Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification.

ABSTRACT: Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the vox

SUBMITTER: Nawa NE 

PROVIDER: S-EPMC4688375 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature