Epilepsy affects more than 65 million people worldwide and around 10.5 million of them are children. Although many children self-heal before adulthood, it has been shown that children with epilepsy confront various problems in learning, attention as well as in memory capacity. Thus, the systematic study of the brain (dys)functionality, and ultimately the design of proper treatments is one of the most challenging problems in neuroscience. Towards this aim, neuroimaging techniques and in particular EEG recordings, most commonly used for clinical assessment play an important role. However, an analysis at the scalp level does not give insight to the functionality and interactions of the “true” brain regions. On the other hand, the inverse problem, i.e. that of identifying the involved brain regions from scalp recordings is an ill-defined problem and as such a comparison between various numerical methods that solve it is critical. Here, we reconstruct the functional connectivity of brain activity of children with epilepsy based on EEG recordings of one-back matching visual discrimination working memory task. We first solve the inverse source localisation problem by using three methods, namely the standarized Low Resolution Electromagnetic Tomography (sLORETA), the weighted Minimum Norm Estimation (wMNE), and the dynamic Statistical Parametric Mapping (dSPM). Then using both linear and nonlinear causality models we reconstruct the functional connectivity network between the sources. A comparative analysis between methods and groups (epileptic vs. children) reveals different spatio-temporal patterns that may serve as “biomarkers” for diagnostic purposes and ultimately localised treatment.

Modelling and Analysis of Functional Connectivity in EEG source level in Chlidren with Epilepsy / Galaris, Evangelos; Siettos, Konstantinos. - (2019). (Intervento presentato al convegno 3rd International Conference and Summer School Numerical Computations: Theory and Algorithms).

Modelling and Analysis of Functional Connectivity in EEG source level in Chlidren with Epilepsy

Evangelos Galaris;Konstantinos Siettos
2019

Abstract

Epilepsy affects more than 65 million people worldwide and around 10.5 million of them are children. Although many children self-heal before adulthood, it has been shown that children with epilepsy confront various problems in learning, attention as well as in memory capacity. Thus, the systematic study of the brain (dys)functionality, and ultimately the design of proper treatments is one of the most challenging problems in neuroscience. Towards this aim, neuroimaging techniques and in particular EEG recordings, most commonly used for clinical assessment play an important role. However, an analysis at the scalp level does not give insight to the functionality and interactions of the “true” brain regions. On the other hand, the inverse problem, i.e. that of identifying the involved brain regions from scalp recordings is an ill-defined problem and as such a comparison between various numerical methods that solve it is critical. Here, we reconstruct the functional connectivity of brain activity of children with epilepsy based on EEG recordings of one-back matching visual discrimination working memory task. We first solve the inverse source localisation problem by using three methods, namely the standarized Low Resolution Electromagnetic Tomography (sLORETA), the weighted Minimum Norm Estimation (wMNE), and the dynamic Statistical Parametric Mapping (dSPM). Then using both linear and nonlinear causality models we reconstruct the functional connectivity network between the sources. A comparative analysis between methods and groups (epileptic vs. children) reveals different spatio-temporal patterns that may serve as “biomarkers” for diagnostic purposes and ultimately localised treatment.
2019
Modelling and Analysis of Functional Connectivity in EEG source level in Chlidren with Epilepsy / Galaris, Evangelos; Siettos, Konstantinos. - (2019). (Intervento presentato al convegno 3rd International Conference and Summer School Numerical Computations: Theory and Algorithms).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/762608
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