Relationships Between Neuronal Oscillatory Amplitude and Dynamic Functional Connectivity.

Event-related fluctuations of neural oscillatory amplitude are reported widely in the context of cognitive processing and are typically interpreted as a marker of brain "activity". However, the precise nature of these effects remains unclear; in particular, whether such fluctuations reflect local dynamics, integration between regions, or both, is unknown. Here, using magnetoencephalography, we show that movement induced oscillatory modulation is associated with transient connectivity between sensorimotor regions. Further, in resting-state data, we demonstrate a significant association between oscillatory modulation and dynamic connectivity. A confound with such empirical measurements is that increased amplitude necessarily means increased signal-to-noise ratio (SNR): this means that the question of whether amplitude and connectivity are genuinely coupled, or whether increased connectivity is observed purely due to increased SNR is unanswered. Here, we counter this problem by analogy with computational models which show that, in the presence of global network coupling and local multistability, the link between oscillatory modulation and long-range connectivity is a natural consequence of neural networks. Our results provide evidence for the notion that connectivity is mediated by neural oscillations, and suggest that time-frequency spectrograms are not merely a description of local synchrony but also reflect fluctuations in long-range connectivity.

[1]  Matthew J. Brookes,et al.  Relationships between cortical myeloarchitecture and electrophysiological networks , 2016, Proceedings of the National Academy of Sciences.

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

[3]  G. Rizzolatti,et al.  Neurophysiological mechanisms underlying the understanding and imitation of action , 2001, Nature Reviews Neuroscience.

[4]  R. VanRullen,et al.  The Phase of Ongoing Oscillations Mediates the Causal Relation between Brain Excitation and Visual Perception , 2011, The Journal of Neuroscience.

[5]  Mathew P. Dafilis,et al.  A spatially continuous mean field theory of electrocortical activity , 2002, Network.

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

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

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

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

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

[11]  Theiler,et al.  Generating surrogate data for time series with several simultaneously measured variables. , 1994, Physical review letters.

[12]  Mark W. Woolrich,et al.  A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks , 2018, PLoS Comput. Biol..

[13]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[14]  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.

[15]  Krish D. Singh,et al.  Visual gamma oscillations: The effects of stimulus type, visual field coverage and stimulus motion on MEG and EEG recordings , 2013, NeuroImage.

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

[17]  J. Schoffelen,et al.  University of Birmingham Occipital alpha activity during stimulus processing gates the information flow to object-selective cortex , 2014 .

[18]  Sidney J. Segalowitz,et al.  Adolescent anxiety and aggression can be differentially predicted by electrocortical phase reset variables , 2014, Brain and Cognition.

[19]  Paul L. Furlong,et al.  The Role of Gabaergic Modulation in Motor Function Related Neuronal Network Activity the Role of Gabaergic Modulation in Motor Function Related Neuronal Network Activity , 2022 .

[20]  P. Robinson,et al.  Prediction and verification of nonlinear sleep spindle harmonic oscillations. , 2014, Journal of theoretical biology.

[21]  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.

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

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

[24]  F. Varela,et al.  Measuring phase synchrony in brain signals , 1999, Human brain mapping.

[25]  P. Robinson,et al.  Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  Gregor Thut,et al.  Alpha Power Increase After Transcranial Alternating Current Stimulation at Alpha Frequency (α-tACS) Reflects Plastic Changes Rather Than Entrainment , 2015, Brain Stimulation.

[27]  P. Robinson,et al.  Prediction of electroencephalographic spectra from neurophysiology. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Michael T. Jurkiewicz,et al.  Post-movement beta rebound is generated in motor cortex: Evidence from neuromagnetic recordings , 2006, NeuroImage.

[29]  P. Schyns,et al.  Rhythmic TMS Causes Local Entrainment of Natural Oscillatory Signatures , 2011, Current Biology.

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

[31]  Mark W. Woolrich,et al.  Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach , 2016, NeuroImage.

[32]  George A. Mashour,et al.  General Relationship of Global Topology, Local Dynamics, and Directionality in Large-Scale Brain Networks , 2015, PLoS Comput. Biol..

[33]  Michael Breakspear,et al.  A Novel Method for the Topographic Analysis of Neural Activity Reveals Formation and Dissolution of ‘Dynamic Cell Assemblies’ , 2004, Journal of Computational Neuroscience.

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

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

[36]  P. Derambure,et al.  Does post-movement beta synchronization reflect an idling motor cortex? , 2001, Neuroreport.

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

[38]  L. M. Ward,et al.  Synchronous neural oscillations and cognitive processes , 2003, Trends in Cognitive Sciences.

[39]  Timothy P. L. Roberts,et al.  Relating MEG measured motor cortical oscillations to resting γ-Aminobutyric acid (GABA) concentration , 2011, NeuroImage.

[40]  C. Gerloff,et al.  Interregional long-range and short-range synchrony: a basis for complex sensorimotor processing. , 2006, Progress in brain research.

[41]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[42]  P. Fries.  A mechanism for cognitive dynamics: neuronal communication through neuronal coherence , 2005, Trends in Cognitive Sciences.

[43]  Stephen Coombes,et al.  Large-scale neural dynamics: Simple and complex , 2010, NeuroImage.

[44]  R. Thatcher,et al.  Self‐organized criticality and the development of EEG phase reset , 2009, Human brain mapping.

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

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

[47]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.

[48]  James A. Roberts,et al.  Biophysical Mechanisms of Multistability in Resting-State Cortical Rhythms , 2011, The Journal of Neuroscience.

[49]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

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

[51]  M. Breakspear,et al.  Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity , 2009, The Journal of Neuroscience.

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

[53]  K. Hoffmann,et al.  Contribution of Cholinergic and GABAergic Mechanisms to Direction Tuning, Discriminability, Response Reliability, and Neuronal Rate Correlations in Macaque Middle Temporal Area , 2012, The Journal of Neuroscience.

[54]  W. Singer,et al.  Neural Synchrony in Brain Disorders: Relevance for Cognitive Dysfunctions and Pathophysiology , 2006, Neuron.

[55]  Bernadette C. M. van Wijk,et al.  On the Influence of Amplitude on the Connectivity between Phases , 2011, Front. Neuroinform..