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A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie

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's XNAT server.


Additional resources:

More information and updates are made available at:

Source code repository:

Documentation for the source code:


  • Jörg Stadler
  • Wolf Zinke
  • Oliver Speck
  • Stefan Pollmann
  • Falko R. Kaule
  • Pierre Ibe
  • Florian J. Baumgartner
  • Michael Hanke

Acknowledgements and Funding:

We are grateful to the authors of the German "Forrest Gump'' audio description that made this study possible and especially Bernd Benecke for his support. We also want to thank Schweizer Radio und Fernsehen and Paramount Home Entertainment Germany for their permission to use the movie and audio description for this study. Thanks also go to Andreas Fügner and Marko Dombach for their help with developing the audio stimulation equipment, Renate Körbs for helping with scanner operations. Furthermore, we thank Michael Casey for providing us with a questionnaire to assess musical background. Only open-source software was employed in this study. We thank their respective authors for making it publicly available. This research was funded 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). Michael Hanke was supported by funds from the German federal state of Saxony-Anhalt, Project: Center for Behavioral Brain Sciences.

External Publication Links:

A pre-print of the data descriptor manuscript is available here.

Sample Size:


Scanner Type:

7 Tesla Siemens MAGNETOM and 3 Tesla Philips Achieva



Accession Number:


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 ds000113