Arithmetic success and gender-based characterization of brain connectivity across EEG bands

Abstract We provide a distinctive view for rest/task, gender and arithmetic success state for human brain concerning EEG-based functional brain networks. Utilizing coherence method to quantify phase synchronization between EEG nodes, signal activities are converted into graph representations. After a complex-theoretic approach is conducted, intelligent brains emerge as more connected ones under resting state. Male brain, featuring lower connection strength and efficiency under resting state, exhibits the ability to boost up connectivity under mental workload. On the other hand, arithmetic success correlates with high resting state connectivity for all EEG bands but dominantly for gamma band, while unsuccessful brains yield greater beta band assortativity behavior. Theta band associated with unconscious actions apparently exhibits greater connection weights for mental activity compared to resting. Contributions of EEG bands to diagnosing differences in rest/task, gender and arithmetic success states are detailed within the study. We also spot out which connection patterns are related with mental progressing, outlining that intelligent brains yield less inter-frontal and more frontal to central and frontal to parieto-occipital connections.

[1]  Arjan Hillebrand,et al.  Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and Alzheimer's disease: an EEG study , 2016, Neurobiology of Aging.

[2]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[3]  Jason B. Mattingley,et al.  Functional brain networks related to individual differences in human intelligence at rest , 2016, Scientific Reports.

[4]  Morten L. Kringelbach,et al.  Exploring the network dynamics underlying brain activity during rest , 2014, Progress in Neurobiology.

[5]  Dinggang Shen,et al.  Disrupted Brain Functional Network in Internet Addiction Disorder: A Resting-State Functional Magnetic Resonance Imaging Study , 2014, PloS one.

[6]  Olaf Sporns,et al.  Weight-conserving characterization of complex functional brain networks , 2011, NeuroImage.

[7]  O. Sporns.  Networks of the Brain , 2010 .

[8]  Helen Christian,et al.  Existence of long-lasting experience-dependent plasticity in endocrine cell networks , 2012, Nature Communications.

[9]  Jun Li,et al.  Brain spontaneous functional connectivity and intelligence , 2008, NeuroImage.

[10]  P. Lin,et al.  Small-World Brain Functional Networks in Children With Attention-Deficit/Hyperactivity Disorder Revealed by EEG Synchrony , 2015, Clinical EEG and neuroscience.

[11]  M. Chun,et al.  A neuromarker of sustained attention from whole-brain functional connectivity , 2015, Nature Neuroscience.

[12]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[13]  Gonzalo M. Rojas,et al.  Study of Resting-State Functional Connectivity Networks Using EEG Electrodes Position As Seed , 2018, Front. Neurosci..

[14]  Michael W. Cole,et al.  Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration , 2016, The Journal of Neuroscience.

[15]  Cornelis J. Stam,et al.  Functional brain network analysis using minimum spanning trees in Multiple Sclerosis: An MEG source-space study , 2014, NeuroImage.

[16]  Richard F. Betzel,et al.  Linked dimensions of psychopathology and connectivity in functional brain networks , 2017, bioRxiv.

[17]  Elzbieta Olejarczyk,et al.  Graph-based analysis of brain connectivity in schizophrenia , 2017, PloS one.

[18]  Wen-Wen Chang,et al.  Functional brain network and multichannel analysis for the P300-based brain computer interface system of lying detection , 2016, Expert Syst. Appl..

[19]  Elizabeth S Norton,et al.  Neurobiology of dyslexia , 2014, Current Opinion in Neurobiology.

[20]  J. Escudero,et al.  The complex hierarchical topology of EEG functional connectivity , 2017, Journal of Neuroscience Methods.

[21]  Yong He,et al.  Imaging Functional and Structural Brain Connectomics in Attention-Deficit/Hyperactivity Disorder , 2014, Molecular Neurobiology.

[22]  Masako Okamoto,et al.  Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping , 2004, NeuroImage.

[23]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

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

[25]  Matjaz Perc,et al.  Functional Connectivity in Islets of Langerhans from Mouse Pancreas Tissue Slices , 2013, PLoS Comput. Biol..

[26]  Yoed N. Kenett,et al.  Robust prediction of individual creative ability from brain functional connectivity , 2018, Proceedings of the National Academy of Sciences.

[27]  O. Sporns,et al.  From regions to connections and networks: new bridges between brain and behavior , 2016, Current Opinion in Neurobiology.

[28]  Tanya M. Evans,et al.  Brain Structural Integrity and Intrinsic Functional Connectivity Forecast 6 Year Longitudinal Growth in Children's Numerical Abilities , 2015, The Journal of Neuroscience.

[29]  Lorena R. R. Gianotti,et al.  Functional brain network efficiency predicts intelligence , 2012, Human brain mapping.

[30]  E. Carrette,et al.  Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization , 2014, Progress in Neurobiology.

[31]  Cornelis J. Stam,et al.  Structure out of chaos: Functional brain network analysis with EEG, MEG, and functional MRI , 2013, European Neuropsychopharmacology.

[32]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[33]  Malek Adjouadi,et al.  Scalp EEG brain functional connectivity networks in pediatric epilepsy , 2015, Comput. Biol. Medicine.

[34]  Armin Scheurich,et al.  Association of Structural Global Brain Network Properties with Intelligence in Normal Aging , 2014, PloS one.

[35]  Jun Li,et al.  Functional brain network analysis of schizophrenic patients with positive and negative syndrome based on mutual information of EEG time series , 2017, Biomed. Signal Process. Control..

[36]  Matjaz Perc,et al.  The Matthew effect in empirical data , 2014, Journal of The Royal Society Interface.

[37]  M. Perc,et al.  Network science of biological systems at different scales: A review. , 2017, Physics of life reviews.

[38]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[39]  S. Boccaletti,et al.  Complex network theory and the brain , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[40]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[41]  Yong He,et al.  Disrupted structural and functional brain connectomes in mild cognitive impairment and Alzheimer’s disease , 2014, Neuroscience Bulletin.

[42]  Francisco del Pozo,et al.  HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity , 2013, Neuroinformatics.

[43]  Paolo Maria Rossini,et al.  Searching for signs of aging and dementia in EEG through network analysis , 2017, Behavioural Brain Research.

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

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

[46]  Qian Cui,et al.  Disrupted cortical hubs in functional brain networks in social anxiety disorder , 2015, Clinical Neurophysiology.