Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis - Batista Tsukahara, Victor Hugo; de Oliveira Júnior, Jordão Natal; de Oliveira Barth, Vitor Bruno; de Oliveira, Jasiara Carla; Rosa Cota, Vinicius; Maciel, Carlos Dias (Frontiers in Neural Circuits, 2022)
Epilepsy is one of the most common neurological disorders worldwide. Recent findings regarding the disease have led to the theory that brain is a complex system composed by a network of neurons and seizure is considered an emergent property resulted from its interactions. Based on this modern perspective, network physiology emerged to address the question of how brain areas coordinate, synchronize and integrate their dynamics during healthy and under ill conditions. The objective of this paper is to present the application of (Dynamic) Bayesian Networks (DBN) to model Local Field Potentials (LFP) recordings of rats which were induced to epileptic seizures, and the arcs were evaluated using an analytical threshold approach. Results showed that DBN analysis was able to capture very well the dynamic nature of brain connectivity across ictogenesis with great correlation to neurobiology derived from pioneering studies which employed techniques of pharmacological manipulation, lesion, and also modern optogenetics. Additionally, it provided interesting novel insights such as the discontinuity between forelimb clonus and generalized tonic clonic seizure (GTCS) dynamics. The arcs evaluation under the proposed approach was consistent with previous literature and provided robustness to functional connectivity analysis. For these reasons, DBN cmbined with analytical threshold may be a additional great tool for investigation of brain circuitry and their dynamical interplay in both homeosthasis and dysfunction.