Dynamic functional connectivity using state-based dynamic community structure: Method and application to opioid analgesia

NeuroImage

Volumn 108, Issue , 2015, Pages 274-291

  Robinson, Lucy F ^a   Atlas, Lauren Y ^b   Wager, Tor D ^c  

^a DREXEL UNIVERSITY   (United States)

^b NEW YORK UNIVERSITY   (United States)

^c UNIVERSITY OF COLORADO   (United States)

Author keywords

FMRI; Functional connectivity

Indexed keywords

OPIATE; NARCOTIC ANALGESIC AGENT;

ANALGESIA; ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN REGION; CONNECTOME; EMOTION; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; HIDDEN MARKOV MODEL; MATHEMATICAL COMPUTING; MATHEMATICAL PHENOMENA; MONTE CARLO METHOD; NERVE CELL NETWORK; NEUROMODULATION; PAIN; PLOTS AND CURVES; PROBABILITY; STATE BASED DYNAMIC COMMUNITY STRUCTURE; STATISTICAL MODEL; STOCHASTIC BLOCKMODEL; VOXEL BASED MORPHOMETRY; WORKING MEMORY; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; DRUG EFFECTS; HUMAN; IMAGE PROCESSING; NERVE TRACT; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES;

ANALGESICS, OPIOID; BRAIN; BRAIN MAPPING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MARKOV CHAINS; MODELS, NEUROLOGICAL; NEURAL PATHWAYS;

EID: 84921314136     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.12.034     Document Type: Article

References (87)

1. Airoldi E.M., Blei D.M., Fienberg S.E., Xing E.P. Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 2008, 9:1981-2014.
2. Allen E.A., Damaraju E., Plis S.M., Erhardt E.B., Eichele T., Calhoun V.D. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 2012, bhs352.
3. Ambroise C., Matias C. New consistent and asymptotically normal parameter estimates for random-graph mixture models. J. R. Stat. Soc. Ser. B (Stat Methodol.) 2012, 74:3-35.
4. Atlas L.Y., Wager T.D. Expectancies and beliefs: insights from cognitive neuroscience. The Oxford Handbook of Cognitive Neuroscience 2013, vol. 2:359-381. The Cutting Edges.
5. Atlas L.Y., Bolger N., Lindquist M.A., Wager T.D. Brain mediators of predictive cue effects on perceived pain. J. Neurosci. 2010, 30:12964-12977.
6. Atlas L.Y., Whittington R.A., Lindquist M.A., Wielgosz J., Sonty N., Wager T.D. Dissociable influences of opiates and expectations on pain. J. Neurosci. 2012, 32:8053-8064.
7. Bassett D.S., Wymbs N.F., Porter M.A., Mucha P.J., Carlson J.M., Grafton S.T. Dynamic reconfiguration of human brain networks during learning. Proc. Natl. Acad. Sci. U. S. A. 2011, 108:7641-7646.
8. Beckmann C.F., DeLuca M., Devlin J.T., Smith S.M. Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B Biol. Sci. 2005, 360:1001-1013.
9. Bickel P.J., Chen A. A nonparametric view of network models and Newman-Girvan and other modularities. Proc. Natl. Acad. Sci. 2009, 106:21068-21073.
10. Biernacki C., Celeux G., Govaert G. Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22:719-725.
11. Boveroux P., Vanhaudenhuyse A., Bruno M.-A., Noirhomme Q., Lauwick S., Luxen A., Degueldre C., Plenevaux A., Schnakers C., Phillips C., et al. Breakdown of within-and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness. Anesthesiology 2010, 113:1038-1053.
12. Bowman F.D., Zhang L., Derado G., Chen S. Determining functional connectivity using fMRI data with diffusion-based anatomical weighting. NeuroImage 2012, 62:1769-1779.
13. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 2009, 10:186-198.
14. Cazabet R., Amblard F., Hanachi C. Detection of overlapping communities in dynamical social networks. Social Computing (SocialCom), 2010 IEEE Second International Conference on 2010, 309-314. IEEE.
15. Celisse A., Daudin J.-J., Pierre L., et al. Consistency of maximum-likelihood and variational estimators in the stochastic block model. Electron. J. Stat. 2012, 6:1847-1899.
16. Chakrabarti D., Kumar R., Tomkins A. Evolutionary clustering. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006, 554-560. ACM.
17. Chen J., Li P., Fu Y. Inference on the order of a normal mixture. J. Am. Stat. Assoc. 2012, 107:1096-1105.
18. Chen Y., Kawadia V., Urgaonkar R. Detecting overlapping temporal community structure in time-evolving networks 2013, (arXiv preprint arXiv:1303.7226).
19. CÔme E., Latouche P. Model selection and clustering in stochastic block models with the exact integrated complete data likelihood 2013, (ArXiv e-prints).
20. Corbetta M., Shulman G.L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 2002, 3:201-215.
21. Costafreda S.G. Pooling fMRI data: meta-analysis, mega-analysis and multi-center studies. Front. Neuroinformatics 2009, 3.
22. Craddock R., James A., Holtzenheimer P., Hu X., Mayberg H. Community structure in networks of functional connectivity: resolving functional organization in the rat brain with pharmacological MRI. Hum. Brain Mapp. 2012, 33:1914-1928.
23. Cribben I., Haraldsdottir R., Atlas L.Y., Wager T.D., Lindquist M.A. Dynamic connectivity regression: determining state-related changes in brain connectivity. NeuroImage 2012, 61:907-920.
24. Cribben I., Wager T., Lindquist M. Detecting functional connectivity change points for single-subject fMRI data. Front. Comput. Neurosci. 2013, 7.
25. Damoiseaux J., Rombouts S., Barkhof F., Scheltens P., Stam C., Smith S.M., Beckmann C. Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. 2006, 103:13848-13853.
26. Daudin J.J., Picard F., Robin S. A mixture model for random graphs. Stat. Comput. 2008, 18:173-183.
27. De Luca M., Beckmann C., De Stefano N., Matthews P., Smith S.M. fMRI resting state networks define distinct modes of long-distance interactions in the human brain. NeuroImage 2006, 29:1359-1367.
28. Dosenbach N.U., Visscher K.M., Palmer E.D., Miezin F.M., Wenger K.K., Kang H.C., Burgund E.D., Grimes A.L., Schlaggar B.L., Petersen S.E. A core system for the implementation of task sets. Neuron 2006, 50:799-812.
29. Eickhoff S.B., Stephan K.E., Mohlberg H., Grefkes C., Fink G.R., Amunts K., Zilles K. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 2005, 25:1325-1335.
30. Elliott R.J., Aggoun L., Moore J.B. Hidden Markov Models 1995, Springer.
31. Fair D.A., Cohen A.L., Power J.D., Dosenbach N.U.F., Church J.A., Miezin F.M., Schlaggar B.L., Petersen S.E. Functional brain networks develop from a "local to distributed" organization. PLoS Comput. Biol. 2009, 5.
32. Ferrarini L., Veer I.M., Baerends E., van Tol M.J., Renken R.J., van der Wee N.J.A., Veltman D.J., Aleman A., Zitman F.G., Penninx B., van Buchem M.A., Reiber J.H.C., Rombouts S., Milles J. Hierarchical functional modularity in the resting-state human brain. Hum. Brain Mapp. 2009, 30:2220-2231.
33. Fienberg S.E., Wasserman S.S. Categorical data analysis of single sociometric relations. Sociol. Methodol. 1981, 156-192.
34. Fortunato S. Community detection in graphs. Phys. Rep. 2010, 486:75-174.
35. Gazal S., Daudin J.-J., Robin S. Accuracy of variational estimates for random graph mixture models. J. Stat. Comput. Simul. 2012, 82:849-862.
36. Greicius M. Resting-state functional connectivity in neuropsychiatric disorders. Curr. Opin. Neurol. 2008, 21:424-430.
37. Hoff P., Raftery A., Handcock M. Latent space approaches to social network analysis. J. Am. Stat. Assoc. 2002, 79.
38. Holland P.W., Laskey K.B., Leinhardt S. Stochastic blockmodels: first steps. Soc. Networks 1983, 5:109-137.
39. Honey G., Bullmore E. Human pharmacological MRI. Trends Pharmacol. Sci. 2004, 25:366-374.
40. Hutchison R.M., Womelsdorf T., Allen E.A., Bandettini P.A., Calhoun V.D., Corbetta M., Penna S.D., Duyn J., Glover G., Gonzalez-Castillo J., et al. Dynamic functional connectivity: promises, issues, and interpretations. NeuroImage 2013, 80:360-378.
41. Jafri M.J., Pearlson G.D., Stevens M., Calhoun V.D. A method for functional network connectivity among spatially independent resting-state components in schizophrenia. NeuroImage 2008, 39:1666-1681.
42. Janoos F., Brown G., Mórocz I.Á., Wells W.M. State-space analysis of working memory in schizophrenia: an FBIRN study. Psychometrika 2013, 78:279-307.
43. Jones D.T., Vemuri P., Murphy M.C., Gunter J.L., Senjem M.L., Machulda M.M., Przybelski S.A., Gregg B.E., Kantarci K., Knopman D.S., Boeve B.F., Petersen R.C., Jack C.R. Non-stationarity in the resting brains modular architecture. PLoS ONE 2012, 7:e39731.
44. Jordan M.I., Ghahramani Z., Jaakkola T.S., Saul L.K. An introduction to variational methods for graphical models. Mach. Learn. 1999, 37:183-233.
45. Karrer B., Newman M.E. Stochastic blockmodels and community structure in networks. Phys. Rev. E. 2011, 83:016107.
46. Kawadia V., Sreenivasan S. Sequential detection of temporal communities by estrangement confinement. Sci. Rep. 2012, 2.
47. Kinnison J., Padmala S., Choi J.-M., Pessoa L. Network analysis reveals increased integration during emotional and motivational processing. J. Neurosci. 2012, 32:8361-8372.
48. Kober H., Barrett L.F., Joseph J., Bliss-Moreau E., Lindquist K., Wager T.D. Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies. NeuroImage 2008, 42:998-1031.
49. Leonardi N., Richiardi J., Gschwind M., Simioni S., Annoni J.-M., Schluep M., Vuilleumier P., Ville D.V.D. Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. NeuroImage 2013, 83:937-950.
50. Li P., Chen J. Testing the order of a finite mixture. J. Am. Stat. Assoc. 2010, 105.
51. Lindquist K.A., Wager T.D., Kober H., Bliss-Moreau E., Barrett L.F. The brain basis of emotion: a meta-analytic review. Behav. Brain Sci. 2012, 35:121-143.
52. Meng X.-L., Rubin D.B. Maximum likelihood estimation via the ECM algorithm: a general framework. Biometrika 1993, 80:267-278.
53. Menon V., Uddin L.Q. Saliency, switching, attention and control: a network model of insula function. Brain Struct. Funct. 2010, 214:655-667.
54. Meunier D., Achard S., Morcom A., Bullmore E. Age-related changes in modular organization of human brain functional networks. NeuroImage 2009, 44:715-723.
55. Mucha P.J., Richardson T., Macon K., Porter M.A., Onnela J.-P. Community structure in time-dependent, multiscale, and multiplex networks. Science 2010, 328:876-878.
56. Newman M. Modularity and community structure in networks. Proc. Natl. Acad. Sci. 2006, 103:8577-8582.
57. Nguyen N.P., Dinh T.N., Xuan Y., Thai M.T. Adaptive algorithms for detecting community structure in dynamic social networks. INFOCOM, 2011 Proceedings IEEE 2011, 2282-2290. IEEE.
58. Nowicki K., Snijders T.A.B. Estimation and prediction for stochastic blockstructures. J. Am. Stat. Assoc. 2001, 96:1077-1087.
59. Olier I., Trujillo-Barreto N.J., El-Deredy W. A switching multi-scale dynamical network model of EEG/MEG. NeuroImage 2013, 83:262-287.
60. Phan K.L., Wager T., Taylor S.F., Liberzon I. Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. NeuroImage 2002, 16:331-348.
61. Rabiner L. A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 1989, 77:257-286.
62. Rissanen J. Information and complexity in statistical modeling 2007, Springer.
63. Robinson L.F., de la Peña V.H., Kushnir Y. Detecting shifts in correlation and variability with application to ENSO-monsoon rainfall relationships. Theor. Appl. Climatol. 2008, 94:215-224.
64. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 2010, 52:1059-1069.
65. Salimi-Khorshidi G., Smith S.M., Keltner J.R., Wager T.D., Nichols T.E. Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. NeuroImage 2009, 45:810-823.
66. Schwarz A.J., Gozzi A., Reese T., Bifone A. In vivo mapping of functional connectivity in neurotransmitter systems using pharmacological MRI. NeuroImage 2007, 34:1627-1636.
67. Schwatz A., Gozzi A., Bifone A. Community structure and modularity in networks of correlated brain activity. Magn. Reson. Imaging 2008, 26:914-920.
68. Schwatz A., Gozzi A., Bifone A. Community structure in networks of functional connectivity: resolving functional organization in the rat brain with pharmacological MRI. NeuroImage 2009, 47:302-311.
69. Scott S.L. Bayesian methods for hidden Markov models. J. Am. Stat. Assoc. 2002, 97.
70. Seeley W.W., Menon V., Schatzberg A.F., Keller J., Glover G.H., Kenna H., Reiss A.L., Greicius M.D. Dissociable intrinsic connectivity networks for salience processing and executive control. J. Neurosci. 2007, 27:2349-2356.
71. Shen X., Papademetris X., Constable R.T. Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data. NeuroImage 2010, 50:1027-1035. (times Cited: 27 Shen, X. Papademetris, X. Constable, R. T. Shen, Xilin /F-4349-2011 27).
72. Simpson S.L., Bowman F.D., Laurienti P.J., et al. Analyzing complex functional brain networks: fusing statistics and network science to understand the brain. Stat. Surv. 2013, 7:1-36.
73. Smith S.M., Vidaurre D., Beckmann C.F., Glasser M.F., Jenkinson M., Miller K.L., Nichols T.E., Robinson E.C., Salimi-Khorshidi G., Woolrich M.W., Barch D.M., UÄŸurbil K., Essen D.C.V., Smith S.M., Vidaurre D., Beckmann C.F., Glasser M.F., Jenkinson M., Miller K.L., Nichols T.E., Robinson E.C., Salimi-Khorshidi G., Woolrich M.W., Barch D.M., UÄŸurbil K., Essen D.C.V. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 2013, 17:666-682. (special Issue: The Connectome).
74. Snijders T., Nowicki K. Estimation and prediction for stochastic blockmodels for graphs with latent block structure. J. Classif. 1997, 75.
75. Sporns O., Chialvo D.R., Kaiser M., Hilgetag C.C. Organization, development and function of complex brain networks. Trends Cogn. Sci. 2004, 8:418-425.
76. Sun F.T., Miller L.M., D'Esposito M. Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage 2004, 21:647-658.
77. Valdés-Sosa P.A., Sánchez-Bornot J.M., Lage-Castellanos A., Vega-Hernández M., Bosch-Bayard J., Melie-Garca L., Canales-Rodrguez E. Estimating brain functional connectivity with sparse multivariate autoregression. Philos. Trans. R. Soc. B Biol. Sci. 2005, 360:969-981.
78. Van Dijk K.R., Sabuncu M.R., Buckner R.L. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 2012, 59:431-438.
79. Vincent J.L., Kahn I., Snyder A.Z., Raichle M.E., Buckner R.L. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J. Neurophysiol. 2008, 100:3328-3342.
80. Volinsky C.T., Raftery A.E. Bayesian information criterion for censored survival models. Biometrics 2000, 56:256-262.
81. Von Luxburg U. A tutorial on spectral clustering. Stat. Comput. 2007, 17:395-416.
82. Wager T.D., Smith E.E. Neuroimaging studies of working memory. Cogn. Affect. Behav. Neurosci. 2003, 3:255-274.
83. Wager T.D., Lindquist M., Kaplan L. Meta-analysis of functional neuroimaging data: current and future directions. Soc. Cogn. Affect. Neurosci. 2007, 2:150-158.
84. Wang S., Tang M. Exact confidence interval for magnitude-squared coherence estimates. IEEE Signal Process. Lett. 2004, 11:326-329.
85. Wise R.G., Rogers R., Painter D., Bantick S., Ploghaus A., Williams P., Rapeport G., Tracey I. Combining fMRI with a pharmacokinetic model to determine which brain areas activated by painful stimulation are specifically modulated by remifentanil. NeuroImage 2002, 16:999-1014.
86. Wise R.G., Williams P., Tracey I. Using fMRI to quantify the time dependence of remifentanil analgesia in humans. Neuropsychopharmacology 2004, 29:626-635.
87. Zuo X.N., Ehmke R., Mennes M., Imperati D., Castellanos F.X., Sporns O., Milham M.P. Network centrality in the human functional connectome.Ê. Cereb. Cortex 2012, 22:1862-1875.