Bayesian Methods in Brain Networks

[1]  Kim-Anh Do,et al.  A Bayesian predictive model for imaging genetics with application to schizophrenia , 2016 .

[2]  Ying Guo,et al.  Integrative Bayesian analysis of brain functional networks incorporating anatomical knowledge , 2018, NeuroImage.

[3]  C. Stam.  Modern network science of neurological disorders , 2014, Nature Reviews Neuroscience.

[4]  S. MacEachern,et al.  Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing , 2005 .

[5]  J. Marron,et al.  Object oriented data analysis: Sets of trees , 2007, 0711.3147.

[6]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[7]  Luca Ambrogioni,et al.  Structurally-informed Bayesian functional connectivity analysis , 2014, NeuroImage.

[8]  Pavel N Krivitsky,et al.  Fitting Position Latent Cluster Models for Social Networks with latentnet. , 2008, Journal of statistical software.

[9]  Alberto Caimo,et al.  Bayesian inference for exponential random graph models , 2010, Soc. Networks.

[10]  Sourabh Bhattacharya,et al.  A Bayesian approach to modeling dynamic effective connectivity with fMRI data , 2006, NeuroImage.

[11]  Daniele Durante,et al.  Spatial modeling of brain connectivity data via latent distance models with nodes clustering , 2019, Stat. Anal. Data Min..

[12]  F. DuBois Bowman,et al.  A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity , 2015, Front. Comput. Neurosci..

[13]  S. Balqis Samdin,et al.  A Unified Estimation Framework for State-Related Changes in Effective Brain Connectivity , 2017, IEEE Transactions on Biomedical Engineering.

[14]  Marina Vannucci,et al.  Bayesian vector autoregressive model for multi‐subject effective connectivity inference using multi‐modal neuroimaging data , 2016, Human brain mapping.

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

[16]  Marina Vannucci,et al.  A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses , 2014, NeuroImage.

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

[18]  Steven C. Cramer,et al.  Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity , 2013, Front. Comput. Neurosci..

[19]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[20]  Karl J. Friston,et al.  Classical and Bayesian Inference in Neuroimaging: Applications , 2002, NeuroImage.

[21]  G. Varoquaux,et al.  Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks? , 2012, Journal of Physiology-Paris.

[22]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[23]  Daniela Perani,et al.  Brain Molecular Connectivity in Neurodegenerative Diseases: Recent Advances and New Perspectives Using Positron Emission Tomography , 2019, Front. Neurosci..

[24]  Marina Vannucci,et al.  Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity , 2018, PloS one.

[25]  Cornelis J. Stam,et al.  Bayesian exponential random graph modeling of whole-brain structural networks across lifespan , 2016, NeuroImage.

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

[27]  Karl J. Friston.  Functional and Effective Connectivity: A Review , 2011, Brain Connect..

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

[29]  Danielle S Bassett,et al.  Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.

[30]  Alan E. Gelfand,et al.  Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data , 2017 .

[31]  Rajan S. Patel,et al.  Determining hierarchical functional networks from auditory stimuli fMRI , 2006, Human brain mapping.

[32]  R. Engle.  Dynamic Conditional Correlation , 2002 .

[33]  Junwei Han,et al.  Inferring functional interaction and transition patterns via dynamic bayesian variable partition models , 2013, Human brain mapping.

[34]  Daniele Durante,et al.  Connecting statistical brains , 2018 .

[35]  Olaf Sporns,et al.  Weighted Stochastic Block Models of the Human Connectome across the Life Span , 2018, Scientific Reports.

[36]  Tom Heskes,et al.  Bayesian inference of structural brain networks , 2013, NeuroImage.

[37]  R. Maitra,et al.  A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments , 2011, 1107.4181.

[38]  J. Gold,et al.  On the nature and use of models in network neuroscience , 2018, Nature Reviews Neuroscience.

[39]  O. Sporns.  Structure and function of complex brain networks , 2013, Dialogues in clinical neuroscience.

[40]  David B. Dunson,et al.  Mapping population-based structural connectomes , 2018, NeuroImage.

[41]  Hao Wang,et al.  Bayesian Graphical Lasso Models and Efficient Posterior Computation , 2012 .

[42]  Stephen M Smith,et al.  Fast transient networks in spontaneous human brain activity , 2014, eLife.

[43]  Marina Vannucci,et al.  Time-dependence of graph theory metrics in functional connectivity analysis , 2016, NeuroImage.

[44]  Daniele Durante,et al.  Bayesian Inference and Testing of Group Differences in Brain Networks , 2014, 1411.6506.

[45]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[46]  Marina Vannucci,et al.  Bayesian models for functional magnetic resonance imaging data analysis , 2014 .

[47]  S. Jbabdi,et al.  How can a Bayesian approach inform neuroscience? , 2012, The European journal of neuroscience.

[48]  Marina Vannucci,et al.  A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data , 2016 .

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

[50]  Martin A. Lindquist,et al.  Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach , 2014, NeuroImage.

[51]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[52]  Edoardo M. Airoldi,et al.  A Survey of Statistical Network Models , 2009, Found. Trends Mach. Learn..

[53]  Marina Vannucci,et al.  A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data , 2018, Journal of the American Statistical Association.

[54]  C. Granger.  Investigating Causal Relations by Econometric Models and Cross-Spectral Methods , 1969 .

[55]  J. Shimony,et al.  Resting-State fMRI: A Review of Methods and Clinical Applications , 2013, American Journal of Neuroradiology.

[56]  Abel Rodriguez,et al.  Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data , 2010, Journal of the American Statistical Association.

[57]  Steven C Cramer,et al.  Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach , 2016, Journal of the American Statistical Association.

[58]  Terrence J. Sejnowski.  Well-connected Brains , 2012 .

[59]  Edward T. Bullmore,et al.  Small-World Brain Networks Revisited , 2016, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

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

[61]  A. Roverato.  Hyper Inverse Wishart Distribution for Non-decomposable Graphs and its Application to Bayesian Inference for Gaussian Graphical Models , 2002 .

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

[63]  Tom Heskes,et al.  Quantifying uncertainty in brain network measures using Bayesian connectomics , 2014, Front. Comput. Neurosci..

[64]  Andreas Hahn,et al.  Making Sense of Connectivity , 2018, The international journal of neuropsychopharmacology.

[65]  Paul Thompson,et al.  Blockmodels for connectome analysis , 2015, Symposium on Medical Information Processing and Analysis.

[66]  Brian Caffo,et al.  A Bayesian hierarchical framework for spatial modeling of fMRI data , 2008, NeuroImage.

[67]  Peng Wang,et al.  Recent developments in exponential random graph (p*) models for social networks , 2007, Soc. Networks.

[68]  Hernando Ombao,et al.  Coherence-based time series clustering for statistical inference and visualization of brain connectivity , 2019, Annals of Applied Statistics.

[69]  Karl J. Friston,et al.  Analysis of functional MRI time‐series , 1994, Human Brain Mapping.

[70]  Garry Robins,et al.  An introduction to exponential random graph (p*) models for social networks , 2007, Soc. Networks.

[71]  Keith Heberlein,et al.  Imaging human connectomes at the macroscale , 2013, Nature Methods.

[72]  Daniele Durante,et al.  Nonparametric Bayes Modeling of Populations of Networks , 2014, 1406.7851.