Brain‐wide network analysis of resting‐state neuromagnetic data

The present study performed a brain‐wide network analysis of resting‐state magnetoencephalograms recorded from 53 healthy participants to visualize elaborate brain maps of phase‐ and amplitude‐derived graph‐theory metrics at different frequencies. To achieve this, we conducted a vertex‐wise computation of threshold‐independent graph metrics by combining proportional thresholding and a conjunction analysis and applied them to a correlation analysis of age and brain networks. Source power showed a frequency‐dependent cortical distribution. Threshold‐independent graph metrics derived from phase‐ and amplitude‐based connectivity showed similar or different distributions depending on frequency. Vertex‐wise age‐brain correlation maps revealed that source power at the beta band and the amplitude‐based degree at the alpha band changed with age in local regions. The present results indicate that a brain‐wide analysis of neuromagnetic data has the potential to reveal neurophysiological network features in the human brain in a resting state.

[1]  R. Constable,et al.  Inside information: Systematic within-node functional connectivity changes observed across tasks or groups , 2021, NeuroImage.

[2]  Margot J. Taylor,et al.  Using OPM-MEG in contrasting magnetic environments , 2021, NeuroImage.

[3]  K. Tsvetanov,et al.  The “Neural Shift” of Sleep Quality and Cognitive Aging: A Resting-State MEG Study of Transient Neural Dynamics , 2022, Frontiers in Aging Neuroscience.

[4]  Matti Stenroos,et al.  Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design , 2021, NeuroImage.

[5]  T. Bardouille,et al.  Age‐related trends in the cortical sources of transient beta bursts during a sensorimotor task and rest , 2021, NeuroImage.

[6]  Sylvain Baillet,et al.  Brief segments of neurophysiological activity enable individual differentiation , 2021, Nature Communications.

[7]  R. Constable,et al.  Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain , 2021, NeuroImage.

[8]  Z. Kurth-Nelson,et al.  Impaired neural replay of inferred relationships in schizophrenia , 2021, Cell.

[9]  M. Hoshiyama,et al.  Chronic pain-related cortical neural activity in patients with complex regional pain syndrome , 2021, IBRO neuroscience reports.

[10]  Matthew J. Brookes,et al.  Theoretical advantages of a triaxial optically pumped magnetometer magnetoencephalography system , 2021, NeuroImage.

[11]  A. Griffa,et al.  Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations , 2021, NeuroImage.

[12]  K. Davis,et al.  Magnetoencephalography: Physics, techniques and applications in the basic and clinical neurosciences. , 2021, Journal of neurophysiology.

[13]  Darren Price,et al.  Transient neural network dynamics in cognitive ageing , 2021, Neurobiology of Aging.

[14]  KongFatt Wong-Lin,et al.  High-dimensional brain-wide functional connectivity mapping in magnetoencephalography , 2020, Journal of Neuroscience Methods.

[15]  Matthew J. Brookes,et al.  Measuring functional connectivity with wearable MEG , 2020, NeuroImage.

[16]  Georgios D. Mitsis,et al.  Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques , 2019, NeuroImage.

[17]  Farnaz Zamani Esfahlani,et al.  Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture , 2020, Nature Neuroscience.

[18]  T. Bardouille,et al.  Age-related trends in neuromagnetic transient beta burst characteristics during a sensorimotor task and rest in the Cam-CAN open-access dataset , 2020, NeuroImage.

[19]  Markus Siegel,et al.  Dissociated neuronal phase- and amplitude-coupling patterns in the human brain , 2020, NeuroImage.

[20]  Sara Larivière,et al.  Older adults exhibit a more pronounced modulation of beta oscillations when performing sustained and dynamic handgrips , 2019, NeuroImage.

[21]  Maurizio Corbetta,et al.  The Impact of the Geometric Correction Scheme on MEG Functional Topology at Rest , 2019, Front. Neurosci..

[22]  Mathieu Bourguignon,et al.  Synchrony, metastability, dynamic integration, and competition in the spontaneous functional connectivity of the human brain , 2019, NeuroImage.

[23]  Ruud L. van den Brink,et al.  Brainstem Modulation of Large-Scale Intrinsic Cortical Activity Correlations , 2019, Front. Hum. Neurosci..

[24]  Stephen Coombes,et al.  Relationships Between Neuronal Oscillatory Amplitude and Dynamic Functional Connectivity. , 2019, Cerebral cortex.

[25]  Stephen M. Smith,et al.  Estimation of brain age delta from brain imaging , 2019, NeuroImage.

[26]  Mark W. Woolrich,et al.  An Introduction to MEG Connectivity Measurements , 2014, Magnetoencephalography.

[27]  Matthew J. Brookes,et al.  Development of human electrophysiological brain networks , 2018, Journal of neurophysiology.

[28]  Mark W. Woolrich,et al.  Altered temporal stability in dynamic neural networks underlies connectivity changes in neurodevelopment , 2018, NeuroImage.

[29]  Ben Godde,et al.  Older adults reveal enhanced task-related beta power decreases during a force modulation task , 2018, Behavioural Brain Research.

[30]  Niall Holmes,et al.  Moving magnetoencephalography towards real-world applications with a wearable system , 2018, Nature.

[31]  Naoki Masuda,et al.  Clustering Coefficients for Correlation Networks , 2018, Front. Neuroinform..

[32]  Jian Kong,et al.  Maturation trajectories of cortical resting-state networks depend on the mediating frequency band , 2018, NeuroImage.

[33]  Matthew J. Brookes,et al.  Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures , 2017, NeuroImage.

[34]  J. Matias Palva,et al.  Hyperedge bundling: A practical solution to spurious interactions in MEG/EEG source connectivity analyses , 2017, NeuroImage.

[35]  Mark W. Woolrich,et al.  Dynamics of large-scale electrophysiological networks: A technical review , 2017, NeuroImage.

[36]  Maxim Bazhenov,et al.  Origin of slow spontaneous resting-state neuronal fluctuations in brain networks , 2017, Proceedings of the National Academy of Sciences.

[37]  P. Peigneux,et al.  The electrophysiological connectome is maintained in healthy elders: a power envelope correlation MEG study , 2017, Scientific Reports.

[38]  R. Hari From Brain–Environment Connections to Temporal Dynamics and Social Interaction: Principles of Human Brain Function , 2017, Neuron.

[39]  R. Henson,et al.  Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging , 2017, Human brain mapping.

[40]  B. T. Thomas Yeo,et al.  Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: Issues and recommendations , 2017, NeuroImage.

[41]  Cornelis J. Stam,et al.  MEG Beamformer-Based Reconstructions of Functional Networks in Mild Cognitive Impairment , 2017, Front. Aging Neurosci..

[42]  S. H. Hojjati,et al.  Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM , 2017, Journal of Neuroscience Methods.

[43]  Brady J. Williamson,et al.  Mapping Critical Language Sites in Children Performing Verb Generation: Whole-Brain Connectivity and Graph Theoretical Analysis in MEG , 2017, Front. Hum. Neurosci..

[44]  Linda Geerligs,et al.  Assessing dynamic functional connectivity in heterogeneous samples , 2017, NeuroImage.

[45]  O. Sporns,et al.  Network neuroscience , 2017, Nature Neuroscience.

[46]  Sylvain Baillet,et al.  Magnetoencephalography for brain electrophysiology and imaging , 2017, Nature Neuroscience.

[47]  Mark W. Woolrich,et al.  Measurement of dynamic task related functional networks using MEG , 2017, NeuroImage.

[48]  Matti Stenroos,et al.  Measuring MEG closer to the brain: Performance of on-scalp sensor arrays , 2016, NeuroImage.

[49]  B. Lehmanna,et al.  Assessing dynamic functional connectivity in heterogeneous samples , 2017 .

[50]  M Corbetta,et al.  A Dynamic Core Network and Global Efficiency in the Resting Human Brain. , 2016, Cerebral cortex.

[51]  Mahdi Jalili,et al.  Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter? , 2016, Scientific Reports.

[52]  Tony W. Wilson,et al.  Is an absolute level of cortical beta suppression required for proper movement? Magnetoencephalographic evidence from healthy aging , 2016, NeuroImage.

[53]  C. Grady,et al.  Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks , 2016, Neurobiology of Aging.

[54]  Joerg F. Hipp,et al.  Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG , 2016, NeuroImage.

[55]  Laura Astolfi,et al.  EEG Resting-State Brain Topological Reorganization as a Function of Age , 2016, Comput. Intell. Neurosci..

[56]  Emi Tanaka,et al.  Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity , 2016, Front. Hum. Neurosci..

[57]  Jan-Mathijs Schoffelen,et al.  A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls , 2016, Front. Syst. Neurosci..

[58]  Krzysztof J. Gorgolewski,et al.  The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance , 2015, Neuron.

[59]  Mathieu Bourguignon,et al.  A geometric correction scheme for spatial leakage effects in MEG/EEG seed‐based functional connectivity mapping , 2015, Human brain mapping.

[60]  Benjamin A. E. Hunt,et al.  Measuring electrophysiological connectivity by power envelope correlation: a technical review on MEG methods , 2015, Physics in medicine and biology.

[61]  P. Fries Rhythms for Cognition: Communication through Coherence , 2015, Neuron.

[62]  Dustin Scheinost,et al.  The (in)stability of functional brain network measures across thresholds , 2015, NeuroImage.

[63]  Derek K. Jones,et al.  Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data , 2015, NeuroImage.

[64]  P. Fries,et al.  Distributed Attention Is Implemented through Theta-Rhythmic Gamma Modulation , 2015, Current Biology.

[65]  Mark W. Woolrich,et al.  A symmetric multivariate leakage correction for MEG connectomes , 2015, NeuroImage.

[66]  M. Raichle The brain's default mode network. , 2015, Annual review of neuroscience.

[67]  N. Maurits,et al.  A Brain-Wide Study of Age-Related Changes in Functional Connectivity. , 2015, Cerebral cortex.

[68]  Tülay Adalı,et al.  Capturing subject variability in fMRI data: A graph-theoretical analysis of GICA vs. IVA , 2015, Journal of Neuroscience Methods.

[69]  Fernando Maestú,et al.  What graph theory actually tells us about resting state interictal MEG epileptic activity , 2015, NeuroImage: Clinical.

[70]  Seung-Hyun Jin,et al.  Mesial temporal lobe epilepsy with hippocampal sclerosis is a network disorder with altered cortical hubs , 2015, Epilepsia.

[71]  James B. Rowe,et al.  The effect of ageing on fMRI: Correction for the confounding effects of vascular reactivity evaluated by joint fMRI and MEG in 335 adults , 2015, Human brain mapping.

[72]  Hao He,et al.  Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia , 2015, NeuroImage.

[73]  David Phillips,et al.  Graph theoretic analysis of structural connectivity across the spectrum of Alzheimer's disease: The importance of graph creation methods , 2015, NeuroImage: Clinical.

[74]  Edwin van Dellen,et al.  The minimum spanning tree: An unbiased method for brain network analysis , 2015, NeuroImage.

[75]  Arjan Hillebrand,et al.  Functional brain networks: Linking thalamic atrophy to clinical disability in multiple sclerosis, a multimodal fMRI and MEG Study , 2015, Human brain mapping.

[76]  M. Kringelbach,et al.  Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders , 2014, Neuron.

[77]  Denise C. Park,et al.  Decreased segregation of brain systems across the healthy adult lifespan , 2014, Proceedings of the National Academy of Sciences.

[78]  Habib Benali,et al.  Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements , 2014, NeuroImage.

[79]  Margot J. Taylor,et al.  Oscillations, networks, and their development: MEG connectivity changes with age , 2014, Human brain mapping.

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

[81]  Selma Supek,et al.  Magnetoencephalography: From Signals to Dynamic Cortical Networks , 2014 .

[82]  O. Sporns,et al.  From Connections to Function: The Mouse Brain Connectome Atlas , 2014, Cell.

[83]  Nick S. Ward,et al.  Beta oscillations reflect changes in motor cortex inhibition in healthy ageing , 2014, NeuroImage.

[84]  Mark W. Woolrich,et al.  Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity , 2014, NeuroImage.

[85]  Chun Kee Chung,et al.  Preserved high-centrality hubs but efficient network reorganization during eyes-open state compared with eyes-closed resting state: an MEG study. , 2014, Journal of neurophysiology.

[86]  S. Billings,et al.  A nonlinear causality measure in the frequency domain: Nonlinear partial directed coherence with applications to EEG , 2014, Journal of Neuroscience Methods.

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

[88]  Xiaoyun Liang,et al.  Graph analysis of resting-state ASL perfusion MRI data: Nonlinear correlations among CBF and network metrics , 2014, NeuroImage.

[89]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[90]  J. Matias Palva,et al.  Phase transfer entropy: A novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions , 2014, NeuroImage.

[91]  Oliver Grimm,et al.  Test–retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state , 2014, NeuroImage.

[92]  I. H. Saleh fMRI resting state time series causality: comparison of Granger causality and phase slope index - , 2014 .

[93]  A. Engel,et al.  Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity , 2013, Neuron.

[94]  R. Kakigi,et al.  Task-Related Changes in Functional Properties of the Human Brain Network Underlying Attentional Control , 2013, PloS one.

[95]  Karl J. Friston,et al.  Structural and Functional Brain Networks: From Connections to Cognition , 2013, Science.

[96]  Mark W. Woolrich,et al.  Adding dynamics to the Human Connectome Project with MEG , 2013, NeuroImage.

[97]  Abraham Z. Snyder,et al.  Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure , 2013, NeuroImage.

[98]  Chun Kee Chung,et al.  Functional Cortical Hubs in the Eyes-Closed Resting Human Brain from an Electrophysiological Perspective Using Magnetoencephalography , 2013, PloS one.

[99]  G. Busatto,et al.  Resting-state functional connectivity in normal brain aging , 2013, Neuroscience & Biobehavioral Reviews.

[100]  A. Hillebrand,et al.  A three dimensional anatomical view of oscillatory resting-state activity and functional connectivity in Parkinson's disease related dementia: An MEG study using atlas-based beamforming☆ , 2012, NeuroImage: Clinical.

[101]  A. Hillebrand,et al.  Connectivity in MEG resting-state networks increases after resective surgery for low-grade glioma and correlates with improved cognitive performance☆ , 2012, NeuroImage: Clinical.

[102]  Mark W. Woolrich,et al.  Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage , 2012, NeuroImage.

[103]  David C. Van Essen,et al.  Cortical cartography and Caret software , 2012, NeuroImage.

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

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

[106]  Fumiko Hoeft,et al.  GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks , 2012, PloS one.

[107]  M. Corbetta,et al.  A Cortical Core for Dynamic Integration of Functional Networks in the Resting Human Brain , 2012, Neuron.

[108]  M. Corbetta,et al.  Large-scale cortical correlation structure of spontaneous oscillatory activity , 2012, Nature Neuroscience.

[109]  Edward T. Bullmore,et al.  On the use of correlation as a measure of network connectivity , 2012, NeuroImage.

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

[111]  Gareth R. Barnes,et al.  Frequency-dependent functional connectivity within resting-state networks: An atlas-based MEG beamformer solution , 2012, NeuroImage.

[112]  Alan C. Evans,et al.  Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex , 2012, NeuroImage.

[113]  A. Engel,et al.  Spectral fingerprints of large-scale neuronal interactions , 2012, Nature Reviews Neuroscience.

[114]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

[115]  Mark Hallett,et al.  Abnormal Reorganization of Functional Cortical Small-World Networks in Focal Hand Dystonia , 2011, PloS one.

[116]  June Sic Kim,et al.  How reliable are the functional connectivity networks of MEG in resting states? , 2011, Journal of neurophysiology.

[117]  Darren Price,et al.  Investigating the electrophysiological basis of resting state networks using magnetoencephalography , 2011, Proceedings of the National Academy of Sciences.

[118]  Manfred G Kitzbichler,et al.  Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks , 2011, The Journal of Neuroscience.

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

[120]  Robert Oostenveld,et al.  An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias , 2011, NeuroImage.

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

[122]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[123]  Yuan Zhou,et al.  Abnormal Cortical Networks in Mild Cognitive Impairment and Alzheimer's Disease , 2010, PLoS Comput. Biol..

[124]  Olaf Sporns,et al.  Can structure predict function in the human brain? , 2010, NeuroImage.

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

[126]  Edward T. Bullmore,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[127]  Andreas Ziehe,et al.  Comparison of Granger Causality and Phase Slope Index , 2008, NIPS Causality: Objectives and Assessment.

[128]  Edward T. Bullmore,et al.  Reproducibility of graph metrics of human brain functional networks , 2009, NeuroImage.

[129]  Danielle S Bassett,et al.  Cognitive fitness of cost-efficient brain functional networks , 2009, Proceedings of the National Academy of Sciences.

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

[131]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[132]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[133]  G. Sandini,et al.  Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. , 2009, Brain : a journal of neurology.

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

[135]  Alan C. Evans,et al.  Structural Insights into Aberrant Topological Patterns of Large-Scale Cortical Networks in Alzheimer's Disease , 2008, The Journal of Neuroscience.

[136]  C. Grady Cognitive Neuroscience of Aging , 2008, Annals of the New York Academy of Sciences.

[137]  K. Müller,et al.  Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.

[138]  Justin L. Vincent,et al.  Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.

[139]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[140]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[141]  M. Corbetta,et al.  Electrophysiological signatures of resting state networks in the human brain , 2007, Proceedings of the National Academy of Sciences.

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

[143]  W. Singer,et al.  Modulation of Neuronal Interactions Through Neuronal Synchronization , 2007, Science.

[144]  C. Stam,et al.  Small-world networks and functional connectivity in Alzheimer's disease. , 2006, Cerebral cortex.

[145]  E. Bullmore,et al.  Adaptive reconfiguration of fractal small-world human brain functional networks , 2006, Proceedings of the National Academy of Sciences.

[146]  C. Jack,et al.  Alzheimer's Disease Neuroimaging Initiative , 2008 .

[147]  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.

[148]  P. Hagmann From diffusion MRI to brain connectomics , 2005 .

[149]  M. Hallett,et al.  Identifying true brain interaction from EEG data using the imaginary part of coherency , 2004, Clinical Neurophysiology.

[150]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[151]  C. J. Stam,et al.  Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? , 2004, Neuroscience Letters.

[152]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[153]  G. Nolte The magnetic lead field theorem in the quasi-static approximation and its use for magnetoencephalography forward calculation in realistic volume conductors. , 2003, Physics in medicine and biology.

[154]  K. Sneppen,et al.  Specificity and Stability in Topology of Protein Networks , 2002, Science.

[155]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[156]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[157]  G. Shulman,et al.  Medial prefrontal cortex and self-referential mental activity: Relation to a default mode of brain function , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[158]  A. Schnitzler,et al.  Dynamic imaging of coherent sources: Studying neural interactions in the human brain. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[159]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[160]  Ryusuke Kakigi,et al.  The somatosensory evoked magnetic fields , 2000, Progress in Neurobiology.

[161]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[162]  P. Mitra,et al.  Analysis of dynamic brain imaging data. , 1998, Biophysical journal.

[163]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[164]  W. Drongelen,et al.  Localization of brain electrical activity via linearly constrained minimum variance spatial filtering , 1997, IEEE Transactions on Biomedical Engineering.

[165]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

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

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

[168]  P. Lewis,et al.  MAGNETOENCEPHALOGRAPHY , 1990, The Lancet.

[169]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .