title: Classifying instantaneous cognitive states from FMRI data.
abstract: We consider the problem of detecting the instantaneous cognitive state of a human subject based on their observed functional Magnetic Resonance Imaging (fMRI) data. Whereas fMRI has been widely used to determine average activation in different brain regions, our problem of automatically decoding instantaneous cognitive states has received little attention. This problem is relevant to diagnosing cognitive processes in neurologically normal and abnormal subjects. We describe a machine learning approach to this problem, and report on its successful use for discriminating cognitive states such as observing a picture versus reading a sentence, and reading a word about people versus reading a word about buildings.
authors.affiliation: Carnegie Mellon University, Computer Science, Pittsburgh, PA 15213, USA.
authors.forename: Tom M
authors.initials: TM
authors.lastname: Mitchell
authors.forename: Rebecca
authors.initials: R
authors.lastname: Hutchinson
authors.forename: Marcel A
authors.initials: MA
authors.lastname: Just
authors.forename: Radu S
authors.initials: RS
authors.lastname: Niculescu
authors.forename: Francisco
authors.initials: F
authors.lastname: Pereira
authors.forename: Xuerui
authors.initials: X
authors.lastname: Wang
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000378:metabolism; D003071:Cognition / Q000145:classification*; D003072:Cognition Disorders / Q000175:diagnosis; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D011932:Reading