Effective connectivity analysis of fMRI and MEG data collected under identical paradigms

Estimation of effective connectivity, a measure of the influence among brain regions, can potentially reveal valuable information about organization of brain networks. Effective connectivity is usually evaluated from the functional data of a single modality. In this paper we show why that may lead to incorrect conclusions about effective connectivity. In this paper we use Bayesian networks to estimate connectivity on two different modalities. We analyze structures of estimated effective connectivity networks using aggregate statistics from the field of complex networks. Our study is conducted on functional MRI and magnetoencephalography data collected from the same subjects under identical paradigms. Results showed some similarities but also revealed some striking differences in the conclusions one would make on the fMRI data compared with the MEG data and are strongly supportive of the use of multiple modalities in order to gain a more complete picture of how the brain is organized given the limited information one modality is able to provide.

[1]  Béla Bollobás,et al.  Random Graphs , 1985 .

[2]  Vince D. Calhoun,et al.  Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data , 2010, NeuroImage.

[3]  Jun S. Liu,et al.  Monte Carlo strategies in scientific computing , 2001 .

[4]  Karl J. Friston,et al.  Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization , 2006, NeuroImage.

[5]  Karl J. Friston.  Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[6]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[7]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[8]  W. Gilks.  Markov Chain Monte Carlo , 2005 .

[9]  Vince D. Calhoun,et al.  MEG and fMRI Fusion for Non-Linear Estimation of Neural and BOLD Signal Changes , 2010, Front. Neuroinform..

[10]  R. Näätänen,et al.  The mismatch negativity (MMN) in basic research of central auditory processing: A review , 2007, Clinical Neurophysiology.

[11]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[12]  Terran Lane,et al.  Learning class-discriminative dynamic Bayesian networks , 2005, ICML.

[13]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[14]  L. Shah,et al.  Functional magnetic resonance imaging. , 2010, Seminars in roentgenology.

[15]  Rong Chen,et al.  Network analysis of mild cognitive impairment , 2006, NeuroImage.

[16]  R. Quiroga,et al.  Unmixing concurrent EEG-fMRI with parallel independent component analysis. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[17]  Yul-Wan Sung,et al.  Functional magnetic resonance imaging , 2004, Scholarpedia.

[18]  M. Gerstein,et al.  A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data , 2003, Science.

[19]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[20]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[21]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[22]  Karl J. Friston,et al.  Dynamic causal modelling for fMRI: A two-state model , 2008, NeuroImage.

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

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

[25]  Michael I. Jordan,et al.  Probabilistic Networks and Expert Systems , 1999 .

[26]  Sergey M. Plis,et al.  Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data , 2005, NeuroImage.

[27]  Rainer Goebel,et al.  Mapping directed influence over the brain using Granger causality and fMRI , 2005, NeuroImage.

[28]  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.

[29]  A. Delcher,et al.  Protein secondary structure modelling with probabilistic networks. , 1993, Proceedings. International Conference on Intelligent Systems for Molecular Biology.

[30]  W. Daniel Hillis,et al.  The connection machine , 1985 .

[31]  Peter J. Woolf,et al.  Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data , 2009, J. Mach. Learn. Res..

[32]  M. Newman.  Erratum: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality (Physical Review e (2001) 64 (016132)) , 2006 .

[33]  Hamilton E. Link,et al.  Discrete dynamic Bayesian network analysis of fMRI data , 2009, Human brain mapping.

[34]  Aggelos K. Katsaggelos,et al.  Signal Processing and Communications , 2001 .

[35]  A. Dale,et al.  Distributed current estimates using cortical orientation constraints , 2006, Human brain mapping.

[36]  Kenneth Hugdahl,et al.  Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[37]  P. Thiran,et al.  Mapping Human Whole-Brain Structural Networks with Diffusion MRI , 2007, PloS one.

[38]  Vince D. Calhoun,et al.  Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia , 2008, NeuroImage.

[39]  Barak A. Pearlmutter,et al.  Multimodal Integration : fMRI , MRI , EEG , MEG , 2022 .

[40]  David Poeppel,et al.  Reconstructing spatio-temporal activities of neural sources using an MEG vector beamformer technique , 2001, IEEE Transactions on Biomedical Engineering.

[41]  Martin A. Nowak,et al.  Inferring Cellular Networks Using Probabilistic Graphical Models , 2004 .

[42]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[43]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[44]  Marco Grzegorczyk,et al.  Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move , 2008, Machine Learning.

[45]  Karl J. Friston,et al.  Dynamic causal modeling: A generative model of slice timing in fMRI , 2007, NeuroImage.

[46]  M. Scherg,et al.  Localizing P300 Generators in Visual Target and Distractor Processing: A Combined Event-Related Potential and Functional Magnetic Resonance Imaging Study , 2004, The Journal of Neuroscience.

[47]  R. Benson,et al.  Responses to rare visual target and distractor stimuli using event-related fMRI. , 2000, Journal of neurophysiology.

[48]  D. Le Bihan,et al.  Diffusion tensor imaging: Concepts and applications , 2001, Journal of magnetic resonance imaging : JMRI.

[49]  Michael I. Jordan.  Learning in Graphical Models , 1999, NATO ASI Series.

[50]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[51]  Winfried Schlee,et al.  Top-Down Modulation of the Auditory Steady-State Response in a Task-Switch Paradigm , 2008, Front. Hum. Neurosci..

[52]  Finn V. Jensen,et al.  Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.

[53]  Martin J. McKeown,et al.  Dynamic Bayesian network modeling of fMRI: A comparison of group-analysis methods , 2008, NeuroImage.

[54]  Martin J. McKeown,et al.  Dynamic Bayesian networks (DBNS) demonstrate impaired brain connectivity during performance of simultaneous movements in Parkinson's disease , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[55]  V. Calhoun,et al.  Functional Brain Networks in Schizophrenia: A Review , 2009, Front. Hum. Neurosci..

[56]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[57]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[58]  Vince D. Calhoun,et al.  Efficient sequential inference in DBNs : steps towards joint MEG / fMRI connectivity analysis , 2009 .

[59]  T. Picton,et al.  The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. , 1987, Psychophysiology.

[60]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[61]  Olaf Hauk,et al.  Keep it simple: a case for using classical minimum norm estimation in the analysis of EEG and MEG data , 2004, NeuroImage.

[62]  大西 仁,et al.  Pearl, J. (1988, second printing 1991). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann. , 1994 .

[63]  Gustavo Deco,et al.  Stochastic dynamics as a principle of brain function , 2009, Progress in Neurobiology.

[64]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[65]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[66]  J. Geweke,et al.  Measurement of Linear Dependence and Feedback between Multiple Time Series , 1982 .

[67]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[68]  Jérémie Mattout,et al.  Symmetrical event-related EEG/fMRI information fusion in a variational Bayesian framework , 2007, NeuroImage.

[69]  Peter Green,et al.  Markov chain Monte Carlo in Practice , 1996 .

[70]  Silke Dodel,et al.  Analysis of correlated activity in fMRI data by artificial neural networks , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[71]  Simon Kasif,et al.  Protein Secondary-Structure Modeling with Probabilistic Networks , 1993, ISMB.

[72]  Nikos K Logothetis,et al.  Interpreting the BOLD signal. , 2004, Annual review of physiology.

[73]  T W Picton,et al.  The P300 Wave of the Human Event‐Related Potential , 1992, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[74]  M. Newman,et al.  Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[75]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[76]  O. Sporns,et al.  Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.