InterTVA. Single-trial beta maps for event-related voice localizer.

The InterTVA dataset has been acquired with two main objectives. First, from a neuroscientific perspective, it aims at studying the inter-individual differences observed in people's ability at performing voice perception and voice identification tasks. Secondly, from a methodological perspective, it should allow benchmarking multi-view machine learning methods. Indeed, it includes several MRI modalities: anatomical MRI, diffusion MRI and several sessions of functional MRI -- one resting state run, one event-related voice localizer run (passive listening of vocal and non-vocal sounds), and four runs during which the subject performed a voice identification task. The present dataset contains a pre-processed version of the data acquired during the event-related voice localizer run. A GLM was performed using one regressor for each of the 144 trials, during each of which the participant was passively listening to a vocal or non-vocal stimulus. We therefore provide 144 beta maps for 39 subjects out of the 40 participants (one was excluded for excessive motion). This allows performing group-level MVPA using an inter-subject pattern analysis (ISPA) approach, as described in the following paper: Q. Wang, B. Cagna, T. Chaminade, et S. Takerkart, « Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA », <em>NeuroImage</em>, vol. 204, p. 116205, janv. 2020. https://doi.org/10.1016/j.neuroimage.2019.116205 The source code to perform ISPA is also available: http://www.github.com/SylvainTakerkart/inter_subject_pattern_analysis If you use this data, please cite the following paper which describes it exhaustively: V. Aglieri, B. Cagna, P. Belin, S. Takerkart, "Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis", Data in Brief, vol. 29, p. 105170, April 2020. https://doi.org/10.1016/j.dib.2020.105170