Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas

Magnetic Resonance Imaging

Volumn 31, Issue 2, 2013, Pages 247-261

  Zheng, Weili ^a   Ackley, Elena S ^a   Martínez Ramón, Manel ^b   Posse, Stefan ^a,c,d  

^a UNIVERSITY OF NEW MEXICO SCHOOL OF MEDICINE   (United States)

^b UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^c University of New Mexico   (United States)

^d UNIVERSITY OF NEW MEXICO   (United States)

Author keywords

Boosting; FMRI; Partial least squares; Pattern classification; Real time

Indexed keywords

ARTICLE; BOLD SIGNAL; BRAIN FUNCTION; BRAIN MAPPING; BRAIN REGION; COGNITION; DIAGNOSTIC ACCURACY; DISCRIMINATIVE STIMULUS; ECHO PLANAR IMAGING; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEARING; HUMAN; MOTOR PERFORMANCE; NEUROFEEDBACK; NEUROIMAGING; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIMARY MOTOR CORTEX; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RETROSPECTIVE STUDY; SENSORIMOTOR FUNCTION; SIGNAL NOISE RATIO; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; VISION;

ALGORITHMS; BRAIN; BRAIN MAPPING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LEAST-SQUARES ANALYSIS; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; PROBABILITY; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; RETROSPECTIVE STUDIES; TIME FACTORS;

EID: 84872676990     PISSN: 0730725X     EISSN: 18735894     Source Type: Journal    
DOI: 10.1016/j.mri.2012.07.010     Document Type: Article

References (90)

1. 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(2):261-270.
2. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998, 2(2):1-32.
3. Vapnik V. The nature of statistical learning theory 1995, Springer, New York.
4. LaConte S.M., Strother S., Cherkassky V., Anderson J., Hu X. Support vector machines for temporal classification of block design fMRI data. NeuroImage 2005, 26(2):317-329.
5. LaConte S.M., Strother S., Cherkassky V., Hu X. Predicting motor tasks in fmri data with support vector machines. In Proc Intl Soc Mag Reson Med 2003, 11:494. Toronto, Ontario, Canada.
6. Training fMRI classifiers to detect cognitive states across multiple human subjects. In Advances in Neural Information Processing Systems 2004, 16:709-716. MIT Press, Cambridge (Mass). X. Wang, R. Hutchinson, T.M. Mitchell (Eds.).
7. Kamitani Y., Tong F. Decoding the visual and subjective contents of the human brain. Nat Neurosci 2005, 8(5):679-685.
8. Haynes J.-D., Rees G. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat Neurosci 2005, 8(5):686-691.
9. Davatzikos C., Ruparel K., Fan Y., Shen D.G., Acharyya M., Loughead J.W., et al. Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. NeuroImage 2005, 28(3):663-668.
10. LaConte SM, Scott J. Peltier, Hu XP. Real-time fMRI using brain-state classification. Hum Brain Mapp 2007;28:1033-1-44.
11. deCharms C.R. Reading and controlling human brain activation using real-time functional magnetic resonance imaging. Trends Cogn Sci 2007, 11(11):473-481.
12. Norman K., Polyn S.M., Detre G., Haxby J.V. Beyond mind-reading: multi-voxel pattern analysis of fmri data. Trends Cogn Sci 2006, 10(9):424-430.
13. LaConte S.M. Decoding fMRI brain states in real-time. NeuroImage 2011, 56:440-454.
14. Bellman R. Adaptive control processes 1961, Princeton University Press, Princeton, NJ.
15. Guyon I., Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003, 3:1157-1182.
16. Mitchell T.M., Hutchinson R., Niculescu R.S., Pereira F., Wang X., Just M., et al. Learning to decode cognitive states from brain images. Mach Learn 2004, 57:145-175.
17. De Martino F., Valente G., Staeren N., Ashburner J., Goebel R., Formisano E. Combining multivariate voxel selection and support vector machines for mapping and classification of fmri spatial patterns. NeuroImage 2008, 43(1):44-58.
18. Ryali S., Supekar K., Abrams D.A., Menon V. Sparse logistic regression for whole-brain classification of fMRI data. NeuroImage 2010, 51(2):752-764.
19. Bi J., Bennett K.P. Dimensionality reduction via sparse support vector machines. J Mach Learn Res 2003, 3:1229-1243.
20. Aizerman A., Braverman E.M., Rozoner L.I. Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control 1964, 25:821-837.
21. Mourão-Miranda J., Bokde A.L.W., Born C., Hampel H., Stetter M. Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage 2005, 28(4):980-995.
22. Fan Y., Resnick S.M., Wu X., Davatzikos C. Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study. NeuroImage 2008, 41(2):277-285.
23. Martínez-Ramón M., Koltchinskii V., Heileman G.L., Posse S. fMRI pattern classification using neuroanatomically constrained boosting. NeuroImage 2006, 31(3):1129-1141.
24. Schapire R.E. A brief introduction to boosting 1999, 1401-1406. Morgan Kauffman Pub. Inc, San Francisco (Calif).
25. Schapire R.E., Singer Y. Improved boosting algorithms using confidence-rated predictions. Mach Learn 1999, 37(3):297-336.
26. Williams M.A., Dang S., Kanwisher N.G. Only some spatial patterns of fMRI response are read out in task performance. Nat Neurosci 2007, 10:685-686.
27. Freund Y., Schapire R. A decision-theoritic generalization of online learning and an application to boosting. J Comp Syst Sci 1997, 55:119-139.
28. Allwein E.A., Schapire R., Singer Y. Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 2000, 1:113-141.
29. Mason L., Baxter J., Bartlett P., Frean M. Advances in large margin classifiers. Ch. Functional gradient techniques for combining hypotheses 1999, 33-58. MIT Press, Cambridge.
30. Valiant L.G. A theory of the learnable. Commun ACM 1984, 27(11):1134-1142.
31. Krishnan A., Williams L.J., McIntosh A.R., Abdi H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage 2011, 56(2):455-475.
32. Frank I., Friedman J. A statistical view of some chemometrics regression tools. Technometrics 1993, 35:109-135.
33. Helland I. On the structure of partial least squares regression. Commun Stat Simul 1998, 17:581-607.
34. Helland I. Partial least squares regression and statistical models. Scand J Stat 1990, 17:97-114.
35. Wold H. Soft modelling, the basic design and some extensions. Systems under indirect observation: causality-structure-prediction. Part II 1982, 1-54. North-Holland Publishing Company, Amsterdam. H. Wold, K.-G. Jöreskog (Eds.).
36. Wold H. Estimation of principal components and related models by iterative least squares. Multivariate analysis 1966, 391-420. Academic Press, New York. D.P.R. Krishnaiag, K.-G. Jöreskog (Eds.).
37. McIntosh A.R., Bookstein F.L., Haxby J.V., Grady C.L. Spatial pattern analysis of functional brain images using partial least squares. NeuroImage 1996, 3:143-157.
38. McIntosh A.R., Chau W.K., Protzner A.B. Spatiotemporal analysis of event-related fMRI data using partial least squares. NeuroImage 2004, 23(2):764-775.
39. Wold S., Sjöström M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 2001, 58:109-130.
40. de Jong S. SIMPLS: an alternative approach to partial least squares regression. Chemom Intell Lab Syst 1993, 18:251-263.
41. Giessing C., Fink G., Rosler F., Thiel C. fMRI data predict individual differences of behavioral effects of nicotine: a partial least squares analysis. J Chemometr 2007, 18:208-215.
42. Gottfries J., Blennow K., Wallin A., Gottfries C.G. Diagnosis of dementias using partial least squares discriminant analysis. Dementia 1995, 6:83-88.
43. Lehmann C., Koenig T., Jelic V., Prichep L., John R.E., Wahlund L.-O., et al. Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity. J Neurosci Methods 2007, 161:342-350.
44. Chen K., Reiman E.M., Huan Z., Caselli R.J., Bandy D., Ayutyanont N., et al. Linking functional and structural brain images with multivariate network analyses: a novel application of the partial least square method. NeuroImage 2009, 47(2):602-610.
45. Esposito-Vinzi V., Wynne W., Chin W.W., Henseler J., Wang H. Handbook of partial least squares: concepts, methods and applications 2010, Springer, New York.
46. Posse S., Binkofski F., Schneider F., Gembris D., Wiese S., Kiselev V., et al. A new approach to measure single event related brain activity using real-time fMRI: feasibility of sensory, motor, and higher cognitive tasks. Hum Brain Mapp 2001, 12(1):25-41.
47. Posse S., Wiese S., Gembris D., Mathiak K., Kessler C., Grosse-Ruyken M.-L., et al. Enhancement of BOLD-contrast sensitivity by single-shot multi-echo functional MR imaging. Magn Reson Med 1999, 42(1):87-97.
48. Mathiak K., Posse S. Evaluation of motion and realignment for functional magnetic resonance imaging in realtime. Magn Reson Med 2001, 45(1):167-171.
49. Evans A.C., Collins D., Mills S.R., Brown E.D., Kelly R.L., Peters T.M. 3D statistical neuroanatomical models from 305 MRI volumes. In the Proceedings of IEEE-Nuclear S. Symposium, and Medical Imaging Conference 1993, 1813-1817.
50. Gao K., Posse S. TurboFire: Real-time fMRI with automated spatial normalization and Talairach Daemon database 2003, 838.
51. Gembris D., Taylor J.G., Schor S., Frings W., Suter D., Posse S. Functional magnetic resonance imaging in real time (FIRE): sliding-window correlation analysis and reference-vector optimization. Magn Reson Med 2000, 43(2):259-268.
52. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines Software available at:, Accessed. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
53. Lancaster J.L., Woldorff M.G., Parsons L.M., Liotti M., Freitas C.S., Rainey L., et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp 2000, 10(3):120-131.
54. Dempster A.D., Larid N.M., Rubin D.B. Maximum likelihood from the incomplete data via the EM algorithm. J R Stat Soc 1977, 39:1-37.
55. Efron B., Tibshirani R.J. An introduction to the bootstrap (monographs on statistics and applied probability 57) 1994, Chapman & Hall/CRC, Florida.
56. Brett M., Christoff K., Cusack R., Lancaster J. Using the Talairach atlas with the MNI template. NeuroImage 2001, 13(6):S85.
57. Wold H. Path models with latent variables: the NIPALS approach. Quantitative sociology: international perspectives on mathematical and statistical model building 1975, 307-357. Academic Press, New York. H.M. Blalock (Ed.).
58. Jäncke L., Shah N.J., Peters M. Cortical activations in primary and secondary motor areas for complex bimanual movements in professional pianists. Cogn Brain Res 2000, 10:177-183.
59. Mattay V.S., Callicott J.H., Bertolino A., Santha A.K.S., Van Horn J.D., Tallent K.A., et al. Hemispheric control of motor function: a whole brain echo planar fMRI study. Psychiatry Res 1998, 83:7-22.
60. Ramsey N.F., Kirkby B.S., Gelderen T.V., Berman K.F., Duy J.H., Frank J.A., et al. Functional mapping of human sensorimotor cortex with 3D BOLD tMRI correlates highly with H2150 PET rCBF. J Cereb Blood Flow Metab 1996, 16:755-764.
61. Rao S.M., Harrington D.L., Haaland K.Y., Bobholz J.A., Cox R.W., Binder J.R. Distributed neural systems underlying the timing of movements. J Neurosci 1997, 17(14):5528-5535.
62. Yoo S.S., Wei X., Dickey C.C., Guttmann C.R.G., Panych L.P. Long-term reproducibility analysis of fMRI using hand motor task. Int J Neurosci 2005, 115:55-77.
63. Sabatini U., Boulanouar K., Fabre N., Martin F., Carel C., Colonnese C., et al. Cortical motor reorganization in akinetic patients with Parkinson's disease: a functional MRI study. Brain 2000, 123:394-403.
64. Gelnar P.A., Krauss B.R., Sheehe P.R., Szeverenyi N.M., Apkarian A.V. A comparative fMRI study of cortical representations for thermal painful, vibrotactile, and motor performance tasks. NeuroImage 1999, 10:460-482.
65. Moradi F., Liu L.C., Cheng K., Waggoner R.A., Tanaka K., Ioannides A.A. Consistent and precise localization of brain activity in human primary visual cortex by MEG and fMRI. NeuroImage 2003, 18:595-609.
66. Hermann D., Smolka M.N., Klein S., Heinz A., Mann K., Braus D.F. Reduced fMRI activation of an occipital area in recently detoxified alcohol-dependent patients in a visual and acoustic stimulation paradigm. Addict Biol 2007, 12:117-121.
67. Bristow D., Frith C., Rees G. Two distinct neural effects of blinking on human visual processing. NeuroImage 2005, 27:136-145.
68. Cohen L., Martinaud O., Lemer C., Lehéricy S., Samson Y., Obadia M., et al. Visual word recognition in the left and right hemispheres: anatomical and functional correlates of peripheral alexias. Cereb Cortex 2003, 13:1313-1333.
69. Simon O., Kherif F., Flandin G., Poline J.-B., Rivière D., Mangin J.-Fo., et al. Automatized clustering and functional geometry of human parietofrontal networks for language, space, and number. NeuroImage 2004, 23:1192-1202.
70. Simon O., Mangin J.-Fo., Cohen L., Bihan D.L., Dehaene S. Topographical layout of hand, eye, calculation, and language-related areas in the human parietal lobe. Neuron 2002, 33:475-487.
71. Cowell S.F., Egan G.F., Code C., Harasty J., Watson J.D.G. The functional neuroanatomy of simple calculation and number repetition: a parametric PET activation study. NeuroImage 2000, 12:565-573.
72. Ortuno F., Ojeda N., Arbizu J., López P., Martí-Climent J.M., Uelas I.P., et al. Sustained attention in a counting task: normal performance and functional neuroanatomy. NeuroImage 2002, 17:411-420.
73. Ghatan P.H., Hsieh J.C., Petersson K.M., Stone-Elander S., Ingvar M. Coexistence of attention-based facilitation and inhibition in the human cortex. NeuroImage 1998, 7:23-29.
74. Chochon F., Cohen L.G., Van De Moortele P.F., Dehaene S. Differential contributions of the left and right inferior parietal lobules to number processing. J Cogn Neurosci 1999, 6:617-630.
75. Mazoyer B., Zago L., Mellet E., Bricogne S., Etard O., Houdé O., et al. Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Res Bull 2001, 54(3):287-298.
76. Stanescu-Cosson R., Pinel P., Van De Moortele P.F., LeBihan D., Cohen L.G., Dehaene S. Understanding dissociations in dyscalculia: a brain imaging study of the impact of number size on the cerebral networks for exact and approximate calculation. Brain 2000, 123:2240-2255.
77. Hutchison E.R., Blumstein S.E., Myers E.B. An event-related fMRI investigation of voice-onset time discrimination. NeuroImage 2008, 40:342-352.
78. Benoit M.M., Raij T., Lin F.-H., Jääskeläinen I.P., Stufflebeam S. Primary and multisensory cortical activity is correlated with audiovisual percepts. Hum Brain Mapp 2010, 31:526-538.
79. Jancke L., Shah N.J., Posse S., Grosse-Ryuken M., Muller-Gartner H.W. Intensity coding of auditory stimuli: an fMRI study. Neuropsychologia 1998, 36:875-883.
80. Riecker A., Wildgruber D., Dogil G., Grodd W., Ackermann H. Hemispheric lateralization effects of rhythm implementation during syllable repetitions: an fMRI study. NeuroImage 2002, 16:169-176.
81. Husain F.T., Fromm S.J., Pursley R.H., Hosey L.A., Braun A.R., Horwitz B. Neural bases of categorization of simple speech and nonspeech sounds. Hum Brain Mapp 2006, 27:636-651.
82. Liebenthal E., Binder J.R., Spitzer S.M., Possing E.T., Medler D.A. Neural substrates of phonemic perception. Cereb Cortex 2005, 15:1621-1631.
83. Laird A.R., Lancaster J.L., Fox P. BrainMap: the social evolution of a functional neuroimaging database. Neuroinformatics 2005, 3:65-78.
84. Fox P., Lancaster J. Mapping context and content: the BrainMap model. Nat Rev Neurosci 2002, 3:319-321.
85. Fox P., Laird A., Fox S., Fox M., Uecker A., Crank M., et al. BrainMap taxonomy of experimental design: description and evaluation. Hum Brain Mapp 2005, 25:185-198.
86. Kunchev L.I., Rodríguez J.J. Classifier ensembles for fMRI data analysis: an experiment. Magn Reson Imaging 2010, 28:583-593.
87. Ku S.-P., Gretton A., Macke J., Logothetis N.K. Comparison of pattern recognition methods in classifying high-resolution BOLD signals obtained at high magnetic field in monkeys. Magn Reson Imaging 2008, 26:1007-1014.
88. Formisano E., Martino F.D., Valente G. Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. Magn Reson Imaging 2008, 26:921-934.
89. Björnsdotter M., Rylander K., Wessberg J. A Monte Carlo method for locally multivariate brain mapping. NeuroImage 2011, 56:508-516.
90. Wessberg J. An evolutionary approach to the identification of informative voxel clusters for brain state discrimination. IEEE J Sel Topics Signal Proc 2008, 2(6):919-928.