title: Prediction of Successful Memory Encoding from fMRI Data.
abstract: In this work, we explore the use of classification algorithms in predicting mental states from functional neuroimaging data. We train a linear support vector machine classifier to characterize spatial fMRI activation patterns. We employ a general linear model based feature extraction method and use the t-test for feature selection. We evaluate our method on a memory encoding task, using participants' subjective prediction about learning as a benchmark for our classifier. We show that the classifier achieves better than random predictions and the average accuracy is close to subject's own prediction performance. In addition, we validate our tool on a simple motor task where we demonstrate an average prediction accuracy of over 90%. Our experiments demonstrate that the classifier performance depends significantly on the complexity of the experimental design and the mental process of interest.
authors.affiliation: CSAIL, MIT, Cambridge, MA, USA.
authors.forename: S K
authors.initials: SK
authors.lastname: Balci
authors.forename: M R
authors.initials: MR
authors.lastname: Sabuncu
authors.forename: J
authors.initials: J
authors.lastname: Yoo
authors.forename: S S
authors.initials: SS
authors.lastname: Ghosh
authors.forename: S
authors.initials: S
authors.lastname: Whitfield-Gabrieli
authors.forename: J D E
authors.initials: JD
authors.lastname: Gabrieli
authors.forename: P
authors.initials: P
authors.lastname: Golland
references.citation: Neuroimage. 2005 Jun;26(2):317-29
references.pmid: 15907293
references.citation: Science. 2005 Dec 23;310(5756):1963-6
references.pmid: 16373577
references.citation: J Cogn Neurosci. 2007 Nov;19(11):1735-52
references.pmid: 17958478
references.citation: Trends Cogn Sci. 2007 Aug;11(8):356-65
references.pmid: 17618161
references.citation: Science. 2001 Sep 28;293(5539):2425-30
references.pmid: 11577229
references.citation: Trends Cogn Sci. 2006 Sep;10(9):424-30
references.pmid: 16899397
references.citation: Neuroimage. 2007 Oct 1;37(4):1250-9
references.pmid: 17686634
references.citation: Neuroimage. 2005 Dec;28(4):980-95
references.pmid: 16275139
references.citation: Neuroimage. 2005 Nov 15;28(3):663-8
references.pmid: 16169252
references.citation: J Cogn Neurosci. 2005 Apr;17(4):580-90
references.pmid: 15829079
references.citation: Neuroimage. 2003 Jun;19(2 Pt 1):261-70
references.pmid: 12814577
references.citation: Proc Natl Acad Sci U S A. 2006 Mar 7;103(10):3863-8
references.pmid: 16537458
references.citation: Neuroimage. 2002 Apr;15(4):747-71
references.pmid: 11906218
references.citation: Nat Neurosci. 2005 May;8(5):679-85
references.pmid: 15852014
references.citation: Neuroimage. 2006 Jul 1;31(3):1129-41
references.pmid: 16529955
references.citation: Nat Rev Neurosci. 2006 Jul;7(7):523-34
references.pmid: 16791142
references.citation: Nat Neurosci. 2005 Dec;8(12):1776-83
references.pmid: 16286927
references.citation: Neuroimage. 2008 Jan 1;39(1):181-205
references.pmid: 17919928