Avaliação da dinâmica do processo ictal por meio de amostras vetoriais de campo elétrico

Abstract

Epilepsy is a disease that affects approximately 50 million people, making it one of the most prevalent neurological disorders worldwide. Since the mechanisms responsible for seizure propagation across neural circuits are not yet fully understood, understanding ictal dynamics is essential for diagnosis, treatment, and the development of models for seizure prediction and prevention. In this work, we propose the use of Vector-EEG (Vector-Electroencephalogram), a technique for vectorization of electroencephalographic recordings, to study the dynamics of neural circuit recruitment during ictogenic processes. The first stage of the study analyzed hippocampal electrographic patterns under the influence of electrical stimulation applied to the basolateral amygdala, which enabled the investigation of circuits involved in the ictogenic process in a generalized seizure model. The results indicated that amygdala stimulation evokes a potential in the hippocampus and that the neural network is progressively coupled to the stimuli. We demonstrated that Vector-EEG displays characteristics distinct from conventional EEG, allowing the identification of a competitive state among circuits that precedes the electrographic seizure, followed by the dominance of a single circuit synchronized with the pathway facilitated by stimulation. The second phase of the thesis investigated the diversity and activation dynamics of circuits involved in status epilepticus in a focal epilepsy model, with bilateral recordings in the hippocampi and basolateral amygdalae. Application of Vector-EEG revealed a broader repertoire of circuits than that obtained through conventional EEG analysis. Circuits preferentially active during specific seizure periods, as well as circuits specialized in isolated seizures, were identified, indicating increasing network complexity across successive ictal events. This work also describes the development of a low-cost electrophysiological signal acquisition system, MicroMAP, including methodologies for assembling the analog-to-digital converter and constructing implants targeting multiple brain regions. The system provides a low-cost solution for electrophysiological recordings with great potential for exploring epileptogenic processes in long-term parallel experiments. Finally, we present CircadiPy, a Python library for the analysis of chronobiological time series. It offers a set of tools for extracting rhythmic data, such as locomotor activity, gene expression, and body temperature, which are important for systemic studies of epileptogenic processes