 32 channel head coil","acknowledgements":"The author thanks Daniel Weissman and Jean Vettel for their helpful\r\ncomments on the initial phases of this study, Kirk Erickson for supplying the experimental task used in this study, and Kevin Jarbo, Patrick Buekema, David Creswell, and Fang-Cheng Yeh for their critiques on early versions of the manuscript. \r\nThis research was sponsored in part by Pennsylvania Department of Health\r\nFormula Award SAP4100062201 and by the Army Research Laboratory under Cooperative Agreement Number W911NF-10-2-0022. ","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"The organization and dynamics of corticostriatal pathways link the medial orbitofrontal cortex to future behavioral responses","url":"http://www.ncbi.nlm.nih.gov/pubmed/25143543"}],"task_set":[{"cogat_id":"tsk_4a57abb949e27","number":1,"name":"Stroop task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949e27"}],"revision_set":[{"revision_number":"1.0.1","notes":"- Clarified on voxel size for functional files in README\r\n- MRIQC","date_set":"2017-05-17"},{"revision_number":"1.0.0","notes":" - Initial release","date_set":"2016-12-12"}],"investigator_set":[{"investigator":"Timothy D. Verstynen"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000164/ds000164_R1.0.1/uncompressed/derivatives/mriqc/bold_group.html","revision":"1.0.1"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000164/ds000164_R1.0.1/uncompressed/derivatives/mriqc/T1w_group.html","revision":"1.0.1"},{"title":"MRIQC","url":"https://s3.amazonaws.com/openneuro/ds000164/ds000164_R1.0.1/compressed/ds000164_R1.0.1_MRIQC.zip","revision":"1.0.1"},{"title":"Metadata","url":"https://s3.amazonaws.com/openneuro/ds000164/ds000164_R1.0.1/compressed/ds000164_R1.0.1_metadata.zip","revision":"1.0.1"},{"title":"Data for Subjects 001-028 (1.6GB)","url":"https://s3.amazonaws.com/openneuro/ds000164/ds000164_R1.0.1/compressed/ds000164_R1.0.1_sub001-028.zip","revision":"1.0.1"},{"title":"Data for all subjects (1.5 GB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000164/ds000164_R1.0.0/compressed/ds000164_R1.0.0_sub001-028.zip","revision":"1.0.0"},{"title":"Metadata (3 KB)","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000164/ds000164_R1.0.0/compressed/ds000164_R1.0.0_metadata.zip","revision":"1.0.0"}],"contacts":[{"email":"timothyv@gmail.com","name":"Timothy D. Verstynen","website":""}]},{"accession_number":"ds000255","project_name":"Visual image reconstruction","summary":"In this study, we reconstructed visual images by combining local image bases of multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant vox- els and exploiting their correlated patterns. Binary- contrast, 10 × 10-patch images (2^100 possible states) were accurately reconstructed without any image prior on a single trial or volume basis by measuring brain activity only for several hundred random images. Reconstruction was also used to identify the presented image among millions of candidates.
\r\n","sample_size":2,"scanner_type":"Siemens MAGNETOM Trio A Tim 3T","acknowledgements":"The authors thank M. Kawato and K. Toyama for helpful comments; A. Harner and S. Murata for technical assistance; and T. Beck and Y. Yamada for man- uscript editing. This research was supported in part by the SRPBS, MEXT, the NICT-KARC, the Nissan Science Foundation, and the SCOPE, SOUMU.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Visual image reconstruction from human brain activity using a combination of multiscale local image decoders.","url":"https://www.ncbi.nlm.nih.gov/pubmed/19081384"}],"task_set":[{"cogat_id":"trm_4c8a84f20dde2","number":1,"name":"visual attention task","url":"http://www.cognitiveatlas.org/id/trm_4c8a84f20dde2"},{"cogat_id":"trm_4c8a84f20dde2","number":2,"name":"visual attention task","url":"http://www.cognitiveatlas.org/id/trm_4c8a84f20dde2"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2018-01-10"}],"investigator_set":[{"investigator":"Yukiyasu Kamitani"},{"investigator":"Norihiro Sadato"},{"investigator":"Hiroki C. Tanabe"},{"investigator":" Yusuke Morito"},{"investigator":"Masa-aki Sato"},{"investigator":"Okito Yamashita"},{"investigator":"Hajime Uchida"},{"investigator":"Yoichi Miyawaki"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000255/ds000255_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000255/ds000255_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for all subjects (1.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000255/ds000255_R1.0.0/compressed/ds000255_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"aoki@atr.jp","name":"Shuntaro C. Aoki","website":""}]},{"accession_number":"ds000053","project_name":"Training of loss aversion modulates neural sensitivity toward potential gains","summary":"We investigated behavioral and neural mechanisms for modulating loss aversion.
\r\n\r\nBehavior task: We adapted the gambling task (Tom et al., 2007) by introducing contexts and feedback that encourage participants to take more or less loss averse choices.
\r\n\r\nfMRI: We used general linear model to find brain activation that correlates with magnitude of potential gains or potential losses during the learning and post-learning probe. We also used psychophysiological interaction analysis (independent seeded at vmPFC) to identified the brain areas showing interaction with vmPFC over the course of training.
\r\n\r\nTraining primarily modulated behavioral and neural sensitivity toward potential gains, and was reflected in connectivity between regions involved in cognitive control and those involved in value representation. These findings highlight the importance of experience in development of biases in decision-making.
\r\n\r\nSixty human participants completed the behavioral paradigm in the MRI scanner (31 females, 29 males; age range: 18 - 30 with mean 22.9-year-old). Two participants were discarded from the brain imaging analyses; one due to a missing anatomical image, and the other due to excessive head movement (more than one-third of the volumes were considered “bad time points” according to the motion correction procedures detailed in the Preprocessing section).
\r\n","sample_size":60,"scanner_type":"Siemens Skyra 3T","acknowledgements":"This work was supported by a James S. McDonnell Foundation grant to R. A. P. and the Taiwanese National Graduate Scholarship to M.-Y. C.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":false,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4f24496a80587","number":1,"name":"gambling task","url":"http://www.cognitiveatlas.org/id/trm_4f24496a80587"}],"revision_set":[{"revision_number":"1.0.2","notes":"- Ran jsonconsolidator\r\n- Converted slice timings from millisec to seconds","date_set":"2017-12-04"},{"revision_number":"1.0.1","notes":"- Corrected tarballs.","date_set":"2017-08-17"},{"revision_number":"1.0.0","notes":"-- Initial release","date_set":"2017-05-17"}],"investigator_set":[{"investigator":"Russell A. Poldrack"},{"investigator":"W. Todd Maddox"},{"investigator":"Ungi Kim"},{"investigator":"Sagar Parikh"},{"investigator":"Albert Elumn"},{"investigator":"Nathan Giles"},{"investigator":"Corey N. White"},{"investigator":"Mei-Yen Chen"}],"link_set":[{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/uncompressed/derivatives/mriqc/T1w_group.html","revision":"1.0.2"},{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/uncompressed/derivatives/mriqc/bold_group.html","revision":"1.0.2"},{"title":"Derivatives and MRIQC ","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_derivatives.zip","revision":"1.0.2"},{"title":"Metadata","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_metadata.zip","revision":"1.0.2"},{"title":"Data for Subjects 059-060","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_sub059-060.zip","revision":"1.0.2"},{"title":"Data for Subjects 056-058","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_sub056-058.zip","revision":"1.0.2"},{"title":"Data for Subjects 052-054","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_sub052-054.zip","revision":"1.0.2"},{"title":"Data for Subjects 049-051","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_sub049-051.zip","revision":"1.0.2"},{"title":"Data for Subjects 046-048","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_sub046-048.zip","revision":"1.0.2"},{"title":"Data for Subjects 043-045","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_sub043-045.zip","revision":"1.0.2"},{"title":"Data for Subjects 040-042","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.2/compressed/ds000053_R1.0.2_sub040-042.zip","revision":"1.0.2"},{"title":"Data for 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report","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/uncompressed/derivatives/mriqc/T1w_group.html","revision":"1.0.0"},{"title":"MRIQC","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_MRIQC.zip","revision":"1.0.0"},{"title":"Metadata","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_metadata.zip","revision":"1.0.0"},{"title":"Data for Subjects 025-027(8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub025-027.zip","revision":"1.0.0"},{"title":"Data for Subjects 001-003(8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub001-003.zip","revision":"1.0.0"},{"title":"Data for Subjects 059-060 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub059-060.zip","revision":"1.0.0"},{"title":"Data for Subjects 056-058 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub056-058.zip","revision":"1.0.0"},{"title":"Data for Subjects 052-054 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub052-054.zip","revision":"1.0.0"},{"title":"Data for Subjects 049-051 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub049-051.zip","revision":"1.0.0"},{"title":"Data for Subjects 046-048 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub046-048.zip","revision":"1.0.0"},{"title":"Data for Subjects 043-045 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub043-045.zip","revision":"1.0.0"},{"title":"Data for Subjects 040-042 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub040-042.zip","revision":"1.0.0"},{"title":"Data for Subjects 037-039 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub037-039.zip","revision":"1.0.0"},{"title":"Data for Subjects 034-036 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub034-036.zip","revision":"1.0.0"},{"title":"Data for Subjects 031-033 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub031-033.zip","revision":"1.0.0"},{"title":"Data for Subjects 028-030 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub028-030.zip","revision":"1.0.0"},{"title":"Data for Subjects 022-024 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub022-024.zip","revision":"1.0.0"},{"title":"Data for Subjects 019-021 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub019-021.zip","revision":"1.0.0"},{"title":"Data for Subjects 016-018 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub016-018.zip","revision":"1.0.0"},{"title":"Data for Subjects 013-015 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub013-015.zip","revision":"1.0.0"},{"title":"Data for Subjects 010-012 (8.8GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub010-012.zip","revision":"1.0.0"},{"title":"Data for Subjects 007-009 (8.7 GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub007-009.zip","revision":"1.0.0"},{"title":"Data for Subjects 004-006 (8.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000053/ds000053_R1.0.0/compressed/ds000053_R1.0.0_sub004-006.zip","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000113c","project_name":"Multi-resolution 7T fMRI data on the representation of visual orientation","summary":"This dataset consists of empirical ultra high-field fMRI data recorded at four
\r\nspatial resolutions (0.8 mm, 1.4 mm, 2 mm, and 3 mm isotropic voxel size) for
\r\norientation decoding in visual cortex — in order to test hypotheses on the
\r\nstrength and spatial scale of orientation discriminating signals. This is an
\r\nextension of the studyforrest project. All seven participants previously
\r\nvolunteered for the audio-only and the audio-visual Forrest Gump study. The
\r\ndataset is compliant with the BIDS data description standard
\r\n(http://bids.neuroimaging.io). A detailed description can be found in:
Sengupta, A., Yakupov, R., Speck, O., Pollmann, S., Hanke, M. Ultra
\r\nHigh-Field (7 Tesla) multi-resolution fMRI data on the representation
\r\nof visual orientation. Data in Brief (submitted)
For more information about the project visit: http://studyforrest.org
\r\n\r\nThis dataset may also be accessed using git/git-annex. Please refer to the github project page: studyforrest-data-multires7t for more information.
\r\n","sample_size":7,"scanner_type":"Siemens 7T","acknowledgements":"Only open-source research software was employed in this study. We thank their respective authors for making it publicly available. Please follow good scientific practice by citing the associated data descriptor: Sengupta et al. (2016), Data in Brief\r\n\r\n\r\nThis research was supported by a grant from the German Research Foundation (DFG) awarded to S. Pollmann and O. Speck (DFG PO 548/15-1). This research was, in part, also supported by the German Federal Ministry of Education and Research (BMBF) as part of a US-German collaboration in computational neuroscience (CRCNS), co-funded by the BMBF and the US National Science Foundation (BMBF 01GQ1112; NSF 1129855). Work on the data-sharing technology employed for this research was supported by US-German CRCNS project, co-funded by the BMBF and the US National Science Foundation (BMBF 01GQ1411; NSF 1429999).","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"Initial Publishing. ","date_set":"2016-05-20"}],"investigator_set":[{"investigator":"Michael Hanke"},{"investigator":"Stefan Pollmann"},{"investigator":"Oliver Speck"},{"investigator":"Renat Yakupov"},{"investigator":"Ayan Sengupta"}],"link_set":[{"title":"Experimental data for subject sub-21","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113c_R1.0.0_sub-21.tgz","revision":"1.0.0"},{"title":"Experimental data for subject sub-20","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113c_R1.0.0_sub-20.tgz","revision":"1.0.0"},{"title":"Experimental data for subject sub-18","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113c_R1.0.0_sub-18.tgz","revision":"1.0.0"},{"title":"Experimental data for subject sub-17","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113c_R1.0.0_sub-17.tgz","revision":"1.0.0"},{"title":"Experimental data for subject sub-16","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113c_R1.0.0_sub-16.tgz","revision":"1.0.0"},{"title":"Experimental data for subject sub-06","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113c_R1.0.0_sub-06.tgz","revision":"1.0.0"},{"title":"Experimental data for subject sub-04","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113c_R1.0.0_sub-04.tgz","revision":"1.0.0"},{"title":"Metadata and top-level files","url":"http://openfmri.s3.amazonaws.com/tarballs/ds113c_R1.0.0_metadata.tar.gz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000138","project_name":"Spinal fMRI reveals decreased descending inhibition during secondary mechanical hyperalgesia","summary":"Mechanical hyperalgesia is one distressing symptom of neuropathic pain which is explained by central sensitization of the nociceptive system. This sensitization can be induced experimentally with the heat/capsaicin sensitization model. The aim was to investigate and compare spinal and supraspinal activation patterns of identical mechanical stimulation before and after sensitization using functional spinal magnetic resonance imaging (spinal fMRI).
\r\n\r\nSixteen healthy subjects (6 female, 10 male, mean age 27.2 ± 4.0 years) were investigated with mechanical stimulation of the C6 dermatome of the right forearm during spinal fMRI. Testing was always performed in the area outside of capsaicin application (i.e. area of secondary mechanical hyperalgesia).
\r\n\r\nDuring slightly noxious mechanical stimulation before sensitization, activity was observed in ipsilateral dorsolateral pontine tegmentum (DLPT) which correlated with activity in ipsilateral spinal cord dorsal gray matter (dGM) suggesting activation of descending nociceptive inhibition. During secondary mechanical hyperalgesia, decreased activity was observed in bilateral DLPT, ipsilateral/midline rostral ventromedial medulla (RVM), and contralateral subnucleus reticularis dorsalis, which correlated with activity in ipsilateral dGM. Comparison of voxel-based activation patterns during mechanical stimulation before/after sensitization showed deactivations in RVM and activations in superficial ipsilateral dGM.
\r\n\r\nThis study revealed increased spinal activity and decreased activity in supraspinal centers involved in pain modulation (SRD, RVM, DLPT) during secondary mechanical hyperalgesia suggesting facilitation of nociception via decreased endogenous inhibition. Results should help prioritize approaches for further in vivo studies on pain processing and modulation in humans.
\r\nWe investigated the neural basis of body image processing in overweight and average weight young women to understand whether brain regions that were previously found to be involved in processing self-reflective, perspective and affective components of body image would show different activation between two groups.
\r\n","sample_size":26,"scanner_type":"3T Siemens TRIO MRI scanner","acknowledgements":"This research was supported by Chinese National Natural Science Foundation (31100758) and Central Universities Fundamental Research Funds SWU1409116) grants to Xiao Gao, as well as Chongqing Health Bureau Foundation (2012-2-135) to Xiao Deng and the Chinese National Natural\r\nScience Foundation (31170981) to Hong Chen.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"My Body Looks Like That Girl's: Body Mass Index Modulates Brain Activity during Body Image Self-Reflection among Young Women.","url":"https://www.ncbi.nlm.nih.gov/pubmed/27764116"}],"task_set":[{"cogat_id":"trm_5879199fde201","number":1,"name":"body image self-reflection task","url":"http://www.cognitiveatlas.org/id/trm_5879199fde201"}],"revision_set":[{"revision_number":"1.0.2","notes":"- Added mriqc result\r\n - corrected funding field in dataset_description to string","date_set":"2017-09-21"},{"revision_number":"1.0.0","notes":" - Initial release","date_set":"2017-02-02"}],"investigator_set":[{"investigator":"Xiao Gao"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000213/ds000213_R1.0.2/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.2"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000213/ds000213_R1.0.2/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.2"},{"title":"Data for all subjects ","url":"https://s3.amazonaws.com/openneuro/ds000213/ds000213_R1.0.2/compressed/ds000213_R1.0.2.zip","revision":"1.0.2"},{"title":"Data for all subjects (1.7 GB)","url":"https://s3.amazonaws.com/openneuro/ds000213/ds000213_R1.0.0/compressed/ds000213_R1.0.0_sub01-28.zip","revision":"1.0.0"},{"title":"Metadata (3.8 KB)","url":"https://s3.amazonaws.com/openneuro/ds000213/ds000213_R1.0.0/compressed/ds000213_R1.0.0_metadata.zip","revision":"1.0.0"}],"contacts":[{"email":"sheying5201314@qq.com","name":"Ying She","website":""}]},{"accession_number":"ds000217","project_name":"Route Learning","summary":"Subjects were scanned while learning 2 pairs of overlapping routes on the NYU campus. Subjects completed 14 rounds of route learning during which they viewed repetitions of the routes and learned to predict the destination of each. To encourage learning catch trials were included during which the route would stop in the middle and subjects would be asked to either identify 1) the final destination of that route or 2) the direction of the next turn in the route (right/left). After exiting the scanner subjects completed a picture test in which they were shown still images selected from the routes and were asked to identify the destination associated with each image. Two independent experiments were performed using different sets of routes.
\r\n\r\n\r\n\r\n
Link to a sample task video (also present in dataset): https://www.youtube.com/watch?v=vqp2m2Lx49k
\r\n","sample_size":41,"scanner_type":"Siemens Allegra 3T","acknowledgements":"We thank Anthony Stigliani and Kalanit Grill-Spector for providing stimuli for the category localizer. This work was supported by a grant from the National Institutes of Health (1RO1NS089729) to B.A.K.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Overlap among spatial memories triggers divergence of hippocampal representations","url":"http://biorxiv.org/content/early/2017/01/10/099226"}],"task_set":[{"cogat_id":"trm_553e85265f51e","number":1,"name":"functional localizer fMRI tasks","url":"http://www.cognitiveatlas.org/id/trm_553e85265f51e"},{"cogat_id":"trm_58ab8a6131c5a","number":2,"name":"route learning","url":"http://www.cognitiveatlas.org/id/trm_58ab8a6131c5a"}],"revision_set":[{"revision_number":"1.0.1","notes":" - Re-defaced sub-Exp1s08/anat/sub-Exp1s08_T1w.nii.gz","date_set":"2017-03-04"},{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-02-28"}],"investigator_set":[{"investigator":"Brice A Kuhl"},{"investigator":"Serra E Favila"},{"investigator":"Ashima Oza"},{"investigator":"Avi JH Chanales"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/uncompressed/derivatives/mriqc/reports/func_group.html","revision":"1.0.1"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/uncompressed/derivatives/mriqc/reports/anat_group.html","revision":"1.0.1"},{"title":"MRIQC (1.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_mriqc.zip","revision":"1.0.1"},{"title":"Metadata (6.3 KB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_metadata.zip","revision":"1.0.1"},{"title":"Data for subjects Exp2s15-Exp2s21 (17.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_subExp2s15-Exp2s21.zip","revision":"1.0.1"},{"title":"Data for subjects Exp2s08-Exp2s14 (17.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_subExp2s08-Exp2s14.zip","revision":"1.0.1"},{"title":"Data for subjects Exp2s01-Exp2s07 (17.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_subExp2s01-Exp2s07.zip","revision":"1.0.1"},{"title":"Data for subjects Exp1s15-Exp1s20 (14.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_subExp1s15-Exp1s20.zip","revision":"1.0.1"},{"title":"Data for subjects Exp1s08-Exp1s14 (17.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_subExp1s08-Exp1s14.zip","revision":"1.0.1"},{"title":"Data for subjects Exp1s01-Exp1s07 (17.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_subExp1s01-Exp1s07.zip","revision":"1.0.1"},{"title":"Stimuli (235.4 MB)","url":"https://s3.amazonaws.com/openneuro/ds000217/ds000217_R1.0.1/compressed/ds000217_R1.0.1_stimuli.zip","revision":"1.0.1"}],"contacts":[{"email":"avi.chanales@nyu.edu","name":"Avi Chanales","website":""}]},{"accession_number":"ds000122","project_name":"Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial ICA","summary":"Animal studies indicate that different functional networks (FNs), each with a unique timecourse, may overlap at common brain regions. For understanding how different FNs overlap in the human brain and how the timecourses of overlapping FNs are modulated by cognitive tasks, we applied spatial independent component analysis (sICA) to functional magnetic resonance imaging (fMRI) data. These data were acquired from healthy participants while they performed a visual task with parametric loads of attention and working memory.
\r\n\r\nFunctional images were acquired using gradient-echo EPI scanning sequence (TR/ TE=1500/30ms, Flip angle=70 degrees, 26 slices, 3mm thick with 1.2 mm skip, 3.125 × 3.125 mm2 in plane pixels) with a Siemens Allegra 3T system. The scanning plane was off the AC-PC line rostrally at 20 degrees. The thin scanning slice and tilted scanning plane were used to reduce susceptibility-related signal loss at the basal forebrain (Deichmann et al 2003). Each participant had three functional runs, and each run used a different task script and acquired 258 volumes. The order of scripts was counterbalanced across participants.
\r\n","sample_size":17,"scanner_type":"Siemens Allegra 3T","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Task-related concurrent but opposite modulations of overlapping functional networks as revealed by spatial ICA","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3677796/"}],"task_set":[{"cogat_id":"trm_4c8a84f20dde2","number":1,"name":"visual attention task","url":"http://www.cognitiveatlas.org/id/trm_4c8a84f20dde2"}],"revision_set":[{"revision_number":"1.0.0","notes":"Initial release","date_set":"2016-04-08"}],"investigator_set":[{"investigator":"MN Potenza"},{"investigator":"J Monterosso"},{"investigator":"VD Calhoun"},{"investigator":"S Zhang"},{"investigator":"J Xu"}],"link_set":[{"title":"Raw data archive (imaging + metadata)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds122_R1.0.0_complete.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000244","project_name":"Individual Brain Charting","summary":"Functional Magnetic Resonance Imaging (fMRI) has opened the possibility to map all types of perceptual, motor or cognitive functions, providing an instrumental basis for the whole field of cognitive neuroimaging. However, no systematic data collection has so far been undertaken toward a fine spatial and systematic representation of mental functions. In order to attain to such a comprehensive atlas of brain responses, the Individual Brain Charting (IBC) project aims to provide a dataset that contains high-resolution multi-task fMRI data. These refer to a cohort of twelve participants performing many different tasks, yielding a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. Additionally, the dataset is complemented with high-resolution anatomical and diffusion images, pertaining to a fine anatomical characterization of these brains.
\r\n","sample_size":12,"scanner_type":"Siemens Prisma 3T","acknowledgements":"We wish to thank Alexis Amadon for the set-up of MRI sequences, Chantal Ginisty, Séverine Becuwe, and Séverine Desmidt for running MRI acquisitions while tracking all possible artifacts and for their help in all experimental procedures. Thanks also to Isabelle Denghien for helping in task set-up and video annotations, to Torsten Ruest for the set up of HCP tasks and pilot acquisitions; thanks to Murielle Fabre Christophe Pallier for the RSV Language task design, and to Philippe Pinel for the ARCHI task design. Many thanks to Laurence Laurier for participant handling, to Christine Doublé for hiring and communication with participants, to Ludivine Monassier for participant screening and to Bernadette Martins the set up of the medical protocol and careful follow-up of legal and safety safety procedure. Thanks to Elvis Dohmatob for help in data processing, to Evelyn Eger, Stanislas Dehaene and Gaël Varoquaux for their thoughtful advice on study design and analysis. \r\n\r\nH2020 Framework Programme for Research and Innovation; grant agreement 720270 (Human Brain Project SGA1)","license_title":"CC0","license_url":"https://creativecommons.org/publicdomain/zero/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[],"revision_set":[{"revision_number":"1.0.1","notes":"- Updated participants.tsv","date_set":"2018-04-07"},{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-11-09"}],"investigator_set":[{"investigator":"Ana Luisa Grilo-Pinho"},{"investigator":"Lucie Hertz-Pannier"},{"investigator":"Bertrand Thirion"}],"link_set":[{"title":"Data for sub-14 (23.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub14.zip","revision":"1.0.1"},{"title":"Data for sub-13 (24.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub13.zip","revision":"1.0.1"},{"title":"Data for sub-11 (24.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub11.zip","revision":"1.0.1"},{"title":"Data for sub-09 (23.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub09.zip","revision":"1.0.1"},{"title":"Data for sub-08 (24.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub08.zip","revision":"1.0.1"},{"title":"Data for sub-07 (25.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub07.zip","revision":"1.0.1"},{"title":"Data for sub-06 (25.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub06.zip","revision":"1.0.1"},{"title":"Data for sub-05 (24.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub05.zip","revision":"1.0.1"},{"title":"Data for sub-04 (24.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub04.zip","revision":"1.0.1"},{"title":"Data for sub-02 (21.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub02.zip","revision":"1.0.1"},{"title":"Data for sub-01 (24.2 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_sub01.zip","revision":"1.0.1"},{"title":"Metadata (109.5 KB)","url":"https://s3.amazonaws.com/openneuro/ds000244/ds000244_R1.0.1/compressed/ds000244_R1.0.1_metadata.zip","revision":"1.0.1"},{"title":"Data for sub-14 (23.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub14.zip","revision":"1.0.0"},{"title":"Data for sub-13 (24.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub13.zip","revision":"1.0.0"},{"title":"Data for sub-12 (24.0 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub12.zip","revision":"1.0.0"},{"title":"Data for sub-11 (24.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub11.zip","revision":"1.0.0"},{"title":"Data for sub-09 (23.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub09.zip","revision":"1.0.0"},{"title":"Data for sub-08 (24.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub08.zip","revision":"1.0.0"},{"title":"Data for sub-07 (25.9 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub07.zip","revision":"1.0.0"},{"title":"Data for sub-06 (25.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub06.zip","revision":"1.0.0"},{"title":"Data for sub-05 (24.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub05.zip","revision":"1.0.0"},{"title":"Data for sub-04 (24.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub04.zip","revision":"1.0.0"},{"title":"Data for sub-02 (21.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub02.zip","revision":"1.0.0"},{"title":"Data for sub-01 (24.2 GB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_sub01.zip","revision":"1.0.0"},{"title":"Metadata (109.4 KB)","url":"https://s3.amazonaws.com/openneuro/ds000244/compressed/ds000244_R1.0.0_metadata.zip","revision":"1.0.0"}],"contacts":[{"email":"bertrand.thirion@inria.fr","name":"Bertrand Thirion","website":""},{"email":"ana-luisa.grilo-pinho@inria.fr","name":"Ana Luisa Grilo Pinho","website":""}]},{"accession_number":"ds000120","project_name":"Developmental changes in brain function underlying the influence of reward processing on inhibitory control (Slot Reward)","summary":"Adolescence is a period marked by changes in motivational and cognitive brain systems. However, the development of the interactions between reward and cognitive control processing are just beginning to be understood. Using event-related functional neuroimaging and an incentive modulated antisaccade task, we compared blood-oxygen level dependent activity underlying motivated response inhibition in children, adolescents, and adults.
\r\n\r\nAFNI (Analysis and Visualization of Functional Neuroimages) software (Cox, 1996) was used for individual subject deconvolution as well as subsequent group analyses. Deconvolution methods followed steps delineated previously (Ward, 1998). Briefly, our model consisted of two orthogonal regressors of interest for reward and neutral correct AS trials, as well as regressors for incorrect AS trials and all VGS trials. Linear and non-linear trends and six motion parameters were also included as nuisance regressors. A unique estimated impulse response function (i.e., hemodynamic response function) for each regressor of interest (correct reward and neutral AS trials) was determined by a weighted linear sum of eight sine basis functions multiplied by data determined least squares estimated beta weights. The estimated impulse response function reflects the estimated BOLD response to a type of trial (reward AS trial) after controlling for variations in the BOLD signal due to other regressors. We made no assumptions about the shape of the function. We specified the duration of the estimated response from the trial onset (0 seconds) to 24 seconds (17 TRs) post trial onset, a sufficient time window for the hemodynamic response to peak and return to baseline, which was defined as the jittered fixation periods between trials.
\r\n\r\n\r\n\r\n
\r\n","sample_size":27,"scanner_type":"3T Siemens Allegra MRI scanner","acknowledgements":"This study was supported by National Institute of Mental Health Grant MH080243.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Developmental Changes in Brain Function Underlying the Influence of Reward Processing on Inhibitory Control.","url":"http://www.sciencedirect.com/science/article/pii/S1878929311000612"}],"task_set":[{"cogat_id":"tsk_4a57abb949869","number":1,"name":"antisaccade","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949869"},{"cogat_id":"tsk_4a57abb949869","number":2,"name":"antisaccade","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949869"},{"cogat_id":"tsk_4a57abb949869","number":3,"name":"antisaccade","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949869"}],"revision_set":[{"revision_number":"1.0.1","notes":"- Removed sub-07 from participants.tsv as data for it didn't existed","date_set":"2017-10-11"},{"revision_number":"1.0.0","notes":"","date_set":"2016-04-01"}],"investigator_set":[{"investigator":"Beatriz Luna"},{"investigator":"Theresa Teslovich"},{"investigator":"Sarah J. Ordaz"},{"investigator":"Charles F. Geier"},{"investigator":"Padmanabhan, Aarthi"}],"link_set":[{"title":"Metadata and Derivatives","url":"https://s3.amazonaws.com/openneuro/ds000120/ds000120_R1.0.1/compressed/ds000120_R1.0.1_metdata_derivatives.zip","revision":"1.0.1"},{"title":"Data for subject 1-27 (2.4 GB)","url":"https://s3.amazonaws.com/openneuro/ds000120/ds000120_R1.0.1/compressed/ds000120_R1.0.1_sub01-27.zip","revision":"1.0.1"},{"title":"Imaging data for all subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds120_R1.0.0_01-27.tgz","revision":"1.0.0"},{"title":"Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds120_R1.0.0_metadata_derivatives.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000212","project_name":"Moral judgments of intentional and accidental moral violations across Harm and Purity domains","summary":"
The purpose of this data is to investigate neural differences within regions associated with Theory of Mind across a) intentional vs accidental moral acts; b) across moral domains (harmful vs impure acts); c) across moral subdomains; d) between morally relevant and nonmoral scenarios. Subjects were scanned while completing a Theory of Mind localizer task, and while completing the moral judgment task. For each scenario, subjects saw a text-based version of the scenario, and rated its moral wrongness on a 1-4 scale. Each scenario text was presented in 4 serial segments, comprising Background, Action, Outcome, and Intent.
\r\n\r\n\r\n\r\n
For representational similarity analysis scripts: https://github.com/lypsychlab/RSA
\r\n","sample_size":39,"scanner_type":"Siemens 3T Trio","acknowledgements":"Alfred P. Sloan Foundation, Simons Foundation, NIH Grant 1R01 MH096914-01A1. Thanks to the members of the Morality Lab and Saxelab for helpful comments on manuscripts and data analyses.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Decoding moral judgments from neural representations of intentions","url":"http://www.pnas.org/content/110/14/5648"},{"title":"When minds matter for moral judgment: intent information is neurally encoded for harmful but not impure acts","url":"http://scan.oxfordjournals.org/content/11/3/476.long"}],"task_set":[{"cogat_id":"trm_4f2456027809f","number":0,"name":"false belief task","url":"http://www.cognitiveatlas.org/id/trm_4f2456027809f"}],"revision_set":[{"revision_number":"1.0.1","notes":"- Added BIDSVersion to dataset_description.json","date_set":"2017-10-11"},{"revision_number":"1.0.0","notes":"- Initial release ","date_set":"2016-12-01"}],"investigator_set":[{"investigator":"Amelia Brown"},{"investigator":"James Dungan"},{"investigator":"Jorie Koster-Hale"},{"investigator":"Emily Wasserman"},{"investigator":"Alek Chakroff"},{"investigator":"Rebecca Saxe"},{"investigator":"Liane Young"}],"link_set":[{"title":"Metadata and derivatives","url":"https://s3.amazonaws.com/openneuro/ds000212/ds000212_R1.0.1/compressed/ds000212_R1.0.1_metadata_derivatives.zip","revision":"1.0.1"},{"title":"Data for subjects 20-47 (4.5 GB)","url":"https://s3.amazonaws.com/openneuro/ds000212/ds000212_R1.0.1/compressed/ds000212_R1.0.1_sub20-47.zip","revision":"1.0.1"},{"title":"Data for subject 03-19 (3.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000212/ds000212_R1.0.1/compressed/ds000212_R1.0.1_sub03-19.zip","revision":"1.0.1"},{"title":"Raw Data for Sub 20-47","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000212/ds000212_R1.0.0/compressed/ds000212_R1.0.0_sub20-47.zip","revision":"1.0.0"},{"title":"Raw Data for Sub 03-19","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000212/ds000212_R1.0.0/compressed/ds000212_R1.0.0_sub03-19.zip","revision":"1.0.0"},{"title":"Metadata and Derivatives ","url":"https://s3.amazonaws.com/traverses3-openfmri/ds000212/ds000212_R1.0.0/compressed/ds000212_R1.0.0_metadata_derivatives.zip","revision":"1.0.0"}],"contacts":[{"email":"liane.young@bc.edu","name":"Liane Young (Contact only after 7/2017)","website":""}]},{"accession_number":"ds000105","project_name":"Visual object recognition","summary":"Neural responses, as reflected in hemodynamic changes, were measured in six subjects (five female and one male) with gradient echo echoplanar imaging on a GE 3T scanner (General Electric, Milwaukee, WI) [repetition time (TR) = 2500 ms, 40 3.5-mm-thick sagittal images, field of view (FOV) = 24 cm, echo time (TE) = 30 ms, flip angle = 90] while they performed a one-back repetition detection task. High-resolution T1-weighted spoiled gradient recall (SPGR) images were obtained for each subject to provide detailed anatomy (124 1.2-mm-thick sagittal images, FOV = 24 cm). Stimuli were gray-scale images of faces, houses, cats, bottles, scissors, shoes, chairs, and nonsense patterns. The categories were chosen so that all stimuli from a given category would have the same base level name. The specific categories were selected to allow comparison with our previous studies (faces, houses, chairs, animals, and tools) or ongoing studies (shoes and bottles). Control nonsense patterns were phase-scrambled images of the intact objects. Twelve time series were obtained in each subject. Each time series began and ended with 12 s of rest and contained eight stimulus blocks of 24-s duration, one for each category, separated by 12-s intervals of rest. Stimuli were presented for 500 ms with an interstimulus interval of 1500 ms. Repetitions of meaningful stimuli were pictures of the same face or object photographed from different angles. Stimuli for each meaningful category were four images each of 12 different exemplars.
\r\n\r\n\r\n\r\n
Note that the original version of the raw data that was posted prior to 10/29/2012 had one extra timepoint incorrectly added to the end of runs 1-11 for each subject. The currently posted version has been corrected.
\r\n","sample_size":6,"scanner_type":"GE 3T","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Partially distributed representations of objects and faces in ventral temporal cortex","url":"http://www.ncbi.nlm.nih.gov/pubmed/15829079"},{"title":"Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a “face” area?","url":"http://www.ncbi.nlm.nih.gov/pubmed/15325362"},{"title":"Distributed and overlapping representations of faces and objects in ventral temporal cortex","url":"http://www.ncbi.nlm.nih.gov/pubmed/11577229"}],"task_set":[{"cogat_id":"trm_4ebd47b8bab6b","number":1,"name":"object one-back task","url":"http://www.cognitiveatlas.org/id/trm_4ebd47b8bab6b"}],"revision_set":[{"revision_number":"2.0.1","notes":"- Added authors to dataset_description.json","date_set":"2016-10-01"},{"revision_number":"1.0.1","notes":" - Updated release, corrected a problem with initial release in which one extra timepoint was incorrectly added to the end of runs 1-11 for each subject.","date_set":"2012-10-29"},{"revision_number":"1.0.0","notes":" - Initial release","date_set":"2011-10-12"},{"revision_number":"2.0.2","notes":"- Edited authors string in dataset_description.json","date_set":"2016-10-28"},{"revision_number":"2.0.0","notes":"- Converted to BIDS Std","date_set":"2016-09-28"}],"investigator_set":[{"investigator":"Pietrini, P."},{"investigator":"Schouten, J."},{"investigator":"Ishai, A."},{"investigator":"Furey, M."},{"investigator":"Gobbini, M."},{"investigator":"Haxby, J."}],"link_set":[{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000105/ds000105_R2.0.1/compressed/ds000105_R2.0.1_raw.tgz","revision":"2.0.1"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000105/ds000105_R1.0.1/compressed/ds105_raw.tgz","revision":"1.0.1"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000105/ds000105_R2.0.2/compressed/ds000105_R2.0.2_raw.zip","revision":"2.0.2"},{"title":"Raw data on AWS","url":"https://s3.amazonaws.com/openneuro/ds000105/ds000105_R2.0.0/compressed/ds000105_R2.0.0_raw.tgz","revision":"2.0.0"}],"contacts":[]},{"accession_number":"ds000229","project_name":"Integration of sweet taste and metabolism determines carbohydrate reward - study 1","summary":"Non-caloric beverages were mixed from novel flavors, citric acid, sucralose and food coloring. For each session participants arrived to the lab fasted (4 hours). In the pre-conditioning session, participants were trained on how to use the scales to rate overall intensity, sweetness, liking, and wanting. Then participants rated each of the non-caloric stimuli 3 times. Average ratings were calculated and participants who rated 5 beverages as equally liked were asked to perform a triangle test to determine if they were able to reliably detect the presence of maltodextrin (DE = 12.5) in a beverage. Participants, with 5 similarly liked beverages who were unable to detect maltodextrin were then trained on the fMRI procedures. Meanwhile a second experimenter prepared the subject-specific beverage assignments so that color, flavors and caloric load were counterbalanced across subjects. The participant was then scheduled for 5 exposure sessions during which each beverage was consumed 6 times. Following the exposure sessions, subjects returned for a post-conditioning session where they rated the 10 non-caloric beverages for sweetness, liking, familiarity and wanting (as in the pre-conditioning session). An fMRI session followed in which participants sampled the non-caloric versions of the 5 exposed beverage (CS-, CS37.5, CS75, CS112.5, and CS150), as well as a tasteless and odorless control solution.
\r\n","sample_size":15,"scanner_type":"Siemens Trio Tim Syngo MR B17","acknowledgements":"","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_5887c029d46f4","number":1,"name":"Gustatory stimulation with liquid tastes or flavors ","url":"http://www.cognitiveatlas.org/id/trm_5887c029d46f4"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial Release","date_set":"2017-07-17"}],"investigator_set":[{"investigator":"Dana M Small"},{"investigator":"Martin Yeomans"},{"investigator":"Elizabeth Garcia"},{"investigator":"Nils B Kroemer"},{"investigator":"Wambura Fobbs"},{"investigator":"Barkha Patel"},{"investigator":"Richard Keith Babbs"},{"investigator":"Maria Geraldine Veldhuizen"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000229/ds000229_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000229/ds000229_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Statistical map - Neurovault.org","url":"http://neurovault.org/collections/2570/","revision":"1.0.0"},{"title":"Data for all Subjects (3.7 GB)","url":"https://s3.amazonaws.com/openneuro/ds000229/ds000229_R1.0.0/compressed/ds000229_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"mveldhuizen@jbpierce.org","name":"Maria Veldhuizen","website":""}]},{"accession_number":"ds000222","project_name":"Sequential Inference VBM","summary":"These data comprise behaviour from 79 subjects on a probabilistic reversal task together with T1-weighted structural images. (The task is described in more detail in: FitzGerald et al. Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions. PLoS Computational Biology (2017))
\r\n
\r\nThe principal findings of the original study were that the majority of subjects employed a strategy of inferring about the joint probability of sequences of states stretching into the past, and that betwene-subject differences in strategy correlated with gery-matter density changes in various parts of the brain.
\r\n
\r\nData were collected from 43 younger adults and 36 older adults. Additionally, most of the subjects performed the Raven's matrices task, and an n-back working memory task, and results from these are also included, together with height, weight, age and sex.
Subjects performed two versions of a stop signal task. In the unconditional stop-signal task, subjects are told to withhold their response whenever they hear a tone. In the conditional stop signal task, they are told to withhold their response if they hear the tone and the response is the one labeled as critical, whereas they should go ahead and respond if the response is the noncritical one.
\r\n\r\nRevision history:
\r\n\r\n12/20/2012: The originally posted version of this dataset was missing some onsets for task002. The newly posted version contains the full set of onsets for all conditions. If only the onsets and model info are needed, they can be obtained by downloading the updated onsets file and untarring it in the main ds008 directory.
\r\n\r\n3/7/2016: Remove sub008 due to inconsistently defined onsets.
\r\n\r\n5/22/2016: Converted to BIDS file structure
\r\n","sample_size":15,"scanner_type":"3T Siemens Allegra MRI scanner","acknowledgements":"This work was supported by a 21st Century Science Award from the James S. McDonnell Foundation (R.A.P.) and by the United Kingdom Medical Research Council (T.E.B.). We thank David Flitney for three-dimensional rendering, Joe Devlin for helpful comments on this manuscript, and Bill Lanouette for editing assistance.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Triangulating a Cognitive Control Network using Diffusion-weighted MRI and Functional MRI","url":"http://www.ncbi.nlm.nih.gov/pubmed/17409238"}],"task_set":[{"cogat_id":"tsk_4a57abb949e1a","number":1,"name":"stop signal task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949e1a"},{"cogat_id":"trm_4cacf3fbc503b","number":2,"name":"conditional stop signal task","url":"http://www.cognitiveatlas.org/id/trm_4cacf3fbc503b"}],"revision_set":[{"revision_number":"1.0.1","notes":"The originally posted version of this dataset was missing some\r\nonsets for task002. The newly posted version contains the full set of onsets\r\nfor all conditions. If only the onsets and model info are needed, they can be\r\nobtained by downloading the updated onsets file and untarring it in the main\r\nds008 directory.\r\n","date_set":"2012-12-20"},{"revision_number":"2.0.0","notes":"Converted to BIDS format","date_set":"2016-05-22"},{"revision_number":"1.1.1","notes":"Remove sub008 due to inconsistent onsets. task001/cond001 and cond004 had identical onsets. All other data remain the same. sub008 data are still available in R1.1.0 dataset.","date_set":"2016-03-07"},{"revision_number":"1.1.0","notes":"Updated orientation information in NIFTI headers for improved left-right determination","date_set":"2016-02-19"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2011-10-06"}],"investigator_set":[{"investigator":"Poldrack, R.A."},{"investigator":"Smith, S."},{"investigator":"Frank, M."},{"investigator":"Behrens, T.E."},{"investigator":"Aron, A.R."}],"link_set":[{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000008/ds000008_R1.0.1/compressed/ds008_raw.tgz","revision":"1.0.1"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000008/ds000008_R2.0.0/compressed/ds008_R2.0.0_raw.tgz","revision":"2.0.0"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000008/ds000008_R1.1.1/compressed/ds008_R1.1.1_raw.tgz","revision":"1.1.1"},{"title":"Raw data in AWS","url":"https://s3.amazonaws.com/openneuro/ds000008/ds000008_R1.1.0/compressed/ds008_R1.1.0_raw.tgz","revision":"1.1.0"}],"contacts":[]},{"accession_number":"ds000234","project_name":"Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 1","summary":"We investigated the use of accelerated 3D readouts to obtain whole-brain, high-SNR ASL perfusion maps and reduce SAR deposition. Parallel imaging was implemented along the partition-encoding direction in a pseudo-continuous ASL sequence with background-suppression and 3D RARE Stack-Of-Spirals readout, and its performance was evaluated in three small cohorts. First, both non-accelerated and two-fold accelerated single-shot versions of the sequence were evaluated in healthy volunteers during a motor-photic task, and the performance was compared in terms of temporal SNR, GM-WM contrast, and statistical significance of the detected activation. Secondly, single-shot 1D-accelerated imaging was compared to a two-shot accelerated version to assess benefits of SNR and spatial resolution for applications in which temporal resolution is not paramount. Third, the efficacy of this approach in clinical populations was assessed by applying the single-shot 1D-accelerated version to a larger cohort of elderly volunteers.
\r\n","sample_size":5,"scanner_type":"3T Siemens Tim Trio B17 ","acknowledgements":"This work was supported by the National Institutes of Health (http://www.nih.gov/), grants no. P41EB015893 and MH080729, by National Natural Science Foundation of China (http://www.nsfc.gov.cn/), grant no. 81471644, and Hangzhou Innovation Seed Fund.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_5975f939336c0","number":1,"name":"motorphotic","url":"http://www.cognitiveatlas.org/id/trm_5975f939336c0"}],"revision_set":[{"revision_number":"1.0.0","notes":"-- Initial Release ","date_set":"2017-07-10"}],"investigator_set":[{"investigator":"John A. Detre"},{"investigator":"María A. Fernández-Seara"},{"investigator":"Yulin V. Chang"},{"investigator":"Ze Wang"},{"investigator":"Marta Vidorreta"}],"link_set":[{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000234/ds000234_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for all Subjects ","url":"https://s3.amazonaws.com/openneuro/ds000234/ds000234_R1.0.0/compressed/ds000234_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"","name":"Marta Vidorreta","website":""}]},{"accession_number":"ds000171","project_name":"Neural Processing of Emotional Musical and Nonmusical Stimuli in Depression","summary":"The present dataset uses functional MRI and an validated emotional music and nonmusical auditory paradigm (Lepping, et al., 2015) to examine how neural processing of emotionally provocative auditory stimuli is altered in depression. Nineteen individuals with depression (MDD) and 20 never-depressed control participants (ND) listened to positive and negative emotional musical and nonmusical stimuli during fMRI scanning. ND participants had no history of depression or other psychiatric disorder, determined by administration of the Structured Clinical Interview for DSM Disorders, non-patient version (SCID-I/NP) (First, 2002). Participants in the MDD group were all experiencing a current depressive episode at the time of scanning, determined by screening for research purposes using the SCID-I/NP. Participants had no current or past manic episodes, no comorbid anxiety disorders, and no current alcohol abuse or dependence, and were not taking medication for depression at the time of the study.
\r\n","sample_size":39,"scanner_type":"Siemens Skyra 3T","acknowledgements":"The authors wish to acknowledge Trisha Patrician and Natalie Stroupe for their assistance with screening of participants, and Allan Schmitt and Franklin Hunsinger for their role in collecting the MR data.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Development of a validated emotionally provocative musical stimulus set for research","url":"http://pom.sagepub.com/content/early/2015/10/14/0305735615604509"}],"task_set":[{"cogat_id":"trm_4c8990b07a037","number":1,"name":"music comprehension/production","url":"http://www.cognitiveatlas.org/id/trm_4c8990b07a037"}],"revision_set":[{"revision_number":"1.0.0","notes":"Initial Publishing","date_set":"2016-05-26"}],"investigator_set":[{"investigator":"Cary R. Savage"},{"investigator":"W. Kyle Simmons"},{"investigator":"Rick E. Ingram"},{"investigator":"Alicia A. Clair"},{"investigator":"Laura E. Martin"},{"investigator":"Evangelia Chrysikou"},{"investigator":"Ruth Ann Atchley"},{"investigator":"Rebecca J. Lepping"}],"link_set":[{"title":"Imaging data for affected (MDD) subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds171_R1.0.0_mdd.tgz","revision":"1.0.0"},{"title":"Imaging data for control subjects","url":"http://openfmri.s3.amazonaws.com/tarballs/ds171_R1.0.0_controls.tgz","revision":"1.0.0"},{"title":"Dataset Metadata","url":"http://openfmri.s3.amazonaws.com/tarballs/ds171_R1.0.0_metadata.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000216","project_name":"Multiband Multi-Echo Imaging of Simultaneous Oxygenation and Flow Timeseries for Resting State Connectivity","summary":"Multiband imaging was combined with with a multi-echo acquisition to collect whole-brain simultaneous pseudo-continuous arterial spin labeling (pCASL) and blood-oxygenation-level dependent (BOLD) echo-planar imaging (MBME ASL/BOLD). Resting-state connectivity in seven healthy adult subjects was assessed using this sequence. Four echoes were acquired with a multiband acceleration of four, in order to increase spatial resolution, shorten repetition time, and reduce slice-timing effects on the ASL signal. In addition, by acquiring four echoes, advanced multi-echo independent component analysis (ME-ICA) denoising could be employed to increase the signal-to-noise ratio (SNR) and BOLD sensitivity. Seed-based and dual-regression approaches were utilized to analyze functional connectivity. These metrics were compared between single echo (E2), multi-echo combined (MEC), multi-echo combined and denoised (MECDN), and perfusion-weighted (PW) timeseries.
\r\n","sample_size":7,"scanner_type":"GE MR750, DV25","acknowledgements":"This work was partially supported by a grant from the Daniel M. Soref Charitable Trust. We thank Ajit Shankaranarayanan and Matt Middione from GE Healthcare for providing source code of the GE multiband sequence.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Multiband multi-echo imaging of simultaneous oxygenation and flow timeseries for resting state connectivity","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333818/"}],"task_set":[{"cogat_id":"trm_54e69c642d89b","number":0,"name":"rest eyes closed","url":"http://www.cognitiveatlas.org/id/trm_54e69c642d89b"}],"revision_set":[{"revision_number":"1.0.1","notes":" - Updated filenames to comply with BIDS multi-echo filenaming\r\n - Removed derivatives/mriqc/work","date_set":"2017-10-09"},{"revision_number":"1.0.0","notes":"- Initial Release","date_set":"2017-01-26"}],"investigator_set":[{"investigator":"Yang Wang"},{"investigator":"R. Marc Lebel"},{"investigator":"Andrew S. Nencka"},{"investigator":"Alexander D. Cohen"}],"link_set":[{"title":"Full dataset (2 GB)","url":"https://s3.amazonaws.com/openneuro/ds000216/ds000216_R1.0.1/compressed/ds000216_R1.0.1.zip","revision":"1.0.1"},{"title":"Raw data for Subjects 1-7 ","url":"https://s3.amazonaws.com/openneuro/ds000216/ds000216_R1.0.0/compressed/ds000216_R1.0.0_sub01-07.zip","revision":"1.0.0"},{"title":"Metadata","url":"https://s3.amazonaws.com/openneuro/ds000216/ds000216_R1.0.0/compressed/ds000216_R1.0.0_metdata.zip","revision":"1.0.0"}],"contacts":[{"email":"acohen@mcw.edu","name":"Alexander D. Cohen","website":""}]},{"accession_number":"ds000233","project_name":"Neural responses to naturalistic clips of behaving animals in two different task contexts","summary":"Functional MRI was used to measure hemodynamic responses while participants viewed brief naturalistic clips of behaving animals under two task contexts. Twelve right-handed adults participated in the study. Functional and structural images were acquired using a 3 T Philips Intera Achieva MRI scanner with a 32-channel phased-array head coil (functional: TR/TE = 2000/35 ms, flip angle = 90°, resolution = 3 mm isotropic; structural: TR/TE = 8.2/3.7 ms, flip angle = 8°, resolution = 0.9375 × 0.9375 × 1.0 mm voxels). In total, stimuli comprised 40 unique 2 s video clips and their horizontally flipped counterparts for 80 visually unique clips. Ten unique runs were created and run order was counterbalanced across participants. Stimuli were presented in pseudorandom order and each of the 80 stimuli occurred once per run. Each event consisted of a 2 s stimulus presentation followed by 2 s fixation. Stimuli were organized into five taxonomic categories (birds, insects, reptiles, primates, and ungulates), and four behavioral categories (eating, fighting, running, and swimming) in a factorial design for 20 total category-level conditions. Participants were instructed to maintain fixation between trials, but freely viewed the video clips. Participants performed two different 1-back category repetition tasks. In half of the runs, participants were instructed to press a button when they noticed a taxonomic category repetition, and in the other half of the runs they were instructed to press the button when they noticed a behavioral category repetition. Repetition events were sparse by design (~4 per run of each type) and participants were instructed to ignore task-irrelevant repetitions.
\r\n","sample_size":12,"scanner_type":"Philips Intera Achieva","acknowledgements":"We thank Jason Gors, Kelsey G. Wheeler J. Swaroop Guntupalli, Matteo Visconti di Oleggio Castello, M. Ida Gobbini, Terry Sacket, and the rest of the DBIC (Dartmouth Brain Imaging Center) personnel for assistance in data collection/curation.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Attention Selectively Reshapes the Geometry of Distributed Semantic Representation","url":"https://academic.oup.com/cercor/article-lookup/doi/10.1093/cercor/bhx138"}],"task_set":[{"cogat_id":"tsk_4a57abb949bcd","number":1,"name":"n-back task","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949bcd"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial Release","date_set":"2017-07-25"}],"investigator_set":[{"investigator":"James V. Haxby"},{"investigator":"Andrew C. Connolly"},{"investigator":"Yaroslav O. Halchenko"},{"investigator":"Samuel A. Nastase"}],"link_set":[{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000233/ds000233_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000233/ds000233_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC (447. MB)","url":"https://s3.amazonaws.com/openneuro/ds000233/ds000233_R1.0.0/compressed/ds000233_R1.0.0_mriqc.zip","revision":"1.0.0"},{"title":"Metadata (203 KB)","url":"https://s3.amazonaws.com/openneuro/ds000233/ds000233_R1.0.0/compressed/ds000233_R1.0.0_metadata.zip","revision":"1.0.0"},{"title":"Data for all Subjects (4.0 GB)","url":"https://s3.amazonaws.com/openneuro/ds000233/ds000233_R1.0.0/compressed/ds000233_R1.0.0_sub-rid000001-rid000041.zip","revision":"1.0.0"}],"contacts":[{"email":"sam.nastase@gmail.com","name":"Samuel A. Nastase","website":""}]},{"accession_number":"ds000121","project_name":"Immaturities in Reward Processing and Its Influence on Inhibitory Control in Adolescence (Ring Reward)","summary":"The nature of immature reward processing and the influence of rewards on basic elements of cognitive control during adolescence are currently not well understood. Here, during functional magnetic resonance imaging, healthy adolescents and adults performed a modified antisaccade task in which trial-by-trial reward contingencies were manipulated. The use of a novel fast, event- related design enabled developmental differences in brain function underlying temporally distinct stages of reward processing and response inhibition to be assessed.
\r\n\r\nBriefly, our model consisted of 6 orthogonal regressors of interest (reward cue, neutral cue, reward preparation, neutral preparation, reward saccade response, neutral saccade response; “correct AS trials only”). We also included regressors for reward and neutral error trials (consisting of the entire trial), regressors for baseline, linear, and nonlinear trends, as well as 6 motion parameters included as “nuisance” regressors. A unique estimated impulse response function (IRF, i.e., hemodynamic response function) for each regressor of interest (reward and neutral cue, preparation, and saccade; “correct AS trials only”) was determined by a weighted linear sum of 5 sine basis functions multiplied by a data determined least squares–estimated beta weight. The estimated IRF reflects the estimated BOLD response to a type of stimulus (e.g., the reward cue) after controlling for variations in the BOLD signal due to other regressors. We specified the duration of the estimated response from stimulus onset (time = 0) to 18-s poststimulus onset (13 TR), a sufficient duration for the estimated BOLD response to return to baseline, for each separate epoch of the trial. We made no assumptions about its specific shape beyond using zero as the start point. Several goodness-of-fit statistics were calculated including partial F-statistics for each regressor and t-scores comparing each of the 5 estimated beta weights with zero.
\r\n\r\nScripts: https://github.com/LabNeuroCogDevel/openfmri_ring_rew
\r\n\r\nScanSheets: https://docs.google.com/spreadsheets/d/1kzNxuRPnyalaG5K66ADFIavJOYfQl5OX...
\r\n","sample_size":28,"scanner_type":"3T Siemens Allegra MRI scanner","acknowledgements":"National Institutes of Health (RO1 MH067924, RO1 MH080243 to B.L.).","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Immaturities in Reward Processing and Its Influence on Inhibitory Control in Adolescence.","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2882823/"}],"task_set":[{"cogat_id":"tsk_4a57abb949869","number":1,"name":"antisaccade","url":"http://www.cognitiveatlas.org/id/tsk_4a57abb949869"}],"revision_set":[{"revision_number":"2.0.2","notes":"- Added duration column to events.tsv files","date_set":"2017-10-11"},{"revision_number":"2.0.1","notes":"Fixed duplicate field in fMRI task .json file (PhaseEncodeDirection -> InPlanePhaseEncodingDirection).\r\nUpdated CHANGES document. \r\nNo changes made to other data.","date_set":"2016-04-10"},{"revision_number":"2.0.0","notes":"Repackaged in BIDS format","date_set":"2016-04-01"},{"revision_number":"1.0.0","notes":"Initial publishing of raw data","date_set":"2015-11-15"}],"investigator_set":[{"investigator":"B. Luna"},{"investigator":"K. Velanova"},{"investigator":"T. Teslovich"},{"investigator":"R. Terwilliger"},{"investigator":"Geier, C. F."}],"link_set":[{"title":"Metadata and Derivatives","url":"https://s3.amazonaws.com/openneuro/ds000121/ds000121_R2.0.2/compressed/ds000121_R2.0.2_metadata_derivatives.zip","revision":"2.0.2"},{"title":"Data for sub 01-28","url":"https://s3.amazonaws.com/openneuro/ds000121/ds000121_R2.0.2/compressed/ds000121_R2.0.2_sub01-28.zip","revision":"2.0.2"},{"title":"Data for all Subjects (01-28)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds121_R2.0.1_01-28.tgz","revision":"2.0.1"},{"title":"Dataset Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds121_R2.0.1_metadata_derivatives.tgz","revision":"2.0.1"},{"title":"Data for all Subjects (01-28)","url":"http://openfmri.s3.amazonaws.com/tarballs/ds121_R2.0.0_01-28.tgz","revision":"2.0.0"},{"title":"Dataset Metadata and Derivatives","url":"http://openfmri.s3.amazonaws.com/tarballs/ds121_R2.0.0_metadata_derivatives.tgz","revision":"2.0.0"},{"title":"Raw Data checksums","url":"http://openfmri.s3.amazonaws.com/tarballs/ds121_raw_checksums.txt","revision":"1.0.0"},{"title":"Raw Data on AWS","url":"http://openfmri.s3.amazonaws.com/tarballs/ds121_raw.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000254","project_name":"Multiband Multi-Echo Simultaneous ASL/BOLD for task-induced functional MRI","summary":"This study applies a sequence combining pseudo-continuous ASL (pCASL) labeling with a multiband (MB), multi-echo (ME) acquisition for simultaneous BOLD and ASL echo planar imaging (MBME ASL/BOLD) for block-design task-fMRI. A MB-factor of 4 was employed and 4 echoes were collected. Data was acquired during a bilateral finger-tapping task with 40s tap and rest periods. Multi-echo independent component analysis (MEICA) was implemented to automatically denoise the BOLD and ASL signal. This technique led to increased temporal signal-to-noise ratio (tSNR) and sensitivity. Signal characteristics and activation were evaluated using single echo BOLD, combined ME BOLD, combined ME BOLD after MEICA denoising, perfusion-weighted (PW), and perfusion-weighted after MEICA denoising time-series. Our data suggest that the MBME ASL/BOLD sequence can be employed to collect whole-brain task-fMRI with improved data quality for both BOLD and PW time series, thus improving the results of block-design task fMRI.
\r\n","sample_size":13,"scanner_type":"GE MR750 DV25_R01","acknowledgements":"The authors thank Marc Lebel, PhD for assistance with the ASL portion of the pulse sequence, and Ajit Shankaranarayanan, PhD and Matthew J. Middione, PhD from GE Healthcare for providing the source code of the GE multiband sequence. \r\nFunding: This work was supported by a Daniel M. Soref Charitable Trust Grant (to YW)","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Multiband multi-echo imaging of simultaneous oxygenation and flow timeseries for resting state connectivity","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333818/"}],"task_set":[{"cogat_id":"trm_4c898f079d05e","number":1,"name":"finger tapping task","url":"http://www.cognitiveatlas.org/id/trm_4c898f079d05e"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2018-01-11"}],"investigator_set":[{"investigator":"Yang Wang"},{"investigator":"Andrew S. Nencka"},{"investigator":"Alexander D. Cohen"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000254/ds000254_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical group report","url":"https://s3.amazonaws.com/openneuro/ds000254/ds000254_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for all subjects (1.8 GB)","url":"https://s3.amazonaws.com/openneuro/ds000254/ds000254_R1.0.0/compressed/ds000254_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"acohen@mcw.edu","name":"Alexander D. Cohen","website":""},{"email":"voonval@gmail.com","name":"Valerie Voon","website":""}]},{"accession_number":"ds000177","project_name":"Effects of mouth breathing on hippocampal activity examined by 3T fMRI","summary":"We investigated the effects of mouth breathing and typical nasal breathing on brain function, using blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI).
\r\n\r\nThe study consisted of two parts: the first test was a simple contrast between mouth and nasal breathing and the second test involved combined breathing modes, e.g., mouth inspiration and nasal expiration. Eleven healthy participants performed the combined breathing task while undergoing 3T fMRI. In the group-level analysis, contrast images from the nasal or mouth breathing acquired by the participant-level analyses were analyzed using a one-sample t-test. We also performed a region-of-interest analysis comparing signal intensity changes between the breathing modes; the regions were selected using an automated anatomical labeling map. The results demonstrated that BOLD signal in the hippocampus and brainstem decreased significantly during mouth breathing, whereas the signal increased in the central gyrus. Given that the hippocampus participates in cognitive functions such as memory, decreased hippocampal activity may explain the adverse effects of mouth breathing on brain function.
\r\n\r\nIn this dataset: High resolution T1 weighted structural and BOLD contrast fMRI scans.
\r\n","sample_size":11,"scanner_type":"Siemens Verio 3T","acknowledgements":"This work was supported by the Gachon University research fund (GCU-2015-0061) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1C1A1A02036462). ","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[],"task_set":[{"cogat_id":"trm_4c898a680e424","number":1,"name":"breath-holding","url":"http://www.cognitiveatlas.org/id/trm_4c898a680e424"}],"revision_set":[{"revision_number":"1.0.1","notes":"- Added orientation warning to README\r\n- Added CogAtlasID field to task-breathing_bold.json\r\n- Added dataset_description.json","date_set":"2017-10-14"},{"revision_number":"1.0.0","notes":"Initial Release","date_set":"2016-04-20"}],"investigator_set":[{"investigator":"Chang-Ki Kang"},{"investigator":"Chaejoon Cheong"},{"investigator":"Young-Bo Kim"},{"investigator":"Yeong-Bae Lee"},{"investigator":"Nambeom Kim"},{"investigator":"Chan-A Park"}],"link_set":[{"title":"MRIQC functional group report (238.0 KB)","url":"https://s3.amazonaws.com/openneuro/ds000177/ds000177_R1.0.1/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.1"},{"title":"MRIQC anatomical T1w group report (245.0 KB)","url":"https://s3.amazonaws.com/openneuro/ds000177/ds000177_R1.0.1/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.1"},{"title":"Full dataset (956.7 MB)","url":"https://s3.amazonaws.com/openneuro/ds000177/ds000177_R1.0.1/compressed/ds000177_R1.0.1.zip","revision":"1.0.1"},{"title":"All data for this dataset","url":"http://openfmri.s3.amazonaws.com/tarballs/ds177_R1.0.0_all_data.tgz","revision":"1.0.0"}],"contacts":[]},{"accession_number":"ds000256","project_name":"Behavioral interventions for reducing head motion during MRI scans in children","summary":"A major limitation to structural and functional MRI (fMRI) scans is their susceptibility to head motion artifacts. Even submillimeter movements can systematically distort functional connectivity, morphometric, and diffusion imaging results. In patient care, sedation is often used to minimize head motion, but it incurs increased costs and risks. In research settings, sedation is typically not an ethical option. Therefore, safe methods that reduce head motion are critical for improving MRI quality, especially in high movement individuals such as children and neuropsychiatric patients. We investigated the effects of (1) viewing movies and (2) receiving real-time visual feedback about head movement in 24 children (5-15 years old). Children completed fMRI scans during which they viewed a fixation cross (i.e., rest) or a cartoon movie clip, and during some of the scans they also received real-time visual feedback about head motion. Head motion was significantly reduced during movie watching compared to rest and when receiving feedback compared to receiving no feedback. However, these results depended largely on age, such that the effects were driven by the younger children. Children older than 10 years showed no significant benefit. We also found that viewing movies significantly altered the functional connectivity of fMRI data, suggesting that fMRI scans during movies cannot be equated to standard resting-state fMRI scans.
\r\n\r\nThe implications of these results are twofold:
\r\n\r\n(1) given the reduction in head motion with behavioral interventions, these methods should be tried first for all clinical and structural MRIs in lieu of sedation; and (2) for fMRI research scans, these methods can reduce head motion in certain groups, but investigators must keep in mind the effects on functional MRI data.
\r\n","sample_size":24,"scanner_type":"Siemens Tim Trio 3T MAGNETOM","acknowledgements":"This paper linked to this dataset was supported by the Intramural Research Program, National Institute of Mental Health/NIH (ZIAMH002920; NCT01031407) and by the Sackler Institute for Developmental Neurobiology (JDP). MRC P008747/1 supported data collection.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Behavioral interventions for reducing head motion during MRI scans in children","url":"https://www.sciencedirect.com/science/article/pii/S1053811918300235"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2018-01-19"}],"investigator_set":[{"investigator":"Nico U.F. Dosenbach"},{"investigator":"Damien A. Fair"},{"investigator":"Bradley L. Schlaggar"},{"investigator":"Steven E. Petersen"},{"investigator":"Joshua S. Shimony"},{"investigator":"Rachel L. Klein"},{"investigator":"Eric A. Earl"},{"investigator":"Lindsey McIntyre"},{"investigator":"Catherine R. Hoyt"},{"investigator":"Annie L. Nguyen"},{"investigator":"Andrew N. Van"},{"investigator":" Victoria Wesevich"},{"investigator":"Jacqueline M. Hampton"},{"investigator":"Jonathan M. Koller"},{"investigator":"Deanna J. Greene"}],"link_set":[{"title":"MRIQC functional group report","url":"https://s3.amazonaws.com/openneuro/ds000256/ds000256_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical group report T2w","url":"https://s3.amazonaws.com/openneuro/ds000256/ds000256_R1.0.0/uncompressed/derivatives/mriqc/reports/T2w_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical group report T1w","url":"https://s3.amazonaws.com/openneuro/ds000256/ds000256_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Data for all subjects, metadata and Mriqc (4.3 GB)","url":"https://s3.amazonaws.com/openneuro/ds000256/ds000256_R1.0.0/compressed/ds000256_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"dgreene@wustl.edu","name":"Deanna Greene","website":""}]},{"accession_number":"ds000258","project_name":"Multi-echo Cambridge","summary":"These are the multi-echo fMRI datasets analyzed in Power et al., 2018 to identify and separate multiple kinds of motion-related variance. More information can be found at www.jonathanpower.net/paper-multiecho.html.
\r\n","sample_size":89,"scanner_type":"Siemens Trio 3T with 32-channel head coil","acknowledgements":"This paper linked to this dataset was supported by the Intramural Research Program, National Institute of Mental Health/NIH (ZIAMH002920; NCT01031407) and by the Sackler Institute for Developmental Psychobiology. MRC P008747/1 supported data collection.","license_title":"PDDL","license_url":"http://opendatacommons.org/licenses/pddl/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Integrated strategy for improving functional connectivity mapping using multiecho fMRI.","url":"https://www.ncbi.nlm.nih.gov/pubmed/24038744"}],"task_set":[],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial revision ","date_set":"2018-01-22"}],"investigator_set":[{"investigator":"Alex Martin"},{"investigator":"Peter Bandettini"},{"investigator":"Prantik Kundu"},{"investigator":"Valerie Voon"},{"investigator":"Jonathan Power"}],"link_set":[{"title":"Data for Subjects 21036-21658","url":"https://s3.amazonaws.com/openneuro/ds000258/ds000258_R1.0.0/compressed/ds000258_R1.0.0_sub21036-21658.zip","revision":"1.0.0"},{"title":"Data for Subjects 04570-20984","url":"https://s3.amazonaws.com/openneuro/ds000258/ds000258_R1.0.0/compressed/ds000258_R1.0.0_sub04570-20984.zip","revision":"1.0.0"},{"title":"Mriqc","url":"https://s3.amazonaws.com/openneuro/ds000258/ds000258_R1.0.0/compressed/ds000258_R1.0.0_mriqc.zip","revision":"1.0.0"},{"title":"Metadata","url":"https://s3.amazonaws.com/openneuro/ds000258/ds000258_R1.0.0/compressed/ds000258_R1.0.0_metadata.zip","revision":"1.0.0"}],"contacts":[{"email":"jdp9009@nyp.org","name":"Jonathan Power","website":""},{"email":"voonval@gmail.com","name":"Valerie Voon","website":""}]},{"accession_number":"ds000245","project_name":"Olfactory dysfunction and functional connectivity changes in cognitively normal Parkinson’s disease","summary":"In this study, we investigated brain atrophic changes and functional connectivity alterations in Parkinson’s disease patients with severe hyposmia, patients with no/mild hyposmia, and healthy controls. All patients were assessed using Odor Stick Identification Test for the Japanese (OSIT-J) and Addenbroke’s Cognitive Examination-Revised (ACE-R) for their odor-identification performance and general cognitive function, respectively. We used resting state functional magnetic resonance imaging to evaluate functional connectivity.
\r\n","sample_size":45,"scanner_type":"Siemens Magnetom Verio 3T scanner","acknowledgements":"This research was supported in part by the following: a Grant-in-Aid from the Research Committee of Central Nervous System Degenerative Diseases by the Ministry of Health, Labour, and Welfare, Integrated Research on Neuropsychiatric Disorders project, carried out by SRBPS; a Grant-in-Aid for Scientific Research on Innovative Areas (Brain Protein Aging and Dementia Control 26117002) from the MEXT of Japan; Integrated Research on Neuropsychiatric Disorders carried out under the Strategic Research Program for Brain Sciences, Scientific Research on Innovative Areas (Comprehensive Brain Science Network); and Integrated Research on Depression, Dementia, and Development Disorders by the Strategic Research Program for Brain Sciences from the Japan Agency for Medical Research and Development (AMED).","license_title":"CC0","license_url":"https://creativecommons.org/publicdomain/zero/1.0/","curated":true,"publicationdocument_set":"dataset.PublicationDocument.None","publicationpubmedlink_set":[{"title":"Severe hyposmia and aberrant functional connectivity in cognitively normal Parkinson’s disease","url":"http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190072"}],"task_set":[{"cogat_id":"trm_54e69c642d89b","number":1,"name":"rest eyes closed","url":"http://www.cognitiveatlas.org/id/trm_54e69c642d89b"}],"revision_set":[{"revision_number":"1.0.0","notes":"- Initial release","date_set":"2017-10-27"}],"investigator_set":[{"investigator":"Gen Sobue"},{"investigator":"Masahisa Katsuno"},{"investigator":"Daisuke Mori"},{"investigator":"Satoshi Maesawa"},{"investigator":"Mizuki Ito"},{"investigator":"Tatsuya Hattori"},{"investigator":"Michihito Masuda"},{"investigator":"Yasutaka Kato"},{"investigator":"Rei Ogura"},{"investigator":"Kazunori Imai"},{"investigator":"Reiko Ohdake"},{"investigator":"Yasuhiro Tanaka"},{"investigator":"Takashi Tsuboi"},{"investigator":"Kazuhiro Hara"},{"investigator":"Epifanio Bagarinao"},{"investigator":"Kazuya Kawabata"},{"investigator":"Hirohisa Watanabe"},{"investigator":"Noritaka Yoneyama"}],"link_set":[{"title":"MRIQC functional group report (284.8 KB)","url":"https://s3.amazonaws.com/openneuro/ds000245/ds000245_R1.0.0/uncompressed/derivatives/mriqc/reports/bold_group.html","revision":"1.0.0"},{"title":"MRIQC anatomical T1w group report (322.6 KB)","url":"https://s3.amazonaws.com/openneuro/ds000245/ds000245_R1.0.0/uncompressed/derivatives/mriqc/reports/T1w_group.html","revision":"1.0.0"},{"title":"Full dataset (2.6 GB)","url":"https://s3.amazonaws.com/openneuro/ds000245/ds000245_R1.0.0/compressed/ds000245_R1.0.0.zip","revision":"1.0.0"}],"contacts":[{"email":"ebagarinao@met.nagoya-u.ac.jp","name":"Epifanio Bagarinao","website":""}]}]