Altered temporal stability in dynamic neural networks underlies connectivity changes in neurodevelopment

ABSTRACT Network connectivity is an integral feature of human brain function, and characterising its maturational trajectory is a critical step towards understanding healthy and atypical neurodevelopment. Here, we used magnetoencephalography (MEG) to investigate both stationary (i.e. time averaged) and rapidly modulating (dynamic) electrophysiological connectivity, in participants aged from mid‐childhood to early adulthood (youngest participant 9 years old; oldest participant 25 years old). Stationary functional connectivity (measured via inter‐regional coordination of neural oscillations) increased with age in the alpha and beta frequency bands, particularly in bilateral parietal and temporo‐parietal connections. Our dynamic analysis (also applied to alpha/beta oscillations) revealed the spatiotemporal signatures of 8 dynamic networks; these modulate on a ˜100ms time scale, and temporal stability in attentional networks was found to increase with age. Significant overlap was found between age‐modulated dynamic networks and inter‐regional oscillatory coordination, implying that altered network dynamics underlie age related changes in functional connectivity. Our results provide novel insights into brain network electrophysiology, and lay a foundation for future work in childhood disorders. HIGHLIGHTSWe studied static and dynamic connectivity change between mid‐childhood and adulthood.Static (time averaged) connectivity increases with age in alpha and beta bands.Connectivity changes with age are strongest in attentional networks.Temporal stability in attentional networks was found to increase with age.Changing network dynamics underlies changes in time averaged functional connectivity.

[1]  Matthew J. Brookes,et al.  On the Potential of a New Generation of Magnetometers for MEG: A Beamformer Simulation Study , 2016, PloS one.

[2]  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.

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

[4]  Margot J. Taylor,et al.  Reduced beta connectivity during emotional face processing in adolescents with autism , 2014, Molecular Autism.

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

[6]  Edwin van Dellen,et al.  Structural degree predicts functional network connectivity: A multimodal resting-state fMRI and MEG study , 2014, NeuroImage.

[7]  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.

[8]  K. Hwang,et al.  The Contribution of Network Organization and Integration to the Development of Cognitive Control , 2015, PLoS biology.

[9]  Matthew J. Brookes,et al.  The relationship between MEG and fMRI , 2014, NeuroImage.

[10]  Conor V. Dolan,et al.  Source (or Part of the following Source): Type Article Title Age-related Change in Executive Function: Developmental Trends and a Latent Variable Analysis Author(s) Age-related Change in Executive Function: Developmental Trends and a Latent Variable Analysis , 2022 .

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

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

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

[14]  O. Sporns,et al.  White matter maturation reshapes structural connectivity in the late developing human brain , 2010, Proceedings of the National Academy of Sciences.

[15]  E. Whitham,et al.  Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20Hz are contaminated by EMG , 2007, Clinical Neurophysiology.

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

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

[18]  R. Zimmermann,et al.  MEG and EEG show different sensitivity to myogenic artifacts. , 2004, Neurology & clinical neurophysiology : NCN.

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

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

[21]  Matthew J. Brookes,et al.  A new generation of magnetoencephalography: Room temperature measurements using optically-pumped magnetometers , 2017, NeuroImage.

[22]  Nilam Ram,et al.  Nonlinear growth curves in developmental research. , 2011, Child development.

[23]  Margot J. Taylor,et al.  Atypical resting synchrony in autism spectrum disorder , 2014, Human brain mapping.

[24]  David Poeppel,et al.  Performance of an MEG adaptive-beamformer source reconstruction technique in the presence of additive low-rank interference , 2004, IEEE Transactions on Biomedical Engineering.

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

[26]  Kaustubh Supekar,et al.  Brain hyperconnectivity in children with autism and its links to social deficits. , 2013, Cell reports.

[27]  Max A. Viergever,et al.  High frequency spectral components after Secobarbital: The contribution of muscular origin—A study with MEG/EEG , 2012, Epilepsy Research.

[28]  Hamid Reza Mohseni,et al.  Dynamic state allocation for MEG source reconstruction , 2013, NeuroImage.

[29]  Christopher W Mount,et al.  Neuronal Activity Promotes Oligodendrogenesis and Adaptive Myelination in the Mammalian Brain , 2014, Science.

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

[31]  Mark W. Woolrich,et al.  Using variance information in magnetoencephalography measures of functional connectivity , 2013, NeuroImage.

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

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

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

[35]  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.

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

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

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

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

[40]  Robert J Barry,et al.  Age and sex effects in the EEG: development of the normal child , 2001, Clinical Neurophysiology.

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

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

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

[44]  William Gaetz,et al.  Neuromagnetic imaging of movement-related cortical oscillations in children and adults: Age predicts post-movement beta rebound , 2010, NeuroImage.

[45]  Giles L. Colclough,et al.  Cognitive Training Enhances Intrinsic Brain Connectivity in Childhood , 2015, The Journal of Neuroscience.

[46]  Stephen J. Roberts,et al.  Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis , 2005 .

[47]  Damien A. Fair,et al.  Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature , 2017, NeuroImage.

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

[49]  Catie Chang,et al.  6901 Investigating the electrophysiological fingerprints of spontaneous fMRI activity , 2013 .

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

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

[52]  Chandan J. Vaidya,et al.  Atypical modulation of distant functional connectivity by cognitive state in children with Autism Spectrum Disorders , 2013, Front. Hum. Neurosci..

[53]  S. Muthukumaraswamy.  High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations , 2013, Front. Hum. Neurosci..

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

[55]  T. Gasser,et al.  Development of the EEG of school-age children and adolescents. II. Topography. , 1988, Electroencephalography and clinical neurophysiology.

[56]  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.

[57]  Matthew J. Brookes,et al.  Optimising experimental design for MEG resting state functional connectivity measurement , 2017, NeuroImage.

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

[59]  Frederik Barkhof,et al.  Resting‐state networks in awake five‐ to eight‐year old children , 2012, Human brain mapping.