Aberrant resting-state functional brain networks in dyslexia: Symbolic mutual information analysis of neuromagnetic signals

Neuroimaging studies have identified a variety of structural and functional connectivity abnormalities in students experiencing reading difficulties. The present study adopted a novel approach to assess the dynamics of resting-state neuromagnetic recordings in the form of symbolic sequences (i.e., repeated patterns of neuromagnetic fluctuations within and/or between sensors). Participants were 25 students experiencing severe reading difficulties (RD) and 27 age-matched non-impaired readers (NI) aged 7-14 years. Sensor-level data were first represented as symbolic sequences in eight conventional frequency bands. Next, dominant types of sensor-to-sensor interactions in the form of intra and cross-frequency coupling were computed and subjected to graph modeling to assess group differences in global network characteristics. As a group RD students displayed predominantly within-frequency interactions between neighboring sensors which may reflect reduced overall global network efficiency and cost-efficiency of information transfer. In contrast, sensor networks among NI students featured a higher proportion of cross-frequency interactions. Brain-reading achievement associations highlighted the role of left hemisphere temporo-parietal functional networks, at rest, for reading acquisition and ability.

[1]  张静,et al.  Banana Ovate family protein MaOFP1 and MADS-box protein MuMADS1 antagonistically regulated banana fruit ripening , 2015 .

[2]  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.

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

[4]  Svante Janson,et al.  On the average sequence complexity , 2004, Data Compression Conference, 2004. Proceedings. DCC 2004.

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

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

[7]  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.

[8]  Guang-Bin Huang,et al.  What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle , 2015, Cognitive Computation.

[9]  S. Petersen,et al.  The VWFA: it's not just for words anymore , 2014, Front. Hum. Neurosci..

[10]  J. Zeman,et al.  quantitative evaluation of by , 2010 .

[11]  Michael Vourkas,et al.  Dynamic task-specific brain network connectivity in children with severe reading difficulties , 2011, Neuroscience Letters.

[12]  Maki S. Koyama,et al.  Cortical Signatures of Dyslexia and Remediation: An Intrinsic Functional Connectivity Approach , 2013, PloS one.

[13]  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.

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

[15]  Silvia Brem,et al.  The left occipitotemporal system in reading: Disruption of focal fMRI connectivity to left inferior frontal and inferior parietal language areas in children with dyslexia , 2011, NeuroImage.

[16]  Nitish V. Thakor,et al.  Causal Interactions between Frontalθ – Parieto-Occipitalα2 Predict Performance on a Mental Arithmetic Task , 2016, Front. Hum. Neurosci..

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

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

[19]  Dustin Scheinost,et al.  Disruption of Functional Networks in Dyslexia: A Whole-Brain, Data-Driven Analysis of Connectivity , 2014, Biological Psychiatry.

[20]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[21]  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.

[22]  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.

[23]  Maria L. Rizzo,et al.  Measuring and testing dependence by correlation of distances , 2007, 0803.4101.

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

[25]  Stephen E Robinson,et al.  Mutual Information in a MEG Complexity Measure Suggests Regional Hyper-Connectivity in Schizophrenic Probands , 2015, Neuropsychopharmacology.

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

[27]  Deng Cai,et al.  Laplacian Score for Feature Selection , 2005, NIPS.

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

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

[30]  J. Wouters,et al.  Neuroscience and Biobehavioral Reviews a Qualitative and Quantitative Review of Diffusion Tensor Imaging Studies in Reading and Dyslexia , 2022 .

[31]  D O Walter,et al.  Regional differences in brain electrical activity in dementia: use of spectral power and spectral ratio measures. , 1993, Electroencephalography and clinical neurophysiology.

[32]  Michael Vourkas,et al.  A novel symbolization scheme for multichannel recordings with emphasis on phase information and its application to differentiate EEG activity from different mental tasks , 2011, Cognitive Neurodynamics.

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

[34]  John O. Willis,et al.  Wechsler Abbreviated Scale of Intelligence , 2014 .

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

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

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

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

[39]  David E. J. Linden,et al.  Classifying children with reading difficulties from non-impaired readers via symbolic dynamics and complexity analysis of MEG resting-state data , 2016, 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

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

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

[42]  Chong Zhang,et al.  Causal interactions between the cerebral cortex and the autonomic nervous system , 2014, Science China Life Sciences.

[43]  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.

[44]  Richard E. Frye,et al.  Laterality of Temporoparietal Causal Connectivity during the Prestimulus Period Correlates with Phonological Decoding Task Performance in Dyslexic and Typical Readers , 2011, Cerebral cortex.

[45]  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.

[46]  Jin Li,et al.  Resting-state functional connectivity and reading abilities in first and second languages , 2014, NeuroImage.

[47]  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.

[48]  Ioannis Tarnanas,et al.  Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs) , 2017, Front. Neuroinform..

[49]  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.

[50]  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.

[51]  Martin Kronbichler,et al.  Resting-State and Task-Based Functional Brain Connectivity in Developmental Dyslexia , 2014, Cerebral cortex.

[52]  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.

[53]  Maurizio Corbetta,et al.  Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations , 2013, The Journal of Neuroscience.

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

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

[56]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[57]  F. Varela,et al.  Perception's shadow: long-distance synchronization of human brain activity , 1999, Nature.

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

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

[60]  Panagiotis G. Simos,et al.  Greater Repertoire and Temporal Variability of Cross-Frequency Coupling (CFC) Modes in Resting-State Neuromagnetic Recordings among Children with Reading Difficulties , 2016, Front. Hum. Neurosci..

[61]  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.

[62]  G. Buzsáki Rhythms of the brain , 2006 .

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

[64]  S. Dehaene,et al.  Information Sharing in the Brain Indexes Consciousness in Noncommunicative Patients , 2013, Current Biology.

[65]  G. F. González,et al.  Graph analysis of EEG resting state functional networks in dyslexic readers , 2016, Clinical Neurophysiology.