Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis

Virginia Aglieri; Bastien Cagna; Pascal Belin; Sylvain Takerkart

doi:10.1016/j.dib.2020.105170

Single-trial fMRI activation maps measured during the InterTVA event-related voice localizer. A data set ready for inter-subject pattern analysis

Data Brief. 2020 Jan 25:29:105170. doi: 10.1016/j.dib.2020.105170. eCollection 2020 Apr.

Authors

Virginia Aglieri¹, Bastien Cagna¹, Pascal Belin^{1

2}, Sylvain Takerkart¹

Affiliations

¹ Institut des Neurosciences de la Timone, UMR 7289, CNRS and Université Aix-Marseille, 13005 Marseille, France.
² International Laboratories for Brain, Music and Sound Research, Montreal, QC H2V 2S9, Canada.

Abstract

Multivariate pattern analysis (MVPA) of functional neuroimaging data has emerged as a key tool for studying the cognitive architecture of the human brain. At the group level, we have recently demonstrated the advantages of an under-exploited scheme that consists in training a machine learning model on data from a set of subjects and evaluating its generalization ability on data from unseen subjects (see Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA [1]). We here provide a data set that is fully ready to perform inter-subject pattern analysis, which includes 5616 single-trial brain activation maps recorded in 39 participants who were scanned using functional magnetic resonance imaging (fMRI) with a voice localizer paradigm. This data set should therefore reveal valuable for data scientists developing brain decoding algorithms as well as cognitive neuroscientists interested in voice perception.

Keywords: Functional magnetic resonance imaging (fMRI); Inter-subject pattern analysis (ISPA); Multivariate pattern analysis (MVPA); Single-trial betas; Voice localizer; Voice perception.