Prediction of Successful Memory Encoding from fMRI Data.
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.
Balci
S K
SK
CSAIL, MIT, Cambridge, MA, USA.
Sabuncu
M R
MR
Yoo
J
J
Ghosh
S S
SS
Whitfield-Gabrieli
S
S
Gabrieli
J D E
JD
Golland
P
P
Neuroimage. 2005 Jun;26(2):317-29
15907293
Science. 2005 Dec 23;310(5756):1963-6
16373577
J Cogn Neurosci. 2007 Nov;19(11):1735-52
17958478
Trends Cogn Sci. 2007 Aug;11(8):356-65
17618161
Science. 2001 Sep 28;293(5539):2425-30
11577229
Trends Cogn Sci. 2006 Sep;10(9):424-30
16899397
Neuroimage. 2007 Oct 1;37(4):1250-9
17686634
Neuroimage. 2005 Dec;28(4):980-95
16275139
Neuroimage. 2005 Nov 15;28(3):663-8
16169252
J Cogn Neurosci. 2005 Apr;17(4):580-90
15829079
Neuroimage. 2003 Jun;19(2 Pt 1):261-70
12814577
Proc Natl Acad Sci U S A. 2006 Mar 7;103(10):3863-8
16537458
Neuroimage. 2002 Apr;15(4):747-71
11906218
Nat Neurosci. 2005 May;8(5):679-85
15852014
Neuroimage. 2006 Jul 1;31(3):1129-41
16529955
Nat Rev Neurosci. 2006 Jul;7(7):523-34
16791142
Nat Neurosci. 2005 Dec;8(12):1776-83
16286927
Neuroimage. 2008 Jan 1;39(1):181-205
17919928