A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

ABSTRACT: Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of th

SUBMITTER: Demanuele C 

PROVIDER: S-EPMC4617410 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature