title: Multivoxel pattern analysis for FMRI data: a review.
abstract: Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves.
authors: Université Mohammed V-Agdal, Rabat, Morocco. abdelhak.mahmoudi@gmail.com
authors: Abdelhak
authors: A
authors: Mahmoudi
authors: Sylvain
authors: S
authors: Takerkart
authors: Fakhita
authors: F
authors: Regragui
authors: Driss
authors: D
authors: Boussaoud
authors: Andrea
authors: A
authors: Brovelli
mesh_terms: D019540:Area Under Curve; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D007700:Kinetics; D016018:Least-Squares Analysis; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D009474:Neurons / Q000378:metabolism; D016011:Normal Distribution; D010100:Oxygen / Q000378:metabolism; D012372:ROC Curve; D060388:Support Vector Machine; D013997:Time Factors
chemical_list: D010100:Oxygen
references: Magn Reson Imaging. 2008 Sep;26(7):1007-14
references: 18691999
references: J Cereb Blood Flow Metab. 1991 Jul;11(4):690-9
references: 2050758
references: Trends Cogn Sci. 2006 Sep;10(9):424-30
references: 16899397
references: Proc Natl Acad Sci U S A. 1990 Dec;87(24):9868-72
references: 2124706
references: Neuroimage. 1999 Oct;10(4):385-96
references: 10493897
references: PLoS Comput Biol. 2011 Jun;7(6):e1002079
references: 21731479
references: Neuroimage. 2005 Jun;26(2):317-29
references: 15907293
references: Nat Neurosci. 2005 May;8(5):686-91
references: 15852013
references: Neuroimage. 2003 Jun;19(2 Pt 1):261-70
references: 12814577
references: Neuroimage. 2009 Jan 1;44(1):83-98
references: 18501637
references: Neuroimage. 2002 Apr;15(4):772-86
references: 11906219
references: Neuroimage. 2010 Oct 15;53(1):103-18
references: 20580933
references: Neuroimage. 2011 Mar 1;55(1):185-93
references: 21078400
references: J Neurosci. 2007 Jan 3;27(1):226-33
references: 17202490
references: Proc Natl Acad Sci U S A. 1992 Jun 15;89(12):5675-9
references: 1608978
references: Proc Natl Acad Sci U S A. 2006 Mar 7;103(10):3863-8
references: 16537458
references: Magn Reson Imaging. 2008 Sep;26(7):921-34
references: 18508219
references: Hum Brain Mapp. 1998;6(3):160-88
references: 9673671
references: Neuroimage. 2011 May 15;56(2):476-96
references: 20488249
references: Nat Neurosci. 2005 May;8(5):679-85
references: 15852014
references: J Cereb Blood Flow Metab. 1996 Jan;16(1):7-22
references: 8530558
references: Neural Netw. 2000 May-Jun;13(4-5):411-30
references: 10946390
references: Neuroimage. 1999 May;9(5):526-33
references: 10329292
references: Neuroimage. 2010 Feb 1;49(3):1943-8
references: 19285144
references: Hum Brain Mapp. 1996;4(2):93-102
references: 20408189
references: J Neurosci. 2010 Jan 6;30(1):325-30
references: 20053913
references: Neuroimage. 2010 Feb 1;49(3):1949-52
references: 19559797
references: Science. 2001 Sep 28;293(5539):2425-30
references: 11577229
references: Neuroimage. 2004 Jun;22(2):492-502
references: 15193578
references: Inf Process Med Imaging. 2003 Jul;18:330-41
references: 15344469
references: Int J Biomed Imaging. 2011;2011:627947
references: 22046176
references: Neuroimage. 2010 Feb 15;49(4):3110-21
references: 19931396
references: Neural Comput. 2010 Nov;22(11):2729-62
references: 20804386
references: IEEE Eng Med Biol Mag. 2006 Mar-Apr;25(2):60-4
references: 16568938
references: Neuroimage. 2011 May 15;56(2):508-16
references: 20674749
references: Nature. 2001 Jul 12;412(6843):150-7
references: 11449264
references: Neuroimage. 2008 Oct 15;43(1):44-58
references: 18672070
references: Hum Brain Mapp. 2002 Jan;15(1):1-25
references: 11747097
references: Neuroimage. 2005 Dec;28(4):980-95
references: 16275139
references: Neuroimage. 2011 May 15;56(2):422-39
references: 20347998
references: Hum Brain Mapp. 2009 Jan;30(1):256-66
references: 18041738
references: Neuroinformatics. 2009 Spring;7(1):37-53
references: 19184561
references: Neuroimage. 2012 Mar;60(1):59-70
references: 22166797
references: Neuroimage. 2012 Mar;60(1):601-13
references: 22227886
references: Neuroimage. 2005 Nov 15;28(3):663-8
references: 16169252
references: Neural Comput. 2008 Feb;20(2):486-503
references: 18047411
references: Neuroimage. 2011 May 15;56(2):387-99
references: 21172442