Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis.

ABSTRACT: Epilepsy is one of the most common neurological disorders worldwide. Recent findings suggest that the brain is a complex system composed of a network of neurons, and seizure is considered an emergent property resulting from its interactions. Based on this perspective, network physiology has emerged as a promising approach to explore how brain areas coordinate, synchronize and integrate their dynamics, both under perfect health and critical illness conditions. Therefore, the objective of this paper is to present an application of (Dynamic) Bayesian Networks (DBN) to model Local Field Potentials (LFP) data on rats induced to epileptic seizures based on the number of arcs found using threshold analytics. Results showed that DBN analysis captured the dynamic nature of brain connectivity across

SUBMITTER: Batista Tsukahara VH 

PROVIDER: S-EPMC9399918 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature