High-resolution 7-Tesla fMRI data on the perception of musical

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.

[1]  Michael Hanke paper-f1000_pandora_data: Initial submission , 2015 .

[2]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[3]  Michael A. Casey,et al.  Population Codes Representing Musical Timbre for High-Level fMRI Categorization of Music Genres , 2011, MLINI.

[4]  Daniel S. Margulies,et al.  NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain , 2014, bioRxiv.

[5]  Vinoo Alluri,et al.  Capturing the musical brain with Lasso: Dynamic decoding of musical features from fMRI data , 2014, NeuroImage.

[6]  Oliver Speck,et al.  A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie , 2014, Scientific Data.

[7]  Meinard Müller,et al.  Making chroma features more robust to timbre changes , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Yi Chen,et al.  Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control , 2011, NeuroImage.

[9]  Stefan Pollmann,et al.  Neuroinformatics Original Research Article Pymvpa: a Unifying Approach to the Analysis of Neuroscientifi C Data , 2022 .

[10]  Yaroslav O. Halchenko,et al.  Open is Not Enough. Let's Take the Next Step: An Integrated, Community-Driven Computing Platform for Neuroscience , 2012, Front. Neuroinform..

[11]  Marcelo Gomes Mattar,et al.  de Bruijn cycles for neural decoding , 2011, NeuroImage.

[12]  Katrin Krumbholz,et al.  Hierarchical processing of sound location and motion in the human brainstem and planum temporale , 2005, The European journal of neuroscience.

[13]  Michael Hanke,et al.  Portrayed emotions in the movie "Forrest Gump" , 2015, F1000Research.

[14]  Jonathan W. Peirce,et al.  PsychoPy—Psychophysics software in Python , 2007, Journal of Neuroscience Methods.

[15]  Xavier Serra,et al.  ESSENTIA: an open-source library for sound and music analysis , 2013, ACM Multimedia.

[16]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.

[17]  Michael Hanke,et al.  A communication hub for a decentralized collaboration on studying real-life cognition , 2015, F1000Research.

[18]  Bryan R. Conroy,et al.  A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex , 2011, Neuron.

[19]  Beth Logan,et al.  A music similarity function based on signal analysis , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[20]  Myung-Ho In,et al.  Highly accelerated PSF-mapping for EPI distortion correction with improved fidelity , 2012, Magnetic Resonance Materials in Physics, Biology and Medicine.