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

[1]  P. Latouche,et al.  Model selection and clustering in stochastic block models based on the exact integrated complete data likelihood , 2015 .

[2]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[3]  S. Kosslyn,et al.  The Oxford handbook of cognitive neuroscience , 2013 .

[4]  Nelson J. Trujillo-Barreto,et al.  A switching multi-scale dynamical network model of EEG/MEG , 2013, NeuroImage.

[5]  Thomas E. Nichols,et al.  Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.

[6]  Dimitri Van De Ville,et al.  Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest , 2013, NeuroImage.

[7]  Martin A. Lindquist,et al.  Detecting functional connectivity change points for single-subject fMRI data , 2013, Front. Comput. Neurosci..

[8]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[9]  Firdaus Janoos,et al.  State-Space Analysis of Working Memory in Schizophrenia: An FBIRN Study , 2013, Psychometrika.

[10]  Yudong Chen,et al.  Detecting Overlapping Temporal Community Structure in Time-Evolving Networks , 2013, ArXiv.

[11]  P. Latouche,et al.  Model selection and clustering in stochastic block models with the exact integrated complete data likelihood , 2013, 1303.2962.

[12]  Paul J Laurienti,et al.  Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain*† , 2013, Statistics surveys.

[13]  Vikas Kawadia,et al.  Sequential detection of temporal communities by estrangement confinement , 2012, Scientific Reports.

[14]  O. Sporns,et al.  Network centrality in the human functional connectome. , 2012, Cerebral cortex.

[15]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

[16]  Martin A. Lindquist,et al.  Dynamic connectivity regression: Determining state-related changes in brain connectivity , 2012, NeuroImage.

[17]  David T. Jones,et al.  Non-Stationarity in the “Resting Brain’s” Modular Architecture , 2012, PloS one.

[18]  L. Pessoa,et al.  Network Analysis Reveals Increased Integration during Emotional and Motivational Processing , 2012, The Journal of Neuroscience.

[19]  Martin A Lindquist,et al.  Dissociable Influences of Opiates and Expectations on Pain , 2012, The Journal of Neuroscience.

[20]  Jiahua Chen,et al.  Inference on the Order of a Normal Mixture , 2012 .

[21]  Kristen A. Lindquist,et al.  The brain basis of emotion: A meta-analytic review , 2012, Behavioral and Brain Sciences.

[22]  S. Sreenivasan,et al.  Online detection of temporal communities in evolving networks by estrangement confinement , 2012, ArXiv.

[23]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[24]  C. Matias,et al.  New consistent and asymptotically normal parameter estimates for random‐graph mixture models , 2012 .

[25]  Lijun Zhang,et al.  Determining functional connectivity using fMRI data with diffusion-based anatomical weighting , 2009, NeuroImage.

[26]  Alain Celisse,et al.  Consistency of maximum-likelihood and variational estimators in the Stochastic Block Model , 2011, 1105.3288.

[27]  Nam P. Nguyen,et al.  Adaptive algorithms for detecting community structure in dynamic social networks , 2011, 2011 Proceedings IEEE INFOCOM.

[28]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[29]  Mark E. J. Newman,et al.  Stochastic blockmodels and community structure in networks , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[30]  J. Daudin,et al.  Accuracy of variational estimates for random graph mixture models , 2012 .

[31]  M. Boly,et al.  Breakdown of within- and between-network Resting State Functional Magnetic Resonance Imaging Connectivity during Propofol-induced Loss of Consciousness , 2010, Anesthesiology.

[32]  N. Bolger,et al.  Brain Mediators of Predictive Cue Effects on Perceived Pain , 2010, The Journal of Neuroscience.

[33]  Pengfei Li,et al.  Testing the Order of a Finite Mixture , 2010 .

[34]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[35]  Rémy Cazabet,et al.  Detection of Overlapping Communities in Dynamical Social Networks , 2010, 2010 IEEE Second International Conference on Social Computing.

[36]  V. Menon,et al.  Saliency, switching, attention and control: a network model of insula function , 2010, Brain Structure and Function.

[37]  Xenophon Papademetris,et al.  Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data , 2010, NeuroImage.

[38]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[39]  P. Bickel,et al.  A nonparametric view of network models and Newman–Girvan and other modularities , 2009, Proceedings of the National Academy of Sciences.

[40]  Angelo Bifone,et al.  Community structure in networks of functional connectivity: Resolving functional organization in the rat brain with pharmacological MRI , 2009, NeuroImage.

[41]  Sergi G. Costafreda,et al.  Pooling fMRI Data: Meta-Analysis, Mega-Analysis and Multi-Center Studies , 2009, Front. Neuroinform..

[42]  S. Rombouts,et al.  Hierarchical functional modularity in the resting‐state human brain , 2009, Human brain mapping.

[43]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[44]  Jonathan D. Power,et al.  Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..

[45]  Stephen M. Smith,et al.  Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies , 2009, NeuroImage.

[46]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[47]  Edward T. Bullmore,et al.  Age-related changes in modular organization of human brain functional networks , 2009, NeuroImage.

[48]  Justin L. Vincent,et al.  Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. , 2008, Journal of neurophysiology.

[49]  Lisa Feldman Barrett,et al.  Functional grouping and cortical–subcortical interactions in emotion: A meta-analysis of neuroimaging studies , 2008, NeuroImage.

[50]  M. Greicius.  Resting-state functional connectivity in neuropsychiatric disorders , 2008, Current opinion in neurology.

[51]  Franck Picard,et al.  A mixture model for random graphs , 2008, Stat. Comput..

[52]  Vince D. Calhoun,et al.  A method for functional network connectivity among spatially independent resting-state components in schizophrenia , 2008, NeuroImage.

[53]  Y. Kushnir,et al.  Detecting shifts in correlation and variability with application to ENSO-monsoon rainfall relationships , 2008 .

[54]  Edoardo M. Airoldi,et al.  Mixed Membership Stochastic Blockmodels , 2007, NIPS.

[55]  Angelo Bifone,et al.  Community structure and modularity in networks of correlated brain activity. , 2007, Magnetic resonance imaging.

[56]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[57]  M. Lindquist,et al.  Meta-analysis of functional neuroimaging data: current and future directions. , 2007, Social cognitive and affective neuroscience.

[58]  G. Glover,et al.  Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control , 2007, The Journal of Neuroscience.

[59]  Angelo Bifone,et al.  In vivo mapping of functional connectivity in neurotransmitter systems using pharmacological MRI , 2007, NeuroImage.

[60]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[61]  Deepayan Chakrabarti,et al.  Evolutionary clustering , 2006, KDD '06.

[62]  Kristina M. Visscher,et al.  A Core System for the Implementation of Task Sets , 2006, Neuron.

[63]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[64]  Stephen M. Smith,et al.  fMRI resting state networks define distinct modes of long-distance interactions in the human brain , 2006, NeuroImage.

[65]  Jorma Rissanen,et al.  Information and Complexity in Statistical Modeling , 2006, ITW.

[66]  Lester Melie-García,et al.  Estimating brain functional connectivity with sparse multivariate autoregression , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[67]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[68]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[69]  O. Sporns,et al.  Organization, development and function of complex brain networks , 2004, Trends in Cognitive Sciences.

[70]  E. Bullmore,et al.  Human pharmacological MRI. , 2004, Trends in pharmacological sciences.

[71]  Irene Tracey,et al.  Using fMRI to Quantify the Time Dependence of Remifentanil Analgesia in the Human Brain , 2004, Neuropsychopharmacology.

[72]  ShouYan Wang,et al.  Exact confidence interval for magnitude-squared coherence estimates , 2004, IEEE Signal Processing Letters.

[73]  Lee M. Miller,et al.  Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data , 2004, NeuroImage.

[74]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[75]  Edward E. Smith,et al.  Neuroimaging studies of working memory: , 2003, Cognitive, affective & behavioral neuroscience.

[76]  Peter D. Hoff,et al.  Latent Space Approaches to Social Network Analysis , 2002 .

[77]  Richard G. Wise,et al.  Combining fMRI with a Pharmacokinetic Model to Determine Which Brain Areas Activated by Painful Stimulation Are Specifically Modulated by Remifentanil , 2002, NeuroImage.

[78]  K. Luan Phan,et al.  Functional Neuroanatomy of Emotion: A Meta-Analysis of Emotion Activation Studies in PET and fMRI , 2002, NeuroImage.

[79]  S. L. Scott.  Bayesian Methods for Hidden Markov Models , 2002 .

[80]  M. Corbetta,et al.  Control of goal-directed and stimulus-driven attention in the brain , 2002, Nature Reviews Neuroscience.

[81]  T. Snijders,et al.  Estimation and Prediction for Stochastic Blockstructures , 2001 .

[82]  Gérard Govaert,et al.  Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[83]  A. Raftery,et al.  Bayesian Information Criterion for Censored Survival Models , 2000, Biometrics.

[84]  Chris T. VolinskyAT.  Bayesian Information Criterion for Censored Survival Models 1 , 1998 .

[85]  T. Snijders,et al.  Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure , 1997 .

[86]  S. Eddy.  Hidden Markov models. , 1996, Current opinion in structural biology.

[87]  Xiao-Li Meng,et al.  Maximum likelihood estimation via the ECM algorithm: A general framework , 1993 .

[88]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[89]  Kathryn B. Laskey,et al.  Stochastic blockmodels: First steps , 1983 .

[90]  S. Fienberg,et al.  Categorical Data Analysis of Single Sociometric Relations , 1981 .