Skip to main content
PMC home page
Neuroscience Bulletin logoLink to Neuroscience Bulletin
. 2012 Aug 7;28(4):399–408. doi: 10.1007/s12264-012-1253-3

Recent developments in multivariate pattern analysis for functional MRI

Zhi Yang 1,✉, Fang Fang 2,3, Xuchu Weng 2
PMCID: PMC5561894  PMID: 22833038

Abstract

Multivariate pattern analysis (MVPA) is a recently-developed approach for functional magnetic resonance imaging (fMRI) data analyses. Compared with the traditional univariate methods, MVPA is more sensitive to subtle changes in multivariate patterns in fMRI data. In this review, we introduce several significant advances in MVPA applications and summarize various combinations of algorithms and parameters in different problem settings. The limitations of MVPA and some critical questions that need to be addressed in future research are also discussed.

Keywords: multivariate analysis, fMRI, pattern recognition, computational biology

References

  • [1].Haxby J.V., Gobbini M.I., Furey M.L., Ishai A., Schouten J.L., Pietrini P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science. 2001;293:2425–2430. doi: 10.1126/science.1063736. [DOI] [PubMed] [Google Scholar]
  • [2].Haynes J.D., Rees G. Decoding mental states from brain activity in humans. Nat Rev Neurosci. 2006;7:523–534. doi: 10.1038/nrn1931. [DOI] [PubMed] [Google Scholar]
  • [3].Kriegeskorte N., Goebel R., Bandettini P. Information-based functional brain mapping. Proc Natl Acad Sci U S A. 2006;103:3863–3868. doi: 10.1073/pnas.0600244103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].O’Toole A.J., Jiang F., Abdi H., Penard N., Dunlop J.P., Parent M.A. Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. J Cogn Neurosci. 2007;19:1735–1752. doi: 10.1162/jocn.2007.19.11.1735. [DOI] [PubMed] [Google Scholar]
  • [5].Haynes J.D. Decoding and predicting intentions. Ann N Y Acad Sci. 2011;1224:9–21. doi: 10.1111/j.1749-6632.2011.05994.x. [DOI] [PubMed] [Google Scholar]
  • [6].Kriegeskorte N. Pattern-information analysis: from stimulus decoding to computational-model testing. Neuroimage. 2011;56:411–421. doi: 10.1016/j.neuroimage.2011.01.061. [DOI] [PubMed] [Google Scholar]
  • [7].Cox D.D., Savoy R.L. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage. 2003;19:261–270. doi: 10.1016/S1053-8119(03)00049-1. [DOI] [PubMed] [Google Scholar]
  • [8].Bandettini P.A. What’s new in neuroimaging methods? Ann N Y Acad Sci. 2009;1156:260–293. doi: 10.1111/j.1749-6632.2009.04420.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Mur M., Bandettini P.A., Kriegeskorte N. Revealing representational content with pattern-information fMRI—an introductory guide. Soc Cogn Affect Neurosci. 2009;4:101–109. doi: 10.1093/scan/nsn044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [10].Kamitani Y., Tong F. Decoding the visual and subjective contents of the human brain. Nat Neurosci. 2005;8:679–685. doi: 10.1038/nn1444. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Friston K.J. Modalities, modes, and models in functional neuroimaging. Science. 2009;326:399–403. doi: 10.1126/science.1174521. [DOI] [PubMed] [Google Scholar]
  • [12].Kravitz D.J., Kriegeskorte N., Baker C.I. High-level visual object representations are constrained by position. Cereb Cortex. 2010;20:2916–2925. doi: 10.1093/cercor/bhq042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Mur M., Ruff D.A., Bodurka J., Bandettini P.A., Kriegeskorte N. Faceidentity change activation outside the face system: “release from adaptation” may not always indicate neuronal selectivity. Cereb Cortex. 2010;20:2027–2042. doi: 10.1093/cercor/bhp272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Chang K.M., Mitchell T., Just M.A. Quantitative modeling of the neural representation of objects: how semantic feature norms can account for fMRI activation. Neuroimage. 2011;56:716–727. doi: 10.1016/j.neuroimage.2010.04.271. [DOI] [PubMed] [Google Scholar]
  • [15].Corradi-Dell’Acqua C., Hofstetter C., Vuilleumier P. Felt and seen pain evoke the same local patterns of cortical activity in insular and cingulate cortex. J Neurosci. 2011;31:17996–18006. doi: 10.1523/JNEUROSCI.2686-11.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Alink A., Euler F., Kriegeskorte N., Singer W., Kohler A. Auditory motion direction encoding in auditory cortex and high-level visual cortex. Hum Brain Mapp. 2012;33:969–978. doi: 10.1002/hbm.21263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Xu G., Jiang Y., Ma L., Yang Z., Weng X. Similar spatial patterns of neural coding of category selectivity in FFA and VWFA under different attention conditions. Neuropsychologia. 2012;50:862–868. doi: 10.1016/j.neuropsychologia.2012.01.026. [DOI] [PubMed] [Google Scholar]
  • [18].Chadwick M.J., Hassabis D., Weiskopf N., Maguire E.A. Decoding individual episodic memory traces in the human hippocampus. Curr Biol. 2010;20:544–547. doi: 10.1016/j.cub.2010.01.053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Mayhew S.D., Li S., Storrar J.K., Tsvetanov K.A., Kourtzi Z. Learning shapes the representation of visual categories in the aging human brain. J Cogn Neurosci. 2010;22:2899–2912. doi: 10.1162/jocn.2010.21415. [DOI] [PubMed] [Google Scholar]
  • [20].Schultz J. Brain imaging: decoding your memories. Curr Biol. 2010;20:R269–271. doi: 10.1016/j.cub.2010.02.001. [DOI] [PubMed] [Google Scholar]
  • [21].Kahnt T., Heinzle J., Park S.Q., Haynes J.D. Decoding the formation of reward predictions across learning. J Neurosci. 2011;31:14624–14630. doi: 10.1523/JNEUROSCI.3412-11.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Herrmann B., Obleser J., Kalberlah C., Haynes J.D., Friederici A.D. Dissociable neural imprints of perception and grammar in auditory functional imaging. Hum Brain Mapp. 2012;33:584–595. doi: 10.1002/hbm.21235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Haynes J.D., Sakai K., Rees G., Gilbert S., Frith C., Passingham R.E. Reading hidden intentions in the human brain. Curr Biol. 2007;17:323–328. doi: 10.1016/j.cub.2006.11.072. [DOI] [PubMed] [Google Scholar]
  • [24].Soon C.S., Brass M., Heinze H.J., Haynes J.D. Unconscious determinants of free decisions in the human brain. Nat Neurosci. 2008;11:543–545. doi: 10.1038/nn.2112. [DOI] [PubMed] [Google Scholar]
  • [25].Bode S., He A.H., Soon C.S., Trampel R., Turner R., Haynes J.D. Tracking the unconscious generation of free decisions using ultra-high field fMRI. PLoS One. 2011;6:e21612. doi: 10.1371/journal.pone.0021612. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Kahnt T., Heinzle J., Park S.Q., Haynes J.D. Decoding different roles for vmPFC and dlPFC in multi-attribute decision making. Neuroimage. 2011;56:709–715. doi: 10.1016/j.neuroimage.2010.05.058. [DOI] [PubMed] [Google Scholar]
  • [27].Ethofer T., Van De Ville D., Scherer K., Vuilleumier P. Decoding of emotional information in voice-sensitive cortices. Curr Biol. 2009;19:1028–1033. doi: 10.1016/j.cub.2009.04.054. [DOI] [PubMed] [Google Scholar]
  • [28].Lin D., Boyle M.P., Dollar P., Lee H., Lein E.S., Perona P., et al. Functional identification of an aggression locus in the mouse hypothalamus. Nature. 2011;470:221–226. doi: 10.1038/nature09736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Gomez A., Rothkirch M., Kaul C., Weygandt M., Haynes J.D., Rees G., et al. Emotion modulates the effects of endogenous attention on retinotopic visual processing. Neuroimage. 2011;57:1542–1551. doi: 10.1016/j.neuroimage.2011.05.072. [DOI] [PubMed] [Google Scholar]
  • [30].Kotz SA, Kalberlah C, Bahlmann J, Friederici AD, Haynes JD. Predicting vocal emotion expressions from the human brain. Hum Brain Mapp 2012. doi: 10.1002/hbm.22041. [DOI] [PMC free article] [PubMed]
  • [31].Engels A.S., Heller W., Spielberg J.M., Warren S.L., Sutton B.P., Banich M.T., et al. Co-occurring anxiety influences patterns of brain activity in depression. Cogn Affect Behav Neurosci. 2010;10:141–156. doi: 10.3758/CABN.10.1.141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Hamilton J.P., Chen G., Thomason M.E., Schwartz M.E., Gotlib I.H. Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. Mol Psychiatry. 2011;16:763–772. doi: 10.1038/mp.2010.46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Hoeft F., McCandliss B.D., Black J.M., Gantman A., Zakerani N., Hulme C., et al. Neural systems predicting long-term outcome in dyslexia. Proc Natl Acad Sci U S A. 2011;108:361–366. doi: 10.1073/pnas.1008950108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Pereira F., Mitchell T., Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage. 2009;45:S199–209. doi: 10.1016/j.neuroimage.2008.11.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Hassabis D., Chu C., Rees G., Weiskopf N., Molyneux P.D., Maguire E.A. Decoding neuronal ensembles in the human hippocampus. Curr Biol. 2009;19:546–554. doi: 10.1016/j.cub.2009.02.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Kriegeskorte N., Simmons W.K., Bellgowan P.S., Baker C.I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci. 2009;12:535–540. doi: 10.1038/nn.2303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Dosenbach N.U., Nardos B., Cohen A.L., Fair D.A., Power J.D., Church J.A., et al. Prediction of individual brain maturity using fMRI. Science. 2010;329:1358–1361. doi: 10.1126/science.1194144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Craddock R.C., Holtzheimer P.E., 3rd, Hu X.P., Mayberg H.S. Disease state prediction from resting state functional connectivity. Magn Reson Med. 2009;62:1619–1628. doi: 10.1002/mrm.22159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Shen H., Wang L., Liu Y., Hu D. Discriminative analysis of restingstate functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage. 2010;49:3110–3121. doi: 10.1016/j.neuroimage.2009.11.011. [DOI] [PubMed] [Google Scholar]
  • [40].Fu C.H., Mourao-Miranda J., Costafreda S.G., Khanna A., Marquand A.F., Williams S.C., et al. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry. 2008;63:656–662. doi: 10.1016/j.biopsych.2007.08.020. [DOI] [PubMed] [Google Scholar]
  • [41].Zeng L.L., Shen H., Liu L., Wang L.B., Li B.J., Fang P., et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain. 2012;135:1498–1507. doi: 10.1093/brain/aws059. [DOI] [PubMed] [Google Scholar]
  • [42].Oquendo MA, Baca-Garcia E, Artes-Rodriguez A, Perez-Cruz F, Galfalvy HC, Blasco-Fontecilla H, et al. Machine learning and data mining: strategies for hypothesis generation. Mol Psychiatry 2012 doi: 10.1038/mp.2011.173. [DOI] [PubMed]
  • [43].Thirion B., Duchesnay E., Hubbard E., Dubois J., Poline J.B., Lebihan D., et al. Inverse retinotopy: inferring the visual content of images from brain activation patterns. Neuroimage. 2006;33:1104–1116. doi: 10.1016/j.neuroimage.2006.06.062. [DOI] [PubMed] [Google Scholar]
  • [44].Kay K.N., Naselaris T., Prenger R.J., Gallant J.L. Identifying natural images from human brain activity. Nature. 2008;452:352–355. doi: 10.1038/nature06713. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [45].Daugman J.G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am A. 1985;2:1160–1169. doi: 10.1364/JOSAA.2.001160. [DOI] [PubMed] [Google Scholar]
  • [46].Jones J.P., Palmer L.A. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol. 1987;58:1233–1258. doi: 10.1152/jn.1987.58.6.1233. [DOI] [PubMed] [Google Scholar]
  • [47].Miyawaki Y., Uchida H., Yamashita O., Sato M.A., Morito Y., Tanabe H.C., et al. Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron. 2008;60:915–929. doi: 10.1016/j.neuron.2008.11.004. [DOI] [PubMed] [Google Scholar]
  • [48].Mitchell T.M., Shinkareva S.V., Carlson A., Chang K.M., Malave V.L., Mason R.A., et al. Predicting human brain activity associated with the meanings of nouns. Science. 2008;320:1191–1195. doi: 10.1126/science.1152876. [DOI] [PubMed] [Google Scholar]
  • [49].Naselaris T., Prenger R.J., Kay K.N., Oliver M., Gallant J.L. Bayesian reconstruction of natural images from human brain activity. Neuron. 2009;63:902–915. doi: 10.1016/j.neuron.2009.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [50].Pereira F., Detre G., Botvinick M. Generating text from functional brain images. Front Hum Neurosci. 2011;5:72. doi: 10.3389/fnhum.2011.00072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [51].Meyer K., Kaplan J.T., Essex R., Webber C., Damasio H., Damasio A. Predicting visual stimuli on the basis of activity in auditory cortices. Nat Neurosci. 2010;13:667–668. doi: 10.1038/nn.2533. [DOI] [PubMed] [Google Scholar]
  • [52].Knops A., Thirion B., Hubbard E.M., Michel V., Dehaene S. Recruitment of an area involved in eye movements during mental arithmetic. Science. 2009;324:1583–1585. doi: 10.1126/science.1171599. [DOI] [PubMed] [Google Scholar]
  • [53].Bai J., Shi J., Jiang Y., He S., Weng X. Chinese and Korean characters engage the same visual word form area in proficient early Chinese-Korean bilinguals. PLoS one. 2011;6:e22765. doi: 10.1371/journal.pone.0022765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [54].Reddy L., Kanwisher N. Category selectivity in the ventral visual pathway confers robustness to clutter and diverted attention. Curr Biol. 2007;17:2067–2072. doi: 10.1016/j.cub.2007.10.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [55].Kriegeskorte N., Bandettini P. Analyzing for information, not activation, to exploit high-resolution fMRI. Neuroimage. 2007;38:649–662. doi: 10.1016/j.neuroimage.2007.02.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [56].Kriegeskorte N., Bandettini P. Combining the tools: activation- and information-based fMRI analysis. Neuroimage. 2007;38:666–668. doi: 10.1016/j.neuroimage.2007.06.030. [DOI] [PubMed] [Google Scholar]
  • [57].Friman O., Borga M., Lundberg P., Knutsson H. Detection of neural activity in fMRI using maximum correlation modeling. Neuroimage. 2002;15:386–395. doi: 10.1006/nimg.2001.0972. [DOI] [PubMed] [Google Scholar]
  • [58].Momennejad I., Haynes J.D. Human anterior prefrontal cortex encodes the ‘what’ and ‘when’ of future intentions. Neuroimage. 2012;61:139–148. doi: 10.1016/j.neuroimage.2012.02.079. [DOI] [PubMed] [Google Scholar]
  • [59].Bode S., Bogler C., Soon C.S., Haynes J.D. The neural encoding of guesses in the human brain. Neuroimage. 2012;59:1924–1931. doi: 10.1016/j.neuroimage.2011.08.106. [DOI] [PubMed] [Google Scholar]
  • [60].Carlin J.D., Rowe J.B., Kriegeskorte N., Thompson R., Calder A.J. Direction-sensitive codes for observed head turns in human superior temporal sulcus. Cereb Cortex. 2012;22:735–744. doi: 10.1093/cercor/bhr061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [61].Gilbert S.J. Decoding the content of delayed intentions. J Neurosci. 2011;31:2888–2894. doi: 10.1523/JNEUROSCI.5336-10.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [62].Bogler C., Bode S., Haynes J.D. Decoding successive computational stages of saliency processing. Curr Biol. 2011;21:1667–1671. doi: 10.1016/j.cub.2011.08.039. [DOI] [PubMed] [Google Scholar]
  • [63].Reverberi C., Gorgen K., Haynes J.D. Compositionality of rule representations in human prefrontal cortex. Cereb Cortex. 2012;22:1237–1246. doi: 10.1093/cercor/bhr200. [DOI] [PubMed] [Google Scholar]
  • [64].Weygandt M., Blecker C.R., Schafer A., Hackmack K., Haynes J.D., Vaitl D., et al. fMRI pattern recognition in obsessive-compulsive disorder. Neuroimage. 2012;60:1186–1193. doi: 10.1016/j.neuroimage.2012.01.064. [DOI] [PubMed] [Google Scholar]
  • [65].Power J.D., Barnes K.A., Snyder A.Z., Schlaggar B.L., Petersen S.E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59:2142–2154. doi: 10.1016/j.neuroimage.2011.10.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [66].Kriegeskorte N., Mur M., Bandettini P. Representational similarity analysis — connecting the branches of systems neuroscience. Front Syst Neurosci. 2008;2:4. doi: 10.3389/neuro.01.016.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [67].Liu T., Hospadaruk L., Zhu D.C., Gardner J.L. Feature-specific attentional priority signals in human cortex. J Neurosci. 2011;31:4484–4495. doi: 10.1523/JNEUROSCI.5745-10.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [68].Cichy R.M., Heinzle J., Haynes J.D. Imagery and perception share cortical representations of content and location. Cereb Cortex. 2012;22:372–380. doi: 10.1093/cercor/bhr106. [DOI] [PubMed] [Google Scholar]
  • [69].Cichy RM, Sterzer P, Heinzle J, Elliott LT, Ramirez F, Haynes JD. Probing principles of large-scale object representation: Category preference and location encoding. Hum Brain Mapp 2012. doi: 10.1002/hbm.22020. [DOI] [PMC free article] [PubMed]
  • [70].Sun D., van Erp T.G., Thompson P.M., Bearden C.E., Daley M., Kushan L., et al. Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine learning algorithms. Biol Psychiatry. 2009;66:1055–1060. doi: 10.1016/j.biopsych.2009.07.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [71].Koutsouleris N., Meisenzahl E.M., Davatzikos C., Bottlender R., Frodl T., Scheuerecker J., et al. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry. 2009;66:700–712. doi: 10.1001/archgenpsychiatry.2009.62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [72].Kamitani Y., Tong F. Decoding seen and attended motion directions from activity in the human visual cortex. Curr Biol. 2006;16:1096–1102. doi: 10.1016/j.cub.2006.04.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [73].Freeman J., Brouwer G.J., Heeger D.J., Merriam E.P. Orientation decoding depends on maps, not columns. J Neurosci. 2011;31:4792–4804. doi: 10.1523/JNEUROSCI.5160-10.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [74].Poldrack R.A. Can cognitive processes be inferred from neuroimaging data? Trends in cognitive sciences. 2006;10:59–63. doi: 10.1016/j.tics.2005.12.004. [DOI] [PubMed] [Google Scholar]
  • [75].Poldrack R.A. Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron. 2011;72:692–697. doi: 10.1016/j.neuron.2011.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [76].Bartels A., Logothetis N.K., Moutoussis K. fMRI and its interpretations: an illustration on directional selectivity in area V5/MT. Trends Neurosci. 2008;31:444–453. doi: 10.1016/j.tins.2008.06.004. [DOI] [PubMed] [Google Scholar]
  • [77].Logothetis N.K. What we can do and what we cannot do with fMRI. Nature. 2008;453:869–878. doi: 10.1038/nature06976. [DOI] [PubMed] [Google Scholar]

Articles from Neuroscience Bulletin are provided here courtesy of Springer

RESOURCES