An introduction to anatomical ROI-based fMRI classification analysis

Brain Research

Volumn 1282, Issue , 2009, Pages 114-125

  Etzel, Joset A ^a   Gazzola, Valeria ^a   Keysers, Christian ^a  

^a UNIVERSITY OF GRONINGEN   (Netherlands)

Author keywords

Classification analysis; fMRI; Multivariate analysis; Multivoxel pattern analysis; Region of interest

Indexed keywords

ADULT; ANALYTIC METHOD; ARTICLE; AUDITORY CORTEX; BRAIN FUNCTION; BRAIN MAPPING; BRAIN REGION; CLASSIFICATION; CONTROLLED STUDY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; INFORMATION PROCESSING; MALE; MULTIVARIATE ANALYSIS; NERVE CELL; NEUROANATOMY; NEUROIMAGING; NORMAL HUMAN; PRIORITY JOURNAL; SOMATOSENSORY CORTEX; STIMULUS RESPONSE;

ALGORITHMS; AUDITORY CORTEX; BRAIN; BRAIN MAPPING; DATA INTERPRETATION, STATISTICAL; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MULTIVARIATE ANALYSIS; OBSERVER VARIATION; SOFTWARE; SOFTWARE VALIDATION;

EID: 67649666979     PISSN: 00068993     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.brainres.2009.05.090     Document Type: Article

References (71)

1. Breiman L. Statistical modeling: the two cultures. Stat. Sci. 16 (2001) 199-215
2. Breiman L. Random forests. Mach. Learn. 45 (2001) 5-32
3. Brett M., Johnsrude I.S., and Owen A.M. The problem of functional localization in the human brain. Nat. Rev. Neurosci. 3 (2002) 243-249
4. Buchel C., and Friston K.J. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb. Cortex 7 (1997) 768-778
5. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 (1998) 121-167
6. Carlson T.A., Schrater P., and He S. Patterns of activity in the categorical representations of objects. J. Cogn. Neurosci. 15 (2003) 704-717
7. Cox D.D., and Savoy R.L. Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19 (2003) 261-270
8. Davatzikos C., Ruparel K., Fan Y., Shen D.G., Acharyya M., Loughead J.W., Gur R.C., and Langleben D.D. Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Neuroimage 28 (2005) 663-668
9. De Martino F., Valente G., Staeren N., Ashburner J., Goebel R., and Formisano E. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 43 (2008) 44-58
10. Devlin J.T., and Poldrack R.A. In praise of tedious anatomy. Neuroimage 37 (2007) 1033-1041
11. Devor A., Hillman E.M.C., Tian P., Waeber C., Teng I.C., Ruvinskaya L., Shalinsky M.H., Zhu H., Haslinger R.H., Narayanan S.N., Ulbert I., Dunn A.K., Lo E.H., Rosen B.R., Dale A.M., Kleinfeld D., and Boas D.A. Stimulus-induced changes in blood flow and 2-deoxyglucose uptake dissociate in ipsilateral somatosensory cortex. J. Neurosci. 28 (2008) 14347-14357
12. Dinstein I., Gardner J.L., Jazayeri M., and Heeger D.J. Executed and observed movements have different distributed representations in human aIPS. J. Neurosci. 28 (2008) 11231-11239
13. Edgington E.S. Randomization Tests (1995), Marcel Dekker, New York
14. Eickhoff S.B., Stephan K.E., Mohlberg H., Grefkes C., Fink G.R., Amunts K., and Zilles K. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25 (2005) 1325-1335
15. Eickhoff S.B., Heim S., Zilles K., and Amunts K. Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps. Neuroimage 32 (2006) 570-582
16. Etzel J.A., Gazzola V., and Keysers C. Testing simulation theory with cross-modal multivariate classification of fMRI data. PLoS One 3 (2008) e3690
17. Formisano E., De Martino F., and Valente G. Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. Magn. Reson. Imaging 26 (2008) 921-934
18. Friston K.J., Frith C.D., Liddle P.F., and Frackowiak R.S.J. Functional connectivity: the principal-component analysis of large(PET) data sets. J. Cereb. Blood Flow Metab. 13 (1993) 5-14
19. Friston K.J., Holmes A.P., Worsley K.J., Poline J.P., Frith C.D., and Frackowiak R.S.J. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2 (1994) 189-210
20. Friston K.J., Rotshtein P., Geng J.J., Sterzer P., and Henson R.N. A critique of functional localisers. Neuroimage 30 (2006) 1077-1087
21. Friston K.J., Chu C., Mourao-Miranda J., Hulme O., Rees G., Penny W., and Ashburner J. Bayesian decoding of brain images. Neuroimage 39 (2008) 181-205
22. Gazzola V., Aziz-Zadeh L., and Keysers C. Empathy and the somatotopic auditory mirror system in humans. Curr. Biol. 16 (2006) 1824-1829
23. Georgopoulos A.P., Schwartz A.B., and Kettner R.E. Neuronal population coding of movement direction. Science 233 (1986) 1416-1419
24. Goebel R., Roebroeck A., Kim D.-S., and Formisano E. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn. Reson. Imaging 21 (2003) 1251-1261
25. Golland P., and Fischl B. Permutation tests for classification: towards statistical significance in image-based studies. In: Taylor C.J., and Noble J.A. (Eds). Information Processing in Medical Imaging. Lecture Notes in Computer Science Vol. 2732/2003 (2003), Springer, Berlin 330-341
26. Good, P.I., 2001. Resampling methods: a practical guide to data analysis, Vol., Birkhauser, Boston, Basel, Berlin.
27. Greer B., and Mulhern G. Making Sense of Data and Statistics in Psychology (2002), Palgrave
28. Hampton A.N., and O'Doherty J.P. Decoding the neural substrates of reward-related decision making with functional MRI. Proc. Natl. Acad. Sci. U. S. A. 104 (2007) 1377-1382
29. Hanson S.J., and Halchenko Y.O. Brain reading using full brain support vector machines for object recognition: there is no "face" identification area. Neural Comput. 20 (2008) 486-503
30. Hastie T., Tibshirani R., and Friedman J. The Elements of Statistical Learning (2001), Springer
31. Haynes J.-D., and Rees G. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7 (2006) 523-534
32. Haynes J.-D., Sakai K., Rees G., Gilbert S., Frith C., and Passingham R.E. Reading hidden intentions in the human brain. Curr. Biol. 17 (2007) 323-328
33. Hsu C.W., and Lin C.J. A comparison of methods for multiclass support vector machines. IEEE Trans. Nueral Netw. 13 (2002) 415-425
34. Huettel S.A., Song A.W., and McCarthy G. Functional Magnetic Resonance Imaging (2004), Sinauer Associates, Inc, Sunderland, Massachusetts USA
35. Joachims T. Making large-scale support vector machine learning practical. In: Scholkopf B., Burges C.J.C., and Smola A.J. (Eds). Advances in Kernel Methods: Support Vector Learning (1999), MIT Press, Cambridge, MA 169-184
36. Kamitani Y., and Tong F. Decoding seen and attended motion directions from activity in the human visual cortex. Curr. Biol. 16 (2006) 1096-1102
37. Kriegeskorte N., Goebel R., and Bandettini P. Information-based functional brain mapping. PNAS. 103 (2006) 3863-3868
38. Kriegeskorte N., and Bandettini P. Combining the tools: activation- and information-based fMRI analysis. Neuroimage 38 (2007) 666-668
39. Ku S.-p., Gretton A., Macke J., and Logothetis N.K. Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys. Magn. Reson. Imaging 26 (2008) 1007-1014
40. LaConte S., Strother S., Cherkassky V., Anderson J., and Hu X. Support vector machines for temporal classification of block design fMRI data. Neuroimage 26 (2005) 317-329
41. Logothetis N.K., Pauls J., and Poggio T. Shape representation in the inferior temporal cortex of monkeys. Curr. Biol. 5 (1995) 552-563
42. Logothetis N.K. What we can do and what we cannot do with fMRI. Nature 453 (2008) 869-878
43. Macey P.M., Macey K.E., Kumar R., and Harper R.M. A method for removal of global effects from fMRI time series. Neuroimage 22 (2004) 360-366
44. Mitchell T.M., Hutchinson R., Niculescu R.S., Pereira F., and Wang X. Learning to decode cognitive states from brain images. Mach. Learn. 57 (2004) 145-175
45. Mourao-Miranda J., Bokde A.L.W., Born C., Hampel H., and Stetter M. Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. Neuroimage 28 (2005) 980-995
46. Mourao-Miranda J., Reynaud E., McGlone F., Calvert G., and Brammer M. The impact of temporal compression and space selection on SVM analysis of single-subject and multi-subject fMRI data. Neuroimage 33 (2006) 1055-1065
47. Mourao-Miranda J., Friston K.J., and Brammer M. Dynamic discrimination analysis: a spatial-temporal SVM. Neuroimage 36 (2007) 88-99
48. Mukherjee S., Golland P., and Panchenko D. Permutation tests for classification. AI Memo 2003-019. Vol (2003), Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
49. Mur M., Bandettini P.A., and Kriegeskorte N. Revealing representational content with pattern-information fMRI-an introductory guide. Soc. Cogn. Affect. Neurosci. (2009) nsn044
50. Nandy R., and Cordes D. Improving the spatial specificity of canonical correlation analysis in fMRI. Magn. Reson. Med. 52 (2004) 947-952
51. Nichols T.E., and Holmes A.P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15 (2001) 1-25
52. Nieto-Castanon A., Ghosh S.S., Tourville J.A., and Guenther F.H. Region of interest based analysis of functional imaging data. Neuroimage 19 (2003) 1303-1316
53. Norman K.A., Polyn S.M., Detre G.J., and Haxby J.V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10 (2006) 424-430
54. O'Toole A.J., Jiang F., Abdi H., Penard N., Dunlop J.P., and Parent M.A. Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. J. Cogn. Neurosci. 19 (2007) 1735-1752
55. Pereira F., Mitchell T., and Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45 (2009) S199-S209
56. Pessoa L., and Padmala S. Decoding near-threshold perception of fear from distributed single-trial brain activation. Cereb. Cortex 17 (2007) 691-701
57. Petrie A., and Sabin C. Medical Statistics at a Glance (2000), Blackwell Science, Oxford
58. Poldrack R.A. Region of interest analysis for fMRI. Soc. Cogn. Affect. Neurosci. 2 (2007) 67-70
59. R Development Core Team. R: A Language and Environment for Statistical Computing (2008), R Foundation for Statistical Computing, Vienna, Austria
60. Roebroeck A., Formisano E., and Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 25 (2005) 230-242
61. Saxe R., Brett M., and Kanwisher N. Divide and conquer: a defense of functional localizers. Neuroimage 30 (2006) 1088-1096
62. Schölkopf B., and Smola A.J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (2002), MIT Press, Cambridge, MA
63. Schwarzlose R.F., Swisher J.D., Dang S., and Kanwisher N. The distribution of category and location information across object-selective regions in human visual cortex. Proc. Natl. Acad. Sci. 105 (2008) 4447-4452
64. Shinkareva S.V., Mason R.A., Malave V.L., Wang W., Mitchell T.M., and Just M.A. Using fMRI brain activation to identify cognitive states associated with perception of tools and dwellings. PLoS One 3 (2008) e1394
65. Soon C.S., Brass M., Heinze H.J., and Haynes J.D. Unconscious determinants of free decisions in the human brain. Nat. Neurosci. 11 (2008) 543-545
66. Strother S.C. Evaluating fMRI preprocessing pipelines. IEEE Eng. Med. Biol. Mag. 25 (2006) 27-41
67. Tanabe J., Miller D., Tregellas J., Freedman R., and Meyer F.G. Comparison of detrending methods for optimal fMRI preprocessing. Neuroimage 15 (2002) 902-907
68. Thomas C.G., Harshman R.A., and Menon R.S. Noise reduction in BOLD-based fMRI using component analysis. Neuroimage 17 (2002) 1521-1537
69. Tohka J., Foerde K., Aron A.R., Tom S.M., Toga A.W., and Poldrack R.A. Automatic independent component labeling for artifact removal in fMRI. Neuroimage 39 (2008) 1227-1245
70. Vapnik V.N. The Nature of Statistical Learning Theory (2000), Springer, New York, NY
71. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques (2005), Morgan Kaufmann Publishers