Decoding the large-scale structure of brain function by classifying mental States across individuals.

ABSTRACT: Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes are organized in the brain. Using a variety of classifier techniques, we achieved cross-validated classification accuracy of 80% across individuals (chance = 13%). Using a neural network classifier, we recovered a low-dimensional representation common to all the cognitive-perceptual tasks in our data set

SUBMITTER: Poldrack RA 

PROVIDER: S-EPMC2935493 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature