High-resolution 7-Tesla fMRI data on the perception of musical genres – an extension to the <i>studyforrest</i> dataset. Hanke, M., Dinga, R., Häusler, C., Guntupalli, J. S., Casey, M., Kaule, F. R., & Stadler, J. Technical Report 4:174, F1000Research, June, 2015. Type: article![High-resolution 7-Tesla fMRI data on the perception of musical genres – an extension to the <i>studyforrest</i> dataset [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Here we present an extension to the studyforrest dataset – a versatile resource for studying the behavior of the human brain in situations of real-life complexity ( http://studyforrest.org ). This release adds more high-resolution, ultra high-field (7 Tesla) functional magnetic resonance imaging (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 precomputed 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, are provided. An initial quality control analysis reveals distinguishable patterns of response to individual genres throughout a large expanse of areas known to be involved in auditory and speech processing. The present data can be used to, for example, generate encoding models for music perception that can be validated against the previously released fMRI data from stimulation with the “Forrest Gump” audio-movie and its rich musical content. In order to facilitate replicative and derived works, only free and open-source software was utilized.
@techreport{hanke_high-resolution_2015,
title = {High-resolution 7-{Tesla} {fMRI} data on the perception of musical genres –\ an extension to the \textit{studyforrest} dataset},
copyright = {http://creativecommons.org/licenses/by/4.0/},
url = {https://f1000research.com/articles/4-174},
abstract = {Here we present an extension to the studyforrest dataset – a versatile resource\ for studying the behavior of the human brain in situations of real-life complexity\ ( http://studyforrest.org ). This release adds more high-resolution, ultra high-field\ (7 Tesla) functional magnetic resonance imaging (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 precomputed\ 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, are provided. An initial\ quality control analysis reveals distinguishable patterns of response to individual\ genres throughout a large expanse of areas known to be involved in auditory and\ speech processing. The present data can be used to, for example, generate encoding\ models for music perception that can be validated against the previously\ released fMRI data from stimulation with the “Forrest Gump” audio-movie and its\ rich musical content. In order to facilitate replicative and derived works, only free\ and open-source software was utilized.},
language = {en},
number = {4:174},
urldate = {2021-11-16},
institution = {F1000Research},
author = {Hanke, Michael and Dinga, Richard and Häusler, Christian and Guntupalli, J. Swaroop and Casey, Michael and Kaule, Falko R. and Stadler, Jörg},
month = jun,
year = {2015},
doi = {10.12688/f1000research.6679.1},
note = {Type: article},
keywords = {7 Tesla, auditory features, functional magnetic resonance imaging, music perception, natural sounds},
}
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