Personalized identification, prediction, and stimulation of neural oscillations via data-driven models of epileptic network dynamics

Neural oscillations are considered to be brain-specific signatures of information processing and communication in the brain. They also reflect pathological brain activity in neurological disorders, thus offering a basis for diagnoses and forecasting. Epilepsy is one of the most common neurological d...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Dubcek, Tena, Ledergerber, Debora, Thomann, Jana, Aiello, Giovanna, Serra-Garcia, Marc, Imbach, Lukas, Polania, Rafael
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.10.2023
Subjects
Brain
Data processing
Dynamics
Entrainment
Epilepsy
Neurological diseases
Neurological disorders
Oscillations
Prediction models
Stimulation
Synchronism
Online AccessGet full text

Cover

More Information
Summary:Neural oscillations are considered to be brain-specific signatures of information processing and communication in the brain. They also reflect pathological brain activity in neurological disorders, thus offering a basis for diagnoses and forecasting. Epilepsy is one of the most common neurological disorders, characterized by abnormal synchronization and desynchronization of the oscillations in the brain. About one third of epilepsy cases are pharmacoresistant, and as such emphasize the need for novel therapy approaches, where brain stimulation appears to be a promising therapeutic option. The development of brain stimulation paradigms, however, is often based on generalized assumptions about brain dynamics, although it is known that significant differences occur between patients and brain states. We developed a framework to extract individualized predictive models of epileptic network dynamics directly from EEG data. The models are based on the dominant coherent oscillations and their dynamical coupling, thus combining an established interpretation of dynamics through neural oscillations, with accurate patient-specific features. We show that it is possible to build a direct correspondence between the models of brain-network dynamics under periodic driving, and the mechanism of neural entrainment via periodic stimulation. When our framework is applied to EEG recordings of patients in status epilepticus (a brain state of perpetual seizure activity), it yields a model-driven predictive analysis of the therapeutic performance of periodic brain stimulation. This suggests that periodic brain stimulation can drive pathological states of epileptic network dynamics towards a healthy functional brain state.
ISSN:2331-8422