Greater Repertoire and Temporal Variability of Cross-Frequency Coupling (CFC) Modes in Resting-State Neuromagnetic Recordings among Children with Reading Difficulties

Cross-frequency, phase-to-amplitude coupling (PAC) between neuronal oscillations at rest may serve as the substrate that supports information exchange between functionally specialized neuronal populations both within and between cortical regions. The study utilizes novel algorithms to identify prominent instantaneous modes of cross-frequency coupling and their temporal stability in resting state magnetoencephalography (MEG) data from 25 students experiencing severe reading difficulties (RD) and 27 age-matched non-impaired readers (NI). Phase coherence estimates were computed in order to identify the prominent mode of PAC interaction for each sensor, sensor pair, and pair of frequency bands (from δ to γ) at successive time windows of the continuous MEG record. The degree of variability in the characteristic frequency-pair PACf1−f2 modes over time was also estimated. Results revealed a wider repertoire of prominent PAC interactions in RD as compared to NI students, suggesting an altered functional substrate for information exchange between neuronal assemblies in the former group. Moreover, RD students showed significant variability in PAC modes over time. This temporal instability of PAC values was particularly prominent: (a) within and between right hemisphere temporo-parietal and occipito-temporal sensors and, (b) between left hemisphere frontal, temporal, and occipito-temporal sensors and corresponding right hemisphere sites. Altered modes of neuronal population coupling may help account for extant data revealing reduced, task-related neurophysiological and hemodynamic activation in left hemisphere regions involved in the reading network in RD. Moreover, the spatial distribution of pronounced instability of cross-frequency coupling modes in this group may provide an explanation for previous reports suggesting the presence of inefficient compensatory mechanisms to support reading.

[1]  T. Hafting,et al.  Frequency of gamma oscillations routes flow of information in the hippocampus , 2009, Nature.

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

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

[4]  G. Deco,et al.  Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors , 2012, The Journal of Neuroscience.

[5]  Woodrow L. Shew,et al.  Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality , 2009, The Journal of Neuroscience.

[6]  A. Belger,et al.  Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia , 2014, NeuroImage: Clinical.

[7]  S. Micheloyannis,et al.  Altered cross-frequency coupling in resting-state MEG after mild traumatic brain injury. , 2016, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[8]  J. Kelso,et al.  The Metastable Brain , 2014, Neuron.

[9]  Adriano B. L. Tort,et al.  Sharp edge artifacts and spurious coupling in EEG frequency comodulation measures , 2008, Journal of Neuroscience Methods.

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

[11]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[12]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[13]  Bryon A. Mueller,et al.  Altered resting state complexity in schizophrenia , 2012, NeuroImage.

[14]  E. Bullmore,et al.  Human brain networks in health and disease , 2009, Current opinion in neurology.

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

[16]  Bruce D. McCandliss,et al.  Neural systems predicting long-term outcome in dyslexia , 2010, Proceedings of the National Academy of Sciences.

[17]  Esther Florin,et al.  The brain's resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations , 2015, NeuroImage.

[18]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Bruce D. McCandliss,et al.  Left lateralized white matter microstructure accounts for individual differences in reading ability and disability , 2006, Neuropsychologia.

[20]  A. Pérez-Villalba.  Rhythms of the Brain, G. Buzsáki. Oxford University Press, Madison Avenue, New York (2006), Price: GB £42.00, p. 448, ISBN: 0-19-530106-4 , 2008 .

[21]  Xi-Nian Zuo,et al.  Resting-State Functional Connectivity Indexes Reading Competence in Children and Adults , 2011, The Journal of Neuroscience.

[22]  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.

[23]  W. Nagy,et al.  Contrasting brain patterns of writing-related DTI parameters, fMRI connectivity, and DTI–fMRI connectivity correlations in children with and without dysgraphia or dyslexia , 2015, NeuroImage: Clinical.

[24]  James L. McClelland,et al.  Enlarging the scope: grasping brain complexity , 2013, Front. Syst. Neurosci..

[25]  Michael X. Cohen,et al.  Intracranial EEG Correlates of Expectancy and Memory Formation in the Human Hippocampus and Nucleus Accumbens , 2010, Neuron.

[26]  J. Fell,et al.  Cross-frequency coupling supports multi-item working memory in the human hippocampus , 2010, Proceedings of the National Academy of Sciences.

[27]  O. Sporns.  Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.

[28]  S. Petersen,et al.  The maturing architecture of the brain's default network , 2008, Proceedings of the National Academy of Sciences.

[29]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[30]  N. Laskaris,et al.  Characterizing Dynamic Functional Connectivity Across Sleep Stages from EEG , 2009, Brain Topography.

[31]  Ioannis Tarnanas,et al.  A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses , 2015, Front. Neurosci..

[32]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[33]  Andreas A. Ioannides,et al.  Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks , 2012, Comput. Math. Methods Medicine.

[34]  O. Jensen,et al.  Gamma Power Is Phase-Locked to Posterior Alpha Activity , 2008, PloS one.

[35]  Brendon O. Watson,et al.  Brain rhythms and neural syntax: implications for efficient coding of cognitive content and neuropsychiatric disease. , 2012, Dialogues in clinical neuroscience.

[36]  G. E. Smith.  The Human Brain , 1924, Nature.

[37]  Nitish Thakor,et al.  Cognitive Workload Assessment Based on the Tensorial Treatment of EEG Estimates of Cross-Frequency Phase Interactions , 2014, Annals of Biomedical Engineering.

[38]  Chenwang Jin,et al.  A preliminary study of the dysregulation of the resting networks in first-episode medication-naive adolescent depression , 2011, Neuroscience Letters.

[39]  J. Martinerie,et al.  The brainweb: Phase synchronization and large-scale integration , 2001, Nature Reviews Neuroscience.

[40]  N. A. Laskaris,et al.  Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes , 2015, Cognitive Neurodynamics.

[41]  Michael Vourkas,et al.  Surface EEG shows that functional segregation via phase coupling contributes to the neural substrate of mental calculations , 2012, Brain and Cognition.

[42]  L. Pinneo.  On noise in the nervous system. , 1966, Psychological review.

[43]  Murray Shanahan,et al.  Embodiment and the inner lifeCognition and Consciousness in the Space of Possible Minds , 2010 .

[44]  Manolis Tsiknakis,et al.  Synchronization coupling investigation using ICA cluster analysis in resting MEG signals in reading difficulties , 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.

[45]  Rami K. Niazy,et al.  Resting-State Networks , 2015 .

[46]  C. Rosazza,et al.  Resting-state brain networks: literature review and clinical applications , 2011, Neurological Sciences.

[47]  O. Sporns,et al.  Organization, development and function of complex brain networks , 2004, Trends in Cognitive Sciences.

[48]  George Economou,et al.  Analyzing Functional Brain Connectivity by Means of Commute Times: A New Approach and its Application to Track Event-Related Dynamics , 2012, IEEE Transactions on Biomedical Engineering.

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

[50]  S. Micheloyannis,et al.  What does delta band tell us about cognitive processes: A mental calculation study , 2010, Neuroscience Letters.

[51]  T. Achenbach.  Manual for the child behavior checklist/4-18 and 1991 profile , 1991 .

[52]  Gustavo Deco,et al.  Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.

[53]  Joseph Biederman,et al.  Diagnostic accuracy of the Child Behavior Checklist scales for attention-deficit hyperactivity disorder: a receiver-operating characteristic analysis. , 1994, Journal of consulting and clinical psychology.

[54]  N. A. Laskaris,et al.  On the Quantization of Time-Varying Phase Synchrony Patterns into Distinct Functional Connectivity Microstates (FCμstates) in a Multi-trial Visual ERP Paradigm , 2013, Brain Topography.

[55]  M. Corballis,et al.  Mixed Lateralization of Phonological Assembly in Developmental Dyslexia , 2002, Neurocase.

[56]  N. Logothetis,et al.  Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms , 2013, Neuron.

[57]  D. McCormick,et al.  Neocortical Network Activity In Vivo Is Generated through a Dynamic Balance of Excitation and Inhibition , 2006, The Journal of Neuroscience.

[58]  J. Gabrieli,et al.  Functional and morphometric brain dissociation between dyslexia and reading ability , 2007, Proceedings of the National Academy of Sciences.

[59]  M. Kramer,et al.  Beyond the Connectome: The Dynome , 2014, Neuron.

[60]  Nikolai Axmacher,et al.  Good Vibrations: Cross-frequency Coupling in the Human Nucleus Accumbens during Reward Processing , 2009, Journal of Cognitive Neuroscience.

[61]  Abbas Babajani-Feremi,et al.  Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory , 2015, Clinical Neurophysiology.

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

[63]  Roberto Hornero,et al.  Quantitative Evaluation of Artifact Removal in Real Magnetoencephalogram Signals with Blind Source Separation , 2011, Annals of Biomedical Engineering.

[64]  T. Q. Irigaray,et al.  Intellectual abilities in Alzheimer's disease patients: Contributions from the Wechsler Abbreviated Scale of Intelligence (WASI) , 2010 .

[65]  N. Laskaris,et al.  Spontaneous Up states in vitro: a single-metric index of the functional maturation and regional differentiation of the cerebral cortex , 2015, Front. Neural Circuits.

[66]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[67]  Carolyn A. Denton,et al.  The relative effects of group size on reading progress of older students with reading difficulties , 2010, Reading and writing.

[68]  Gustavo Deco,et al.  Resting brains never rest: computational insights into potential cognitive architectures , 2013, Trends in Neurosciences.

[69]  R. Knight,et al.  The functional role of cross-frequency coupling , 2010, Trends in Cognitive Sciences.

[70]  Peter J Hellyer,et al.  The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs , 2014, Front. Hum. Neurosci..

[71]  M. Corbetta,et al.  The Dynamical Balance of the Brain at Rest , 2011, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[72]  Steven Laureys,et al.  Resting State Networks and Consciousness , 2012, Front. Psychology.

[73]  S E Shaywitz,et al.  Neurobiological studies of reading and reading disability. , 2001, Journal of communication disorders.

[74]  Panagiotis G. Simos,et al.  Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG , 2013, NeuroImage.

[75]  R. Knight,et al.  Shifts in Gamma Phase–Amplitude Coupling Frequency from Theta to Alpha Over Posterior Cortex During Visual Tasks , 2010, Front. Hum. Neurosci..

[76]  A. von Stein,et al.  Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[77]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[78]  James F. Glazebrook.  Embodiment and the inner life: Cognition and consciousness in the space of possible minds, M. Shanahan. Oxford University Press (2010) , 2014, Cogn. Syst. Res..

[79]  M. Just,et al.  Modifying the brain activation of poor readers during sentence comprehension with extended remedial instruction: A longitudinal study of neuroplasticity , 2008, Neuropsychologia.

[80]  D. Contreras,et al.  Synchronization of fast (30-40 Hz) spontaneous oscillations in intrathalamic and thalamocortical networks , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[82]  Jenifer Juranek,et al.  Functional disruption of the brain mechanism for reading: effects of comorbidity and task difficulty among children with developmental learning problems. , 2011, Neuropsychology.

[83]  Sharon Vaughn,et al.  Temporo-parietal Brain Activity as a Longitudinal Predictor of Response to Educational Interventions among Middle School Struggling Readers , 2011, Journal of the International Neuropsychological Society.

[84]  Pablo Balenzuela,et al.  Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis , 2012, Front. Physio..

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

[86]  O. Jensen,et al.  Cross-frequency coupling between neuronal oscillations , 2007, Trends in Cognitive Sciences.

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

[88]  Jon H. Kaas,et al.  The emergence and evolution of mammalian neocortex , 1995, Trends in Neurosciences.

[89]  Michael Vourkas,et al.  Tracking brain dynamics via time-dependent network analysis , 2010, Journal of Neuroscience Methods.

[90]  F. Gómez,et al.  Alterations of multiple resting state network connectivity in physiological, pharmacological, and pathological consciousness states , 2012 .

[91]  George Zouridakis,et al.  Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury , 2015, NeuroImage: Clinical.

[92]  A. Schnitzler,et al.  A critical note on the definition of phase–amplitude cross-frequency coupling , 2011, Journal of Neuroscience Methods.

[93]  Shane McKie,et al.  Resting state networks in major depressive disorder , 2014, Psychiatry Research: Neuroimaging.

[94]  György Buzsáki,et al.  Neural Syntax: Cell Assemblies, Synapsembles, and Readers , 2010, Neuron.

[95]  Jack M Fletcher,et al.  Altering the brain circuits for reading through intervention: a magnetic source imaging study. , 2007, Neuropsychology.

[96]  Michael P Milham,et al.  Reading networks at rest. , 2010, Cerebral cortex.

[97]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[98]  H. Eichenbaum,et al.  Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. , 2010, Journal of neurophysiology.

[99]  Nitish Thakor,et al.  Revealing Cross-Frequency Causal Interactions During a Mental Arithmetic Task Through Symbolic Transfer Entropy: A Novel Vector-Quantization Approach , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[100]  Viktor K. Jirsa,et al.  Cross-frequency coupling in real and virtual brain networks , 2013, Front. Comput. Neurosci..

[101]  M. Wibral,et al.  Untangling cross-frequency coupling in neuroscience , 2014, Current Opinion in Neurobiology.

[102]  Kimberley D. Lakes,et al.  Categorical and Dimensional Definitions and Evaluations of Symptoms of ADHD: History of the SNAP and the SWAN Rating Scales. , 2012, The International journal of educational and psychological assessment.

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

[104]  J. Friedman,et al.  Graphics for the Multivariate Two-Sample Problem , 1981 .

[105]  M. Berger,et al.  High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex , 2006, Science.

[106]  Michael X. Cohen,et al.  Oscillatory Activity and Phase–Amplitude Coupling in the Human Medial Frontal Cortex during Decision Making , 2009, Journal of Cognitive Neuroscience.

[107]  Michael Vourkas,et al.  An EEG study of brain connectivity dynamics at the resting state. , 2012, Nonlinear dynamics, psychology, and life sciences.

[108]  Satu Palva,et al.  Roles of multiscale brain activity fluctuations in shaping the variability and dynamics of psychophysical performance. , 2011, Progress in brain research.