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 ictogenesis and a significant correlation with neurobiology derived from pioneering studies employing techniques of pharmacological manipulation, lesion, and modern optogenetics. The arcs evaluated under the proposed approach achieved consistent results based on previous literature, in addition to demonstrating robustness regarding functional connectivity analysis. Moreover, it provided fascinating and novel insights, such as discontinuity between forelimb clonus and generalized tonic-clonic seizure (GTCS) dynamics. Thus, DBN coupled with threshold analytics may be an excellent tool for investigating brain circuitry and their dynamical interplay, both in homeostasis and dysfunction conditions.

On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain’s functional connectogram

“…Understanding time-dependent events in the target neural circuit is critical to optimize parameters for activity disruption. Therefore, computational modeling and case analysis, fundamental in translating the method to greater applicability in clinical practice, is currently being carried out ( Carvalho et al, 2021 ; Batista Tsukahara et al, 2022 ; Oliveira et al, 2022 ; Terra et al, 2022 ). Finally, spatiotemporally complex ES (NPS included) is a major plus if one considers the application of neuromodulation in a personalized or individualized fashion.…”

“…Considering the well-known aberrations of electrophysiological neural activity underlying epileptic phenomena in general and hyper-synchronism of seizures in particular ( Avoli et al, 2002 , 2004 ; Benini et al, 2003 ; Garcia et al, 2005 ; Nariai et al, 2011 ; Medeiros et al, 2014 ; Edakawa et al, 2016 ; Li et al, 2016 ; Abreu et al, 2018 ; Yu et al, 2018 ; Batista Tsukahara et al, 2022 ), epilepsy was also a natural application field for temporally complex ES. At the beginning of 2000’s, our group devised, patented, and tested—possibly the first therapeutically successful in vivo application of temporally structured ES—a novel temporally irregular pattern of ES later termed non-periodic stimulation (or NPS), in which the IPI were randomized in real-time, with the important advantage of being low frequency on average (mean of 4 pulses per second) ( Cota et al, 2009 , 2021 ; de Oliveira et al, 2019 ).…”

The Time Lag in Local Field Potential Signals for the Development of its Bayesian Belief Network

“…Several studies in the literature make use of Bayesian Networks (Bayesian Network (BN)). Tsukahara et al (2022) [29] used Bayesian Networks to model Local Field Potentials (Local Field Potential (LFP)) recordings of rats induced to epilepsy and the arcs evaluated using an analytical threshold approach. Sip et al (2021) [30] developed a data-driven method, based on Bayesian Inference, to infer the seizure propagation patterns in an epileptic brain from intracranial electroencephalography.…”