Measurement of dynamic task related functional networks using MEG

Abstract The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio‐temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking. HighlightsA method is developed to track dynamic electrophysiological networks using MEG.Method based on ICA applied to timecourses measuring evolution of connectivity.Method allows a unique picture of transient networks that support cognition.Method validated in MEG data recorded during a Sternberg working memory task.Sensory networks observed include visual and sensorimotor.Cognitive networks relate to semantic processing, pattern recognition and language.

[1]  Bernhard A. Sabel,et al.  Dynamic reorganization of brain functional networks during cognition , 2015, NeuroImage.

[2]  Richard S. J. Frackowiak,et al.  The anatomy of phonological and semantic processing in normal subjects. , 1992, Brain : a journal of neurology.

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

[4]  Jonathan D. Cohen,et al.  Working Memory for Letters, Shapes, and Locations: fMRI Evidence against Stimulus-Based Regional Organization in Human Prefrontal Cortex , 2000, NeuroImage.

[5]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[6]  Jan Derrfuss,et al.  Cognitive control in the posterior frontolateral cortex: evidence from common activations in task coordination, interference control, and working memory , 2004, NeuroImage.

[7]  M. Berger,et al.  Mapping functional connectivity in patients with brain lesions , 2008, Annals of neurology.

[8]  M. Woolrich,et al.  Mechanisms underlying cortical activity during value-guided choice , 2011, Nature Neuroscience.

[9]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.

[10]  M. Corbetta,et al.  Temporal dynamics of spontaneous MEG activity in brain networks , 2010, Proceedings of the National Academy of Sciences.

[11]  R M Leahy,et al.  A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG. , 1999, Physics in medicine and biology.

[12]  Yoshio Takane,et al.  Constrained principal component analysis reveals functionally connected load‐dependent networks involved in multiple stages of working memory , 2011, Human brain mapping.

[13]  Bernard Mazoyer,et al.  Meta-analyzing left hemisphere language areas: Phonology, semantics, and sentence processing , 2006, NeuroImage.

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

[15]  D. Cohen.  Magnetoencephalography: Evidence of Magnetic Fields Produced by Alpha-Rhythm Currents , 1968, Science.

[16]  Mathieu Bourguignon,et al.  About the electrophysiological basis of resting state networks , 2014, Clinical Neurophysiology.

[17]  C. Berrou,et al.  Dynamic reorganization of functional brain networks during picture naming , 2015, Cortex.

[18]  Joseph A. Maldjian,et al.  Graph theoretical analysis of resting-state MEG data: Identifying interhemispheric connectivity and the default mode , 2014, NeuroImage.

[19]  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.

[20]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[21]  Matthew J. Brookes,et al.  A multi-layer network approach to MEG connectivity analysis , 2016, NeuroImage.

[22]  A. Schnitzler,et al.  Normal and pathological oscillatory communication in the brain , 2005, Nature Reviews Neuroscience.

[23]  P. Peigneux,et al.  Inter- and Intra-Subject Variability of Neuromagnetic Resting State Networks , 2014, Brain Topography.

[24]  Xiao Liu,et al.  EEG correlates of time-varying BOLD functional connectivity , 2013, NeuroImage.

[25]  Matthew J. Brookes,et al.  Measuring functional connectivity using MEG: Methodology and comparison with fcMRI , 2011, NeuroImage.

[26]  Mark W. Woolrich,et al.  How reliable are MEG resting-state connectivity metrics? , 2016, NeuroImage.

[27]  M Corbetta,et al.  Frontoparietal cortical networks for directing attention and the eye to visual locations: identical, independent, or overlapping neural systems? , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[29]  M. Corbetta,et al.  Selective and divided attention during visual discriminations of shape, color, and speed: functional anatomy by positron emission tomography , 1991, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[30]  D. Perani,et al.  The Neural Correlates of Spatial and Object Working Memory in Elderly and Parkinson's Disease Subjects , 2015, Behavioural neurology.

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

[32]  J. Gore,et al.  An Event-Related fMRI Study of Implicit Phrase-Level Syntactic and Semantic Processing , 1999, NeuroImage.

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

[34]  D. Cohen.  Magnetoencephalography: Detection of the Brain's Electrical Activity with a Superconducting Magnetometer , 1972, Science.

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

[36]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[37]  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.

[38]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[39]  C. Stam,et al.  Disruption of structural and functional networks in long‐standing multiple sclerosis , 2014, Human brain mapping.

[40]  Karl J. Friston.  The disconnection hypothesis , 1998, Schizophrenia Research.

[41]  Darren Price,et al.  Complexity Measures in Magnetoencephalography: Measuring "Disorder" in Schizophrenia , 2015, PloS one.

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

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

[44]  P. F. Liddle,et al.  Task induced modulation of neural oscillations in electrophysiological brain networks , 2012, NeuroImage.

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

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

[47]  Russell A. Poldrack,et al.  Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives , 2015, NeuroImage.

[48]  N. Kanwisher,et al.  The lateral occipital complex and its role in object recognition , 2001, Vision Research.

[49]  Mark W. Woolrich,et al.  Spectrally resolved fast transient brain states in electrophysiological data , 2016, NeuroImage.

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

[51]  Mark W. Woolrich,et al.  Dynamic recruitment of resting state sub-networks , 2015, NeuroImage.

[52]  Alan C. Evans,et al.  Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. , 2009, Cerebral cortex.

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

[54]  Joerg F. Hipp,et al.  BOLD fMRI Correlation Reflects Frequency-Specific Neuronal Correlation , 2015, Current Biology.

[55]  David A. Leopold,et al.  The contribution of electrophysiology to functional connectivity mapping , 2013, NeuroImage.

[56]  Z Kourtzi,et al.  Representation of Perceived Object Shape by the Human Lateral Occipital Complex , 2001, Science.

[57]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[58]  David Poeppel,et al.  Asymptotic SNR of scalar and vector minimum-variance beamformers for neuromagnetic source reconstruction , 2004, IEEE Transactions on Biomedical Engineering.

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

[60]  Richard Bowtell,et al.  Detecting activations in event‐related fMRI using analysis of variance , 1999, Magnetic resonance in medicine.

[61]  Mark W. Woolrich,et al.  Inferring task-related networks using independent component analysis in magnetoencephalography , 2012, NeuroImage.

[62]  N. Geschwind.  Disconnexion syndromes in animals and man. I. , 1965, Brain : a journal of neurology.

[63]  Matthew J. Brookes,et al.  Optimising experimental design for MEG beamformer imaging , 2008, NeuroImage.

[64]  Se Robinson,et al.  Functional neuroimaging by Synthetic Aperture Magnetometry (SAM) , 1999 .

[65]  C. Sripada,et al.  Modality-Spanning Deficits in Attention-Deficit/Hyperactivity Disorder in Functional Networks, Gray Matter, and White Matter , 2014, The Journal of Neuroscience.

[66]  Vince D. Calhoun,et al.  Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information , 2015, NeuroImage.

[67]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[68]  Piet Van Mieghem,et al.  Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions , 2016, NeuroImage.

[69]  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.

[70]  Leslie G. Ungerleider,et al.  The functional organization of human extrastriate cortex: a PET-rCBF study of selective attention to faces and locations , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

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

[73]  J. Grafman,et al.  Dorsolateral prefrontal contributions to human working memory , 2013, Cortex.

[74]  P. Liddle,et al.  Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. , 2012, Journal of psychiatry & neuroscience : JPN.

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

[76]  Dimitri Van De Ville,et al.  Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks , 2015, Nature Communications.

[77]  Kensuke Sekihara,et al.  A simple nonparametric statistical thresholding for MEG spatial-filter source reconstruction images , 2005, NeuroImage.

[78]  Yoshio Takane,et al.  Epoch-specific functional networks involved in working memory , 2013, NeuroImage.

[79]  Jemett L. Desmond,et al.  Semantic encoding and retrieval in the left inferior prefrontal cortex: a functional MRI study of task difficulty and process specificity , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[80]  Jeff H. Duyn,et al.  Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography , 2010, NeuroImage.

[81]  Stephen M. Smith,et al.  Permutation inference for the general linear model , 2014, NeuroImage.

[82]  J. Sarvas.  Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[83]  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.

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

[85]  Richard S. J. Frackowiak,et al.  Functional anatomy of a common semantic system for words and pictures , 1996, Nature.

[86]  Stephen M. Rao,et al.  Human Brain Language Areas Identified by Functional Magnetic Resonance Imaging , 1997, The Journal of Neuroscience.

[87]  I. Toni,et al.  Distinct roles for alpha- and beta-band oscillations during mental simulation of goal-directed actions. , 2014, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[88]  Mitsuo Kawato,et al.  Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns , 2015, Scientific Reports.

[89]  Jorge Sepulcre,et al.  Localization of focal epileptic discharges using functional connectivity magnetic resonance imaging. , 2011, Journal of neurosurgery.

[90]  R. Engle,et al.  The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective , 2002, Psychonomic bulletin & review.

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