Graph Model Evolution During Epileptic Seizures: Linear Model Approach

Epilepsy is a brain disorder characterized by sustained predisposition to generate epileptic seizures. According to the World Health Organization, it is one of the most common neurological disorders, affecting approximately 50 million people worldwide. A modern approach for brain study is to model it as a complex system composed of a network of oscillators in which the emergent property of synchronization arises. By this token, epileptic seizures can be understood as a process of hypersynchronization between brain areas. To assess such property, Partial Directed Coherence (PDC) method represents a suitable technique, once it allows a more precise investigation of interactions that may reveal direct influences from one brain area on another. During connectivity analysis, there may be a need to assess the statistical significance of the communication threshold and Surrogate Data, a method already applied for that purpose, can be used. Hence, the objective in this work was to carry out PDC connectivity analysis in combination with Surrogate Data to evaluate the communication threshold between brain areas and develop a graph model evolution during epileptic seizure, according to the classical EEG frequency bands. The main contribution is the threshold analysis adding statistical significance for connectivity investigation. A case study performed using EEG signals from rats showed that the applied methodology represents an appropriate alternative for functional analysis, providing insights on brain communication.

[1]  Elisa Visani,et al.  Network characteristics in benign epilepsy with centro-temporal spikes patients indicating defective connectivity during spindle sleep: A partial directed coherence study of EEG signals , 2018, Clinical Neurophysiology.

[2]  Luca Faes,et al.  Testing Frequency-Domain Causality in Multivariate Time Series , 2010, IEEE Transactions on Biomedical Engineering.

[3]  L Astolfi,et al.  Multiple-Brain Connectivity During Third Party Punishment: an EEG Hyperscanning Study , 2018, Scientific Reports.

[4]  Luiz A. Baccalá,et al.  Statistically significant time-varying neural connectivity estimation using generalized partial directed coherence , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[5]  S. Bressler,et al.  Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data , 2006, Journal of Neuroscience Methods.

[6]  Aifeng Ren,et al.  Combining Partial Directed Coherence and Graph Theory to Analyse Effective Brain Networks of Different Mental Tasks , 2016, Front. Hum. Neurosci..

[7]  Aydin Akan,et al.  Synchronization analysis of epilepsy data using global field synchronization , 2017, 2017 25th Signal Processing and Communications Applications Conference (SIU).

[8]  Yogesh Rathi,et al.  A Dynamic Regression Approach for Frequency-Domain Partial Coherence and Causality Analysis of Functional Brain Networks , 2018, IEEE Transactions on Medical Imaging.

[9]  Gemma Lancaster,et al.  Surrogate data for hypothesis testing of physical systems , 2018, Physics Reports.

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

[11]  A. Schnitzler,et al.  Normal and pathological oscillatory communication in the brain , 2005, Nature Reviews Neuroscience.

[12]  S. El-Rabaie,et al.  A statistical framework for EEG channel selection and seizure prediction on mobile , 2019, International Journal of Speech Technology.

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

[14]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[15]  Ribhu Chopra,et al.  Statistical Tests for Detecting Granger Causality , 2018, IEEE Transactions on Signal Processing.

[16]  H. Eichenbaum.  A cortical–hippocampal system for declarative memory , 2000, Nature Reviews Neuroscience.

[17]  J. H. Cross,et al.  ILAE Official Report: A practical clinical definition of epilepsy , 2014, Epilepsia.

[18]  Rui Liu,et al.  Connectivity and Centrality Characteristics of the Epileptogenic Focus Using Directed Network Analysis , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Olaf Sporns,et al.  Communication dynamics in complex brain networks , 2017, Nature Reviews Neuroscience.

[20]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[21]  G. Paxinos,et al.  The Rat Brain in Stereotaxic Coordinates , 1983 .

[22]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[23]  V. R. Cota,et al.  The epileptic amygdala: Toward the development of a neural prosthesis by temporally coded electrical stimulation , 2016, Journal of neuroscience research.

[24]  N. Fountain,et al.  Functional anatomy of limbic epilepsy: a proposal for central synchronization of a diffusely hyperexcitable network , 1998, Epilepsy Research.

[25]  Lauren C. Frey,et al.  Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants , 2016 .

[26]  Jens Haueisen,et al.  Multivariate assessment of the central-cardiorespiratory network structure in neuropathological disease , 2018, Physiological measurement.

[27]  Carlos Dias Maciel,et al.  Delayed mutual information infers patterns of synaptic connectivity in a proprioceptive neural network , 2015, Journal of Computational Neuroscience.

[28]  Carlos Dias Maciel,et al.  Pre-processing and transfer entropy measures in motor neurons controlling limb movements , 2017, Journal of Computational Neuroscience.

[29]  I. Marín-Valencia,et al.  Synaptic energy metabolism and neuronal excitability, in sickness and health , 2019, Journal of inherited metabolic disease.

[30]  Jari Hyttinen,et al.  Dynamics of intracranial electroencephalographic recordings from epilepsy patients using univariate and bivariate recurrence networks. , 2015, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  K. Sameshima,et al.  Connectivity Inference between Neural Structures via Partial Directed Coherence , 2007 .

[32]  David Gutiérrez,et al.  Using the Partial Directed Coherence to Assess Functional Connectivity in Electroencephalography Data for Brain–Computer Interfaces , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[33]  Rodrigo Quian Quiroga,et al.  Nonlinear multivariate analysis of neurophysiological signals , 2005, Progress in Neurobiology.

[34]  Thomas Lehmann,et al.  Influence of imputation strategies on the identification of brain functional connectivity networks , 2018, Journal of Neuroscience Methods.

[35]  Luca Faes,et al.  Surrogate data analysis for assessing the significance of the coherence function , 2004, IEEE Transactions on Biomedical Engineering.

[36]  C. Duarte,et al.  Alterations in GABAA-Receptor Trafficking and Synaptic Dysfunction in Brain Disorders , 2019, Front. Cell. Neurosci..

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

[38]  Shennan A. Weiss,et al.  Interneurons and principal cell firing in human limbic areas at focal seizure onset , 2019, Neurobiology of Disease.

[39]  Fabrice Wendling,et al.  Defining epileptogenic networks: Contribution of SEEG and signal analysis , 2017, Epilepsia.

[40]  H. Eichenbaum,et al.  Interplay of Hippocampus and Prefrontal Cortex in Memory , 2013, Current Biology.