Description: https://doi.org/10.1101/2021.04.04.438425 Here, reproducible evidence is presented that a high-dimensional, brain-wide multivariate linear method (SVM) can better detect and characterize the occurrence of visual and socio-affective states in a task-oriented fMRI experiment; in comparison to the classical localizationist mass-univariate analysis (GLM). Classification models for a group of human participants and existing rigorous cluster inference methods (TFCE) are used to construct group anatomical-statistical parametric maps, which correspond to the most likely neural correlates of each psychological state. Anatomical consistency of discriminating features across subjects and contrasts despite of the high number of dimensions, as well as agreement with the wider literature, suggest MVPA is a viable tool for full-brain functional neuroanatomical mapping.
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