High-resolution 7-Tesla fMRI data on the perception of musical genres
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.
- Michael Hanke
- Richard Dinga
- Christian Häusler
- J. Swaroop Guntupalli
- Michael Casey
- Falko R. Kaule
- Jörg Stadler
Acknowledgements and Funding:
This research was supported by the German Federal Ministry of Education and Research (BMBF) as part of a US-German collaboration in computational neuroscience (CRCNS; awarded to James Haxby, Peter Ramadge, and Michael Hanke), co-funded by the BMBF and the US National Science Foundation (BMBF 01GQ1112; NSF 1129855). Michael Hanke was supported by funds from the German federal state of Saxony-Anhalt, Project: Center for Behavioral Brain Sciences.
7 Tesla Siemens MAGNETOM
How to cite this dataset:
In addition to any citation requirements in the dataset summary please use the following to cite this dataset:
This data was obtained from the OpenfMRI database. Its accession number is ds000113b
Direct Links to data:
Revision: 2.0.1 Date Set: Nov. 9, 2016, 11:30 p.m.
- Added authors string to dataset_description.json
Revision: 2.0.0 Date Set: Oct. 3, 2016, 7:53 p.m.
- Converted to BIDS standard