We present a new data release that enables comparative studies of the representation of musical genres (spectrum, timbre, vocal content) with ultra high-field, high resolution fMRI data of the studyforrest participants. Importantly, the same landmark-based procedure for automatic slice positioning that was used to align the scanner field-of-view between acquisition sessions, was used again to align the field-of-view for this acquisition with the one used for the previously released audio-movie data. In conjunction with the previous data releases, these new data will further expand the continuum of research question that can be approached with the joint dataset. For example, the development of encoding models for cortical representations of music in complex auditory stimuli (the audio-movie contains several dozen musical excerpts from a broad range of genres). To this end, we include extracted audio features that represent the time-frequency information of each stimulus in four different views. The views are mapped to different perceptually-motivated scales (mel and decibel scales) and via a decorrelating linear transformation (DCT-II). Source code for the implementation of the stimulation paradigm and audio feature extraction are included in the data release. We hope that providing these data will catalyze discoveries of auditory stimulus codes in neural populations.

music stimulus spectrograms

Spectrograms for all 25 stimuli showing structural differences in the time-frequency characteristics of the five musical genres. Each stimulus was a six second excerpt from the middle of a distinct musical piece. Excerpts were normalized so that their root-mean-square power values were equal, and a 50 ms quarter-sine ramp was applied at the start and end of each excerpt to suppress transients. Most prominent are the differences between music clips with and without vocal components.

A detailed data descriptor has been published in F1000Research. The data itself is also availabled for download from openfmri.org. Moreover, it has been integrated with the full studyforrest dataset (for data access see the overview).


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). Work on the data-sharing technology employed for this research was supported by US-German CRCNS project awarded to Yaroslav O. Halchenko and Michael Hanke, co-funded by the BMBF and the US National Science Foundation (BMBF 01GQ1411; NSF 1429999). Michael Hanke was supported by funds from the German federal state of Saxony-Anhalt, Project: Center for Behavioral Brain Sciences.


Hanke M., Dinga R., Häusler C., Guntupalli, J. S., Casey, M., Kaule, F. R. & Stadler, J. (2015). High-resolution 7-Tesla fMRI data on the perception of musical genres – an extension to the studyforrest dataset. F1000Research, 4:174.