Full-brain auto-regressive modeling (FARM) using fMRI

NeuroImage

Volumn 58, Issue 2, 2011, Pages 416-441

  Garg, Rahul ^a   Cecchi, Guillermo A ^a   Rao, A Ravishankar ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Auto regressive modeling; Brain dynamics; Default mode networks; Dynamical systems; Functional MRI (fMRI); Granger causality; Prediction; Resting state analysis

Indexed keywords

ARTICLE; CONTROLLED STUDY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEMODYNAMICS; HUMAN; HUMAN EXPERIMENT; INSULA; NORMAL HUMAN; PARIETAL CORTEX; POSTERIOR CINGULATE; PRECUNEUS; PREDICTION; PRIORITY JOURNAL; SIMULATION; SOMATOSENSORY SYSTEM; TASK PERFORMANCE;

ALGORITHMS; ANALYSIS OF VARIANCE; BRAIN; BRAIN MAPPING; CAUSALITY; CEREBROVASCULAR CIRCULATION; CLUSTER ANALYSIS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; ELECTROENCEPHALOGRAPHY; HEMODYNAMICS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; INDIVIDUALITY; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); NEURAL PATHWAYS; NEURONS; OXYGEN; REGRESSION ANALYSIS; REPRODUCIBILITY OF RESULTS; STOCHASTIC PROCESSES;

EID: 80051791599     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2011.02.074     Document Type: Article

References (98)

1. Achard S., Salvador R., Whitcher B., Suckling J., Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J. Neurosci. 2006, 26(1):63-72. January.
2. Aguirre G.K., Zarahn E., D'Esposito M. The variability of human, BOLD hemodynamic responses. NeuroImage 1998, 8(4):360-369.
3. Anderson C.W., Stolz E.A., Shamsunder S. Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental task. IEEE. Trans. Biomed. Eng. 1998, 45(3):277-286. March.
4. Aoki T., Tsuda H., Takasawa M., Osaki Y., Oku N., Hatazawa J., Kinoshita H. The effect of tapping finger and mode differences on cortical and subcortical activities: a PET study. Exp. Brain Res. 2005, 160:375-383.
5. Arieli A., Sterkin A., Grinvald A., Aertsen A. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 1996, 273(5283):1868-1871.
6. Binkofski F., Amunts K., Stephan K.M., Posse S., Schormann T., Freund H.-J., Zilles K., Seitz R.J. Broca's region subserves imagery of motion: a combined cytoarchitectonic and fMRI study. Hum. Brain Mapp. 2000, 11(4):273-285.
7. Box G.E.P., Jenkins G.M., Reinsel G.C. Time Series Analysis: Forecasting and Control (Wiley Series in Probability and Statistics) 2008, Wiley, June. 4 edition.
8. Brockwell P.J., Davis R.A. Time Series: Theory and Methods 1986, Springer-Verlag New York, Inc., New York, NY, USA.
9. Brovelli A., Ding M., Ledberg A., Chen Y., Nakamura R., Bressler S.L. Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality. Proc. Natl. Acad. Sci. U. S. A. 2004, 101(26):9849-9854. June.
10. Buchel C., Friston K.J. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb. Cortex 1997, 7(8):768-778.
11. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 2009, 10(3):186-198. March.
12. Buxton R.B., Uludag K., Dubowitz D.J., Liu T.T. Modeling the hemodynamic response to brain activation. NeuroImage 2004, 23(Supplement 1):S220-S233. Mathematics in Brain Imaging.
13. Candès E.J. The restricted isometry property and its implications for compressed sensing. C. R. Acad. Sci. Paris Ser. I 2008, 346:589-592.
14. Candès E.J., Romberg J. Practical signal recovery from random projections. SPIN Conference on Wavelet Applications in Signal and Image Processing 2004.
15. Carroll M.K., Cecchi G.A., Rish I., Garg R., Rao A.R. Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 2009, 44:112-122.
16. Cavanna A.E., Trimble M.R. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 2006, 129:564-583.
17. Cecchi G.A., Rao A.R., Centeno M.V., Baliki M., Apkarian A.V., Chialvo D.R. Identifying directed links in large scale functional networks: application to brain fMRI. BMC Cell Biol. 2007, 8(1):S5.
18. Cecchi G.A., Garg R., Rao A.R. Inferring brain dynamics using Granger causality on fMRI data. Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging (ISBI'08) 2008, 604-607.
19. Cecchi G.A., Ma'ayan A., Rao A.R., Wagner J., Iyengar R., Stolovitzky G. Ordered cyclic motifs contributes to dynamic stability in biological and engineered networks. Proc. Natl. Acad. Sci. U. S. A. 2008, 105:19235-19240.
20. Cecchi G.A., Rish I., Thyreau B., Thirion B., Plaze M., Palliere-Martinot M.-L., Martelli C., Martinot J.-L., Poline J.-B. Discriminative network models of schizophrenia. In proceedings of Advances in Neural Information Processing Systems (NIPS) 2009.
21. Chen S.S., Donoho D.L., Saunders M.A. Atomic decomposition by basis pursuit. SIAM Rev. 2001, 43(1):129-159.
22. Chen G., et al. Granger causality via vector auto-regression tuned for fMRI data analysis. ISMRM 17th Scientific Meeting 2009.
23. Cunnington R., Windischberger C., Deecke L., Moser E. The preparation and execution of self-initiated and externally-triggered movement: a study of event-related fMRI. NeuroImage 2001, 15:373-385.
24. D'Esposito M., Deouell L.Y., Gazzaley A. Alterations in the BOLD fMRI signal with ageing and disease: a challenge for neuroimaging. Nat. Rev. Neurosci. 2003, 4(11):863-872. November.
25. Dahlhaus R., Eichler M. Causality and graphical models in time series analysis. Highly Structured Stochastic Systems 2003, 115-137. Oxford University Press, London. P.J. Green, N.L. Hjort, S. Richardson (Eds.).
26. Darvas F., Leahy R.M. Functional imaging of brain activity and connectivity with MEG. Handbook of Brain Connectivity 2007, 201-220. Springer, Berlin/Heidelberg.
27. Deshpande G., LaConte S., James G.A.A., Peltier S., Hu X. Multivariate Granger causality analysis of fMRI data. Hum. Brain Mapp. 2009, 30(4):1361-1373. April.
28. Dodel S., Herrmann J.M., Geisel T. Functional connectivity by cross-correlation clustering. Neurocomputing 2002, 44-46:1065-1070.
29. Duann J.-R., Jung T.-P., Kuo W.-J., Yeh T.-C., Makeig S., Hsieh J.-C., Sejnowski T.J. Single-trial variability in event-related BOLD signals. NeuroImage 2002, 15(4):823-835.
30. A Universe Of Consciousness How Matter Becomes Imagination 2001, Basic Books. G. Edelman, G. Tononi (Eds.).
31. Efron B., Hastie T., Johnstone I., Tibshirani R. Least angle regression. Ann. Stat. 2004, 32(1):407-499.
32. Eguiluz V.M., Chialvo D.R., Cecchi G.A., Baliki M., Apkarian V.A. Scale-free brain functional networks. Phys. Rev. Lett. 2005, 94:18-102. Jan.
33. Eichler M. A graphical approach for evaluating effective connectivity in neural systems. Phil. Trans. R. Soc. B 2005, 360(1457):953-967. May.
34. Fox M.D., Snyder A.Z., Vincent J.L., Corbetta M., Van Essen D.C., Raichle M.E. The human brain is intrinsically organized into dynamic, anti-correlated functional networks. Proc. Natl. Acad. Sci. U. S. A. 2005, 102:9673-9678.
35. Frasson P., Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation analysis. NeuroImage 2008, 42:1178-1184.
36. Friston K.J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 1994, 2(1-2):56-78.
37. Friston K.J., Holmes A.P., Poline J.-B., Grasby P.J., Williams S.C.R., Frackowiak R.S.J., Turner R. Analysis of fMRI time-series revisited. NeuroImage 1995, 2(1):45-53.
38. Friston K.J., Mechelli A., Turner R., Price C.J. Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. NeuroImage 2000, 12(4):466-477.
39. Friston K.J., Harrison L., Penny W. Dynamic causal modelling. NeuroImage 2003, 19(4):1273-1302.
40. Friston K., et al. Statistical parametric mapping, the analysis of functional brain images 2007.
41. FSL Release 3.3 http://www.fmrib.ox.ac.uk/fsl.
42. Gajic Z., Qureshi M.T.J. Lyapunov Matrix Equation in Systems Stability and Control 1995, Academic Press, San Diego.
43. Garg R., Khandekar R. Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property. In Proceedings of the 26th International Conference on Machine Learning 2009, 337-344. Omnipress, Montreal, June.
44. Garg R., Cecchi G.A., Rao A.R. Applications of high-performance computing to functional magnetic resonance imaging (fMRI) data. High-throughput Image Reconstruction and Analysis 2009, 263-282. Artech House Publishers, City, State of Publication, chapter12. A.R. Rao, G.A. Cecchi (Eds.).
45. Gitelman D.R., Penny W.D., Ashburner J., Friston K.J. Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. NeuroImage 2003, 19(1):200-207.
46. Glover G.H. Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage 1999, 9(4):416-429.
47. Goebel R., Roebroeck A., Kim D.-S., Formisano E. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn. Reson. Imaging 2003, 21(10):1251-1261.
48. Granger C.W.J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 1969, 37(3):424-438.
49. Greicius M.D., Krasnow B., Reiss A.L., Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. U. S. A. 2003, 100:253-258.
50. Greicius M.D., Srivastava G., Reiss A.L., Menon V. Default-mode network activity distinguishes Alzheimers disease from healthy aging: evidence from functional MRI. Proc. Natl. Acad. Sci. U. S. A. 2004, 101:4637-4642.
51. Grinband J., Wager T.D., Lindquist M., Ferrera V.P., Hirsch J. Detection of time-varying signals in event-related fMRI designs. NeuroImage 2008, 43(3):509-520.
52. Handwerker D.A., Ollinger J.M., D'Esposito M. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage 2004, 21(4):1639-1651.
53. Harrison L., Penny W.D., Friston K.J. Multivariate autoregressive modelling of fMRI time series. NeuroImage 2003, 19(4):1477-1491.
54. IBM Blue Gene Team Overview of the IBM Blue Gene/P project. IBM J. Res. Dev. 2008, 52(1/2):199-220.
55. Jäncke L., Loose R., Lutz K., Specht K., Shah N.J. Cortical activations during paced finger-tapping applying visual and auditory pacing stimuli. Cogn. Brain Res. 2000, 10:51-66.
56. Kamiński M., Ding M., Truccolo W.A., Bressler S.L. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biol. Cybern. 2001, 85(2):145-157. August.
57. Large-scale neuronal theories of the brain, chapter 6: Perception as an oneiric-like state modulated by the senses 1995, MIT Press. C. Koch, J. Davis (Eds.).
58. Li W., Crist R.E., Gilbert C.D. Learning to see: experience and attention in primary visual cortex. Nat. Neurosci. 2001, 4(5):519-525.
59. Li K., Guo L., Nie J., Li G., Liu T. Review of methods for functional brain connectivity detection using fMRI. Comput. Med. Imaging Graph. 2009, 33(2):131-139.
60. Lippert M.T., Steudel T., Ohl F., Logothetis N.K., Kayser C. Coupling of neural activity and fMRI-BOLD in the motion area MT. Magn. Reson. Imaging 2010, 28(8):1087-1094.
61. Logothetis N.K. What we can do and what we cannot do with fMRI. Nature 2008, 453(7197):869-878. June.
62. Lu Y., Bagshaw A.P., Grova C., Kobayashi E., Dubeau F., Gotman J. Using voxel-specific hemodynamic response function in EEG-fMRI data analysis. NeuroImage 2006, 32(1):238-247.
63. Machamer P., Wolters G. Thinking about Causes: From Greek Philosophy to Modern Physics 2007, University of Pittsburgh Press, May.
64. Margulies D.S., Vincent J.L., Kellye C., Lohmann G., Uddin L.Q., Biswal B.B., Villringer A., Castellanos F.X., Milham M.P., Petrides M. Precuneus shares intrinsic functional architecture in humans and monkeys. Proc. Natl. Acad. Sci. U. S. A. 2009, 106:20069-20074.
65. Marrelec G., Krainik A., Duffau H., Plgrini-Issac M., Lehricy S., Doyon J., Benali H. Partial correlation for functional brain interactivity investigation in functional MRI. NeuroImage 2006, 32(1):228-237.
66. Mcintosh A.R., Gonzalez-Lima F. Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 1994, 2(1-2):2-22.
67. McKeown M.J., Makeig S., Brown G.G., Jung T.-P., Kindermann S.S., Bell A.J., Sejnowski T.J. Analysis of fMRI data by blind separation into independent spatial components. Hum. Brain Mapp. 1998, 6:160-188.
68. Menon R.S., Kim S.-G. Spatial and temporal limits in cognitive neuroimaging with fMRI. Trends Cogn. Sci. 1999, 3(6):207-216.
69. Menon R.S., Luknowsky D.C., Gati J.S. Mental chronometry using latency-resolved functional MRI. Proc. Natl. Acad. Sci. U. S. A. 1998, 95(18):10902-10907.
70. Mitchell T.M., Shinkareva S.V., Carlson A., Chang K.M., Malave V.L., Mason R.A., Just M.A. Predicting human brain activity associated with the meanings of nouns. Science 2008, 320(5880):1191-1195. May.
71. Natarajan B.K. Sparse approximate solutions to linear systems. SIAM J. Comput. 1995, 24(2):227-234.
72. Neumann J., Lohmann G., Zysset S., von Cramon D.Y. Within-subject variability of BOLD response dynamics. NeuroImage 2003, 19(3):784-796.
73. Neylon, T., 2006. Sparse solutions for linear prediction problems. PhD thesis, Courant Institute, New York University, April 2006.
74. Norman K.A., Polyn S.M., Detre G.J., Haxby J.V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 2006, 10(9):424-430. September.
75. Pearl J. Graphs, causality, and structural equation models. Sociol. Methods Res. 1998, 27:226-284.
76. Penny W.D., Stephan K.E., Mechelli A., Friston K.J. Comparing dynamic causal models. NeuroImage 2004, 22(3):1157-1172.
77. Pereda E., Quiroga R.Q., Bhattacharya J. Nonlinear multivariate analysis of neurophysiological signals. Prog. Neurobiol. 2005, 77(1-2):1-37. September.
78. Raichle M.E., MacLeod A.M., Snyder A.Z., Powers W.J., Gusnard D.A., Shulman G.L. A default mode of brain function. Proc. Natl. Acad. Sci. U. S. A. 2001, 98(2):676-682.
79. Riecker A., Wildgruber D., Mathiak K., Grodd W., Ackermann H. Parametric analysis of rate-dependent hemodynamic response functions of cortical and subcortical brain structures during auditorily cued finger tapping: a fMRI study. NeuroImage 2003, 18:731-739.
80. Roebroeck A., Formisano E., Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. NeuroImage 2005, 25(1):230-242.
81. Salvador R., Suckling J., Schwarzbauer C., Bullmore E. Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2005, 360(1457). May.
82. Salvador R., Martnez A., Pomarol-Clotet E., Sarr S., Suckling J., Bullmore E. Frequency based mutual information measures between clusters of brain regions in functional magnetic resonance imaging. NeuroImage 2007, 35(1):83-88.
83. Sato J.R., Morettin P.A., Arantes P.R., Amaro E. Wavelet based time-varying vector autoregressive modelling. Comput. Stat. Data Anal. 2007, 51(12):5847-5866.
84. Schneidman E., Berry M.J., Segev R., Bialek W. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 2006, 440:1007-1012.
85. Seth A.K. Causal connectivity of evolved neural networks during behavior. Netw. Comput. Neural Syst. 2005, 16(1):35-54.
86. Skipper J.I., Goldin-Meadow S., Nusbaum H.C., Small S.L. Speech-associated gestures, Broca's area, and the human mirror system. Brain Lang. 2007, 101(3):260-277.
87. Small M. Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance 2005, World Scientific Publishing Company.
88. Smith B.D., Bullmore E. Small-world brain networks. Neuroscientist 2006, 12:512-523.
89. Stam C.J., Jones B.F., Nolte G., Breakspear M., Scheltens Ph. Small-world networks and functional connectivity in Alzheimer's disease. Cereb. Cortex 2007, 17(1):92-99.
90. Stephan K.M., Fink G.R., Passingham R.E., Silbersweig D., Ceballos-Baumann A.O., Frith C.D., Frackowiak R.S. Functional anatomy of the mental representation of upper extremity movements in healthy subjects. J. Neurophysiol. 1995, 73:373-386.
91. Strang G. Linear Algebra and Its Applications 1988, Harcourt Brace Jovanovich College Publishers. third ed.
92. Sun F.T., Miller L.M., D'Esposito M. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage 2004, 21(2):647-658.
93. Thomason M.E., Burrows B.E., Gabrieli J.D.E., Glover G.H. Breath holding reveals differences in fMRI BOLD signal in children and adults. NeuroImage 2005, 25(3):824-837.
94. Tibshirani R. Regression shrinkage and selection via the Lasso. J. R. Stat. Soc. B 1996, 58(1):267-288.
95. Valdes-Sosa P.A., Sanchez-Bornot J.M., Lage-Castellanos A., Vega-Hernandez M., Bosch-Bayard J., Melie-Garcia L., Canales-Rodriguez E. Estimating brain functional connectivity with sparse multivariate autoregression. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2005, 360(1457):969-981.
96. Vaudano A.E., Laufs H., Kiebel S.J., Carmichael D.W., Hamandi K., Guye M., Thornton R., Rodionov R., Friston K.J., Duncan J.S., Lemieux L. Causal hierarchy within the thalamo-cortical network in spike and wave discharges. PLoS One 2009, 4(8):e6475. 08.
97. Vazquez A.L., Noll D.C. Nonlinear aspects of the BOLD response in functional MRI. NeuroImage 1998, 7(2):108-118.
98. Zivot E., Wang J. Modeling Financial Time Series with S-PLUS® 2006, Springer-Verlag New York, Inc., Secaucus, NJ, USA.