“More Is Different” in Functional Magnetic Resonance Imaging: A Review of Recent Data Analysis Techniques

Brain Connectivity

Volumn 3, Issue 3, 2013, Pages 223-239

  Lohmann, Gabriele ^a   Stelzer, Johannes ^a   Neumann, Jane ^a,b   ay, Nihat ^c,d   Turner, Robert ^a  

^a MAX PLANCK INSTITUTE FOR HUMAN COGNITIVE AND BRAIN SCIENCES   (Germany)

^b UNIVERSITY HOSPITAL LEIPZIG   (Germany)

^c MAX PLANCK INSTITUTE FOR MATHEMATICS IN THE SCIENCES   (Germany)

^d SANTA FE INSTITUTE   (United States)

Author keywords

algorithms; complex systems; connectivity; data analysis techniques; fMRI; multivariate; networks; pattern analysis

Indexed keywords

EID: 84888379494     PISSN: 21580014     EISSN: 21580022     Source Type: Journal    
DOI: 10.1089/brain.2012.0133     Document Type: Review

References (173)

1. Abou-Elseoud A, Starck T, Remes J, Nikkinen J, Tervonen O, Kiviniemi V. 2010. The effect of model order selection in group PICA. Hum Brain Mapp 31:1207-1216.
2. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. 2006. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26:63-72.
3. Anderson P. 1972. More is different. Science 177:393-396.
4. Ay N, Olbrich E, Bertschinger N, Jost J. 2011. A geometric approach to complexity. Chaos 21:037103.
5. Bandettini P, Bowtell R, Jezzard P, Turner R. 2012. Ultrahigh field systems and applications at 7 T and beyond: progress, pitfalls, and potential. Magn Reson Med 67:317-321.
6. Barabasi A, Albert R. 1999. Emergence of scaling in random networks. Science 286:509-512.
7. Baranger M. 2012. Chaos, complexity, and entropy: a physics talk for non-physicists. http://necsi.edu/projects/baranger/cce.pdf
8. Bassett D, Bullmore E. 2008. Small-world brain networks. Neuroscientist 12:512-523.
9. Beckmann C, De Luca M, Devlin J, Smith S. 2005. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond Ser B 360:1001-1013.
10. Beckmann C, Smith S. 2005. Tensorial extensions of independent component analysis for group FMRI data analysis. NeuroImage 25:294-311.
11. Bell A, Sejnowski T. 1995. An information maximisation approach to blind separation and blind deconvolution. Neural Comput 7:1129-1159.
12. Bell G, Hey T, Szalay A. 2009. Beyond the data deluge. Science 323:1297-1298.
13. Birn R, Murphy K, Bandettini P. 2008. The effect of respiration variations on independent component analysis results of resting state functional connectivity. Hum Brain Mapp 29:740-750.
14. Biswal B, Mennes M, Zuo X.N., Gohel S, Kelly C, Smith S.M., et al. 2010. Toward discovery science of human brain function. Proc Natl Acad Sci U S A 107:4734-4739.
15. Biswal B, Yetkin F, Haughton V, Hyde J. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magn Reson Med 34:537-541.
16. Blinowska K, Ku's R, Kamiåski M. 2004. Granger causality and information flow in multivariate processes. Phys Rev E Stat Nonlin Soft Matter Phys 70:050902.
17. Boehm O, Hardoon D, Manevitz L. 2011. Classifying cognitive states of brain activity via one-class neural networks with feature selection by genetic algorithms. Int J Mach Learn Cybernet 2:125-134.
18. Bonacich P. 1972. Factoring and weighting approaches to clique identification. J Math Soc 2:113-120.
19. Braun U, Plichta M, Esslinger C, Sauer C, Haddad L, Grimm O, Mier D, Mohnke S, Heinz A, Erk S, Walter H, Seiferth N, Kirsch P, Meyer-Lindenberg A. 2012. Test-retest reliability of resting-state connectivity network characteristics using fmri and graph theoretical measures. Neuroimage 59:1404-1412.
20. Breakspear M. 2013. Dynamic and stochastic models of neuroimaging data: a comment on Lohmann et al. Neuroimage 75:270-274.
21. Breakspear M, McIntosh R. 2011. Networks, noise and models: reconceptualizing the brain as a complex, distributed system. Neuroimage 58:293-295.
22. Bressler S, Menon V. 2010. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci 14:277-290.
23. Buckner R, Sepulcre J, Talukdar T, Krienen F, Liu H, Hedden T, Andrewshanna J, Sperling R, Johnson K. 2009. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimers disease. J Neurosci 29:1860-1873.
24. Bullmore E, Sporns O. 2009. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186-198.
25. Buxton R, Wong E, Frank L. 1998. Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model. MRM 39:855-864.
26. Calhoun V, Liu J, Adali T. 2009. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45(Supplement 1):S163-S172.
27. Carlson T, Schrater P, He S. 2003. Patterns of activity in the categorical representations of objects. J Cogn Neurosci 15:704-717.
28. Carp J. 2012. On the plurality of (methodological) worlds: estimating the analytic flexibility of fMRI experiments. Front Neurosci 6:149.
29. Carroll M, Cecchi G, Rish I, Garg R, Rao A. 2009. Prediction and interpretation of distributed neural activity with sparse models. Neuroimage 44:112-122.
30. Chai B, Walther D, Beck D, Fei-Fei L. 2009. Exploring functional connectivity of the human brain using multivariate information analysis. Adv Neural Inform Process Syst (NIPS), Vancouver, B.C., Canada, pp. 270-278.
31. Chang C, Glover GH. 2010. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage 50:81-98.
32. Chen Y, Namburi P, Elliott L, Heinzle J, Soon C, Chee M, Haynes J-D. 2011. Cortical surface-based searchlight decoding. Neuroimage 56:582-592.
33. Chickering D, Geiger D, Heckerman D. 1995. Learning Bayesian networks: search methods and experimental results. In Proceedings of the Fifth Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, F.L., pp. 112-128.
34. Clark C, Ammann W, Martin W, Ty P, Hayden M. 1991. The FDG/PET methodology for early detection of disease onset: a statistical model. J Cereb Blood Flow Metab 11:A96-A102.
35. Cohen R, Havlin S. 2003. Scale-free networks are ultrasmall. Phys Rev Lett 90:058701.
36. Cox D, Savoy R. 2003. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19(2 Pt 1):261-270.
37. Craddock R, James G, Holtzheimer P, Hu X, Mayberg H. 2012. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 33:1914-1928.
38. Daubechies I, Roussos E, Takerkart S, Benharrosh M, Golden C, D'Ardenne K, Richter W, Cohen J, Haxby J. 2009. Independent component analysis for brain fMRI does not select for independence. PNAS 106:10415-10422.
39. De Martino F, Valente G, Staeren N, Ashburner J, Goebel R, Formisano E. 2008. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 43:44-58.
40. Deshpande G, LaConte S, James G, Peltier S, Hu X. 2009. Multivariate granger causality analysis of fMRI data. Hum Brain Mapp 30:1361-1373.
41. Deshpande G, Santhanam P, Hu X. 2011. Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data. Neuroimage 54:1043-1052.
42. Deshpande G, Sathian K, Hu X. 2010. Effect of hemodynamic variability on granger causality analysis of fMRI. Neuroimage 52:884-896.
43. Diedrichsen J, Wiestler T, Krakauer JW. 2013. Two Distinct ipsilateral cortical representations for individuated finger movements. Cereb Cortex 23:1362-1377.
44. Eguãluz V, Chialvo D, Cecchi G, Baliki M, Apkarian A. 2005. Scale-free brain functional networks. Phys Rev Lett 94:018102.
45. Eickhoff S, Stephan K, Mohlberg H, Grefkes C, Fink G, Amunts K, Zilles K. 2005. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25:1325-1335.
46. Etzel J, Gazzola V, Keysers C. 2009. An introduction to anatomical ROI-based fMRI classification analysis. Brain Res 1282:114-125.
47. Ferdowsi S, Abolghasemi V, Makkiabadi B, Sanei S. A new spatially constrained NMF with application to fMRI. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Boston, M.A., 2011, pp. 5052-5055.
48. Formisano E, De Martino F, Valente G. 2008. Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning. Magn Reson Imaging 26:921-934.
49. Fraiman D, Chialvo D. 2012. What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations. Front Physiol 3 [Epub ahead of print]; DOI: 10.3389/fphys.2012.00307.
50. Friston K. 2011. Functional and effective connectivity: a review. Brain Connect 1:13-36.
51. Friston K, Daunizeau J, Stephan K. 2013. Model selection and gobbledygook: response to Lohmann et al. Neuroimage 75:275-278.
52. Friston K, Frith C, Liddle P, Frackowiak R. 1993. The principal component analysis of large data sets. J Cereb Blood Flow Metab 13:5-14.
53. Friston K, Harrison L, Penny W. 2003. Dynamic causal modeling. Neuroimage 19:1273-1302.
54. Geyer S, Weiss M, Reimann K, Lohmann G, Turner R. 2011. Microstructural parcellation of the human cerebral cortex - from brodmann's post-mortem map to in vivo mapping with high-field magnetic resonance imaging. Front Hum Neurosci 5:19.
55. Goldberg D, Holland J. 1988. Genetic algorithms and machine learning. Mach Learn 3:95-99.
56. Golub G, van Loan C. 1996. Matrix Computations. 3rd edition. Baltimore, MD: Johns Hopkins Univ Press.
57. Gonzalez-Castillo J, Saad Z, Handwerker D, Inati S, Brenowitz N, Ban-Dettini, P. 2012. Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. PNAS 109:5487-5492.
58. Guyon I, Elisseeff A. 2003. An introduction to variable and feature selection. J Mach Learn Res 3:1157-1182.
59. Harrison L, Penny W, Friston K. 2003. Multivariate autoregressive modeling of fMRI time series. Neuroimage 19:1477-1491.
60. Hasson U, Honey C. 2012. Future trends in neuroimaging: neural processes as expressed within real-life contexts. Neuroimage 62:1272-1278.
61. Haxby J. 2012. Multivariate pattern analysis of fMRI: the early beginnings. Neuroimage 62:852-855.
62. Haxby J, Gobbini M, Furey M, Ishai A. 2001. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293:2425-2430.
63. Haynes J-D, Rees G. 2005. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat Neurosci 8:686-691.
64. He B. 2011. Scale-free properties of the functional magnetic resonance imaging signal during rest and task. J Neurosci 31:13786-13795.
65. Hinrichs H, Heinze H, Schoenfeld M. 2006. Causal visual interactions as revealed by an information theoretic measure and fMRI. Neuroimage 31:1051-1060.
66. Hoerl A, Kennard R. 1970. Ridge regression: applications to nonorthogonal problems. Technometrics 12:69-82.
67. Hofbauer J, Sigmund K. 1988. The Theory of Evolution and Dynamical Systems. Cambridge University Press, Cambridge, UK.
68. Holland J. 2006. Studying complex adaptive systems. J Syst Sci Complex 19:1-8.
69. Honey C, Thivierge J-P, Sporns O. 2010. Can structure predict function in the human brain? Neuroimage 52:766-776.
70. Hyvãrinen A, Oja E. 2000. Independent component analysis: algorithms and application. Neural Netw 13:411-430.
71. Kamitani Y, Tong F. 2005. Decoding the visual and subjective contents of the human brain. Nat Neurosci 8:679-685.
72. Kim H, Park H. 2007. Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis. Bioinformatics 23:1495-1502.
73. Kitzbichler M, Smith M, Christensen S, Bullmore E. 2009. Broadband criticality of human brain network synchronization. PLoS Comput Biol 5:e1000314.
74. Kiviniemi V, Kantola J, Jauhiainen J, Hyvãrinen A, Tervonen O. 2003. Independent component analysis of nondeterministic fMRI signal sources. Neuroimage 19:253-260.
75. Koch C, Laurent G. 1999. Complexity and the nervous system. Science 284:96-98.
76. Kraskov A, Stãgbauer H, Grassberger P. 2004. Estimating mutual information. Phys Rev E 69:066138.
77. Kriegeskorte N. 2011. Pattern-information analysis: from stimulus decoding to computational-model testing. Neuroimage 56:411-421.
78. Kriegeskorte N, Bandettini P. 2007. Analyzing for information, not activation, to exploit high-resolution fMRI. Neuroimage 38:649-652.
79. Kriegeskorte N, Goebel R, Bandettini P. 2006. Information-based functional brain mapping. Proc Natl Acad Sci U S A 103:3863-3868.
80. Laird A, Lancaster J, Fox P. 2005. The social evolution of a human brain mapping database. NeuroInformatics 3:6577.
81. Lange N, Strother S, Anderson J, Nielsen F, Holmes A, Kolenda T, Savoy R, Hansen L. 1999. Plurality and resemblance in fMRI data analysis. Neuroimage 10:282-303.
82. Langford J. 2005. Tutorial on practical prediction theory for classification. J Mach Learn Res 6:273-306.
83. Langville A, Meyer C. 2006. Google's PageRank and Beyond: The Science of Search Engine Rankings. Princeton University Press Princeton, NJ. ISBN 0-691-12202-4.
84. Lee D, Seung H. 1999. Learning the parts of objects by nonnegative matrix factorization. Nature 401:788-791.
85. Li B, Daunizeau J, Stephan K, Penny W, Hu D, Friston K. 2011. Generalised filtering and stochastic DCM for fMRI. Neuroimage 58:442-457.
86. Li Y, Adali T, Calhoun V. 2007. Estimating the number of independent components for functional magnetic resonance imaging data. Neuroimage 28:1251-1266.
87. Lizier J, Heinzle J, Horstmann A, Haynes J, Prokopenko M. 2011. Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity. J Comput Neurosci 30:85-107.
88. Logothetis N. 2008. What we can do and what we cannot do with fMRI. Nature 453:869-878.
89. Logothetis N. 2012. Intracortical recordings and fMRI: an attempt to study operational modules and networks simultaneously. Neuroimage 62:962-069.
90. Lohmann G, Bohn S. 2002. Using replicator dynamics for analyzing fMRI data of the human brain. IEEE Trans Med Imaging 21:485-492.
91. Lohmann G, Erfurth K, Müller K, Turner R. 2012. Critical comments on dynamic causal modelling. NeuroImage 59:2322-2329.
92. Lohmann G, Margulies D, Horstmann A, Pleger B, Lepsien J. Goldhahn D, Schloegl H, Stumvoll M, Villringer A, Turner R. 2010. Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PloS ONE 5:e10232.
93. Lohmann G, Müller K, Turner R. 2013. Response to commentaries on our paper: critical comments in dynamic causal modelling. Neuroimage 75:279-281.
94. Lohmann G, Volz K, Ullsperger M. 2007. Using non-negative matrix factorization for single-trial analysis of fmri data. Neuroimage 37:1148-1160.
95. Ma S, Calhoun V, Eichele T, Du W, Adali T. 2012. Modulations of functional connectivity in the healthy and schizophrenia groups during task and rest. Neuroimage 62:1694-1704.
96. Margulies D, Boettger J, Long X, Lv Y, Kelly C, Schaefer A, Goldhahn D, Abbushi A, Milham M, Lohmann G, Villringer A. 2010. Resting developments: a review of fMRI post-processing methodologies for spontaneous brain activity. Magn Reson Mater Phys Biol Med 23:289-307.
97. McIntosh A, Bookstein F, Haxby J. 1996. Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 3:143-157.
98. McKeown M, Makeig S, Brown G, Jung T, Kindermann S, Bell A, Sejnowski T. 1998. Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Map 6:160-188.
99. Misaki M, Kim Y, Bandettini P, Kriegeskorte N. 2010. Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage 53:103-118.
100. Moeller J.R., Strother SC. 1991. Abstract of article: a regional covariance approach to the analysis of functional patterns in positron emission tomographic data. J Cereb Blood Flow Metab 11:A121-A135.
101. Mourão-Miranda J, Bokde A, Born C, Hampel H, Stetter M. 2005. Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. Neuroimage 28:980-995.
102. Müller K, Mildner T, Lohman G, von Cramon DY. 2003. Investigating the stimulus-dependent temporal dynamics of the BOLD signal using spectral methods. J Magn Reson Imaging 17:375-382.
103. Müller K, Neumann J, Grigutsch M, von Cramon D, Lohmann G. 2007. Detecting groups of coherent voxels in fMRI data using spectral analysis and replicator dynamics. J Magn Reson Imaging 26:1642-1650.
104. Mumford J, Horvath S, Oldham M, Langfelder P, Geschwind D, Poldrack R. 2010. Detecting network modules in fMRI time series: a weighted network analysis approach. Neuroimage 52:1465-1476.
105. Murphy K, Birn R, Handwerker D, Jones T, Bandettini P. 2009. The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44:893-905.
106. Naselaris T, Kay K, Nishimoto S, Gallant J. 2011. Encoding and decoding in fMRI. Neuroimage 56:400-410.
107. Neumann J, Fox P.T., Turner R, Lohmann G. 2010. Learning partially directed functional networks from meta-analysis imaging data. NeuroImage 49:1372-1384.
108. Neumann J, Lohmann G, Derrfuss J, Von Cramon D. 2005. Meta-analysis of functional imaging data using replicator dynamics. Hum Brain Mapp 25:165-173.
109. Neumann J, Lohmann G, Zysset S, Von Cramon D. 2003. Within-subject variability of BOLD response dynamics. NeuroImage 19:784-796.
110. Neumann J, Turner R, Fox P.T., Lohmann G. 2011. Exploring functional relations between brain regions from fmri meta-analysis data: comments on ramsey, spirtes, and glymour. NeuroImage 57:331-333.
111. Neumann J, von Cramon D, Forstmann B, Zysset S, Lohmann G. 2006. The parcellation of cortical areas using replicator dynamics in fMRI. NeuroImage 32:208-219.
112. Newman M. 2010. Networks: An Introduction. Oxford University Press, Oxford, UK.
113. Ng A, Jordan M. 2001. On discriminative vs. generative classifiers: a comparison of logistic regression and naive bayes. In Neural Information Processing Systems (NIPS), Vancouver, B.C., Canada.
114. Ng B, McKeown M, Abugharbieh R. 2012. Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection. IEEE Trans Med Imaging 31:576-585.
115. Nieto-Castaãon A, Ghosh S, Tourville J, Guenther F. 2003. Region of interest based analysis of functional imaging data. Neuroimage 19:1303-1316.
116. Nishimoto S, Vu A, Naselaris T, Benjamini Y, Yu B, Gallant J. 2011. Reconstructing visual experiences from brain activity evoked by natural movies. Curr Biol 21:1641-1646.
117. Norman K, Polyn S, Detre G, Haxby J. 2006. Beyond mind-reading: multivoxel pattern analysis of fMRI data. Trends Cogn Sci 10:424-430.
118. Olbrich E, Bertschinger N, Ay N, Jost J. 2008. How should complexity scale with system size? Eur Phys J B 67:407-415.
119. O'Toole A, Jiang F, Abdi H, Pãnard N, Dunlop J, Parent M. 2007. Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. J Cogn Neurosci 19:1735-1752.
120. Paatero P, Tapper U. 1994. Positive matrix factorization: a nonnegative factor model with optimal utilization of error estimates of data values. Environmetrics 5:111-126.
121. Panagiotaropoulos T, Deco G, Kapoor V, Logothetis N. 2012. Neuronal discharges and gamma oscillations explicitly reflect visual consciousness in the lateral prefrontal cortex. Neuron 74:924-935.
122. Patel R, Bowman F, Rilling J. 2006. A Bayesian approach to determining connectivity of the human brain. Hum Brain Mapp 27:267-276.
123. Pearl J. 2000. Causality. Cambridge University Press, Cambridge, UK.
124. Pearl J. 2009. Causal inference in statistics: an overview. Stat Surveys 3:96-146.
125. Pearson K. 1901. On lines and planes of closest fit to systems of points in space. Philos Mag 2:559-572.
126. Penny W, Stephan K, Daunizeau J, Rosa M, Friston K, Schofield T, Leff A. 2010. Comparing families of dynamic causal models. PLoS Comput Biol 6:e1000709.
127. Pereira F, Mitchell T, Botvinick M. 2009. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage 45:S199-S209.
128. Poldrack R. 2007. Region of interest analysis for fMRI. Soc Cogn Affect Neurosci 2:67-70.
129. Potluru V, Calhoun V. 2008. Group learning using contrast NMF: application to functional and structural MRI of schizophrenia. In IEEE International Symposium on Circuits and Systems (ISCAS 2008), Seattle, W.A., pp. 1336-1339.
130. Rickles D, Hawe P, Shiell A. 2007. A simple guide to chaos and complexity. J Epidemiol Commun Health 61:933-937.
131. Roebroeck A, Formisano E, Goebel R. 2005. Mapping directed influence over the brain using granger causality and fMRI. Neuroimage 25:230-242.
132. Rubinov M, Sporns O. 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059-1069.
133. Ryali S, Supekar K, Abrams D, Menon V. 2010. Sparse logistic regression for whole-brain classification of fMRI data. Neuroimage 51:752-764.
134. Salvador R, Suckling J, Schwarzbauer C, Bullmore E. 2005. Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Philos Trans R Soc Lond B Biol Sci 360:937-946.
135. Santos F, Pacheco J, Lenaerts T. 2006. Evolutionary dynamics of social dilemmas in structured heterogeneous populations. PNAS 103:3490-3494.
136. Schippers M, Renken R, Keysers C. 2011. The effect of intra- and inter-subject variability of hemodynamic responses on group level Granger causality analyses. Neuroimage 57:22-36.
137. Schãlkopf B, Smola A. 2002. Learning with Kernels. Cambridge, MA: The MIT Press.
138. Schreiber T. 2000. Measuring information transfer. Phys Rev Lett 85:461-464.
139. Schuster P, Sigmund K. 1983. Replicator dynamics. J Theor Biol 100:533-538.
140. Seth A, Chorley P, Barnett L. 2013. Granger causality analysis of fMRI BOLD signals is invariant to hemodynamic convolution but not downsampling. Neuroimage 65:540-555.
141. Skarda C, Freeman W. 1990. Chaos and the new science of the brain. Concepts Neurosci 1:275-285.
142. Smith S, Bandettini P, Miller K, Behrens T, Friston K, David O, Liu T, Woolrich M, Nichols T. 2012a. The danger of systematic bias in group-level FMRI-lag-based causality estimation. Neuroimage 59:1228-1229.
143. Smith S, Fox P, Miller K, Glahn D, Fox P, Mackay C, Filippini N, Watkins K, Toro R, Laird A, Beckmann C. 2009. Correspondence of the brain's functional architecture during activation and rest. PNAS 106:13040-13045.
144. Smith S, Miller K, Moeller S, Xu J, Auerbach E.J. Woolrich M, Beckmann C, Jenkinson M, Andersson J, Glasser M, Van Essen D, Feinberg D, Yacoub E, Ugurbil K. 2012b. Temporally-independent functional modes of spontaneous brain activity. Proc Natl Acad Sci U S A 109:3131-3136.
145. Smith S, Miller K, Salimi-Khorshidi G, Webster M, Beckmann C, Nichols T, Ramsey J, Woolrich M. 2011. Network modelling methods for FMRI. Neuroimage 54:875-891.
146. Song C, Makse H. 2005. Self-similarity of complex networks. Nature 433:392-395.
147. Soon C, Brass M, Heinze H, Haynes JD. 2008. Unconscious determinants of free decisions in the human brain. Nat Neurosci 11:543-545.
148. Spirtes P, Glymour C, Scheines R. 2000. Causation, Prediction, and Search. 2nd edition. New York: MIT Press.
149. Sporns O. 2011a. Networks of the Brain. Cambridge, MA: MIT Press.
150. Sporns O. 2011b. The non-random brain: efficiency, economy, and complex dynamics. Front Comput Neurosci 5:5.
151. Stam C, Reijneveld J. 2007. Graph theoretical analysis of complex networks in the brain. Nonlin Biomed Phys 1:3.
152. Stelzer J, Chen Y, Turner R. 2012. Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): random permutations and cluster size control. Neuroimage 65C:69-82.
153. Stephan K, Kasper L, Harrison L, Daunizeau J, den Ouden H, Breakspear M, Friston K. 2008. Nonlinear dynamic causal models for fmri. Neuroimage 42:649-662.
154. Steyvers M, Tenenbaum J, Wagenmakers E-J, Blum B. 2003. Inferring causal networks from observations and interventions. Cogn Sci 27:453-489.
155. Strother S. 2006. Evaluating fMRI preprocessing pipelines. IEEE Eng Med Biol Mag 25:27-41.
156. Strother S, Anderson J, Hansen L, Kjems U, Kustra R, Sidtis J, Frutiger S, Muley S, LaConte S, Rottenberg D. 2002. The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework. Neuroimage 15:747-771.
157. Tang W, Bressler S, Sylvester C, Shulman G, Corbetta M. 2012. Measuring granger causality between cortical regions from voxelwise fMRI BOLD signals with LASSO. PLoS Comput Biol 8:e1002513.
158. Tibshirani R. 1996. Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B 58:267-288.
159. Tomasi D, Volkow N. 2011. Functional connectivity hubs in the human brain. Neuroimage 57:908-917.
160. Tononi G, Sporns O, Edelman G. 1994. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci U S A 91:5033-5037.
161. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M. 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273-289.
162. Van Essen D, Ugurbil K, Auerbach E, Barch D, Behrens T, Bucholz R, Chang A, Chen L, Corbetta M, Curtiss S, Della Penna S, Feinberg D, Glasser M, Harel N, Heath A, Larson-Prior L, Marcus D, Michalareas G, Moeller S, Oostenveld R, Petersen S, Prior F, Schlaggar B, Smith S, Snyder A, Xub J, Yacoub E. 2012. The Human Connectome Project: a data acquisition perspective. Neuroimage 62:2222-2231.
163. Varoquaux G, Gramfort A, Poline J, Thirion B. 2012. Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks? J Physiol Paris 106:212-221.
164. Vãrtes, P., Alexander-Bloch A, Gogtay N, Giedd J, Rapoport J, Bullmore E. 2012. Simple models of human brain functional networks. PNAS 109:5868-5873.
165. Watts D, Strogatz D. 1998. Collective dynamics of ‘small-world’ networks. Nature 393:440-442.
166. Weissenbacher A, Kasess C, Gerstl F, Lanzenberger R, Moser E, Windischberger C. 2009. Correlations and anticorrelations in resting-state functional connectivity mri: a quantitative comparison of preprocessing strategies. Neuroimage 47:1408-1416.
167. Yourganov G, Chen X, Lukic A, Grady C, Small S, Wernick M, Strother S. 2011. Dimensionality estimation for optimal detection of functional networks in bold fmri data. Neuroimage 56:531-543.
168. Yousef M, Jung S, Showe L, Showe M. 2007. Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data. BMC Bioinform 8:144.
169. Zalesky A, Fornito A, Bullmore E. 2012. On the use of correlation as a measure of network connectivity. Neuroimage 60:2096-2106.
170. Zalesky A, Fornito A, Harding I, Cocchi L, Yücel, M., Pantelis C, Bullmore E. 2010. Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50:970-983.
171. Zhang Y, Tian J, Yuan K, Liu P, Zhuo L, Qin W, Zhao L, Liu J, von Deneen K, Klahr N, Gold M, Liu Y. 2011. Distinct resting-state brain activities in heroin-dependent individuals. Brain Res 1402:46-53.
172. Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. J R Stat Soc Ser B 67:301-320.
173. Zuo X, Ehmke R, Mennes M, Imperati D, Castellanos F, Sporns O, Milham M. 2012. Network centrality in the human functional connectome. Cereb Cortex 22:1862-1875.