Computational Models For Epilepsy

TL;DR

This paper reviews various computational models for epilepsy, highlighting their role in understanding brain mechanisms and aiding in the development of new treatment strategies for drug-resistant cases.

Contribution

It provides a comprehensive survey of computational approaches in epilepsy research, emphasizing their potential in advancing diagnosis and therapy.

Findings

Computational models help elucidate epileptic mechanisms.

Models assist in developing new treatment protocols.

Survey covers diverse modeling techniques.

Abstract

Epilepsy is a neurological disease characterized by recurrent and spontaneous seizures. It affects approximately 50 million people worldwide. In majority of the cases accurate diagnosis of the disease can be made without using any technologically advanced techniques and seizures are controlled using standard treatment in the form of regular use of anti-epileptic drugs. However, approximately 30% of the patients suffer from medically refractory epilepsy, wherein seizures are not controlled by the use of anti-epileptic drugs. Understanding the mechanisms underlying these forms of drug resistant epileptic seizures and the development of alternative effective treatment strategies is a fundamental challenge in modern epilepsy research. In this context, the need for integrative approaches combining various modalities of treatment strategies is high. Computational modeling has gained prominence in recent years as an important tool for tackling the complexity of the epileptic phenomenon. In this review article we present a survey of different computational models for epilepsy and discuss how computer models can aid in our understanding of brain mechanisms in epilepsy and the development of new epilepsy treatment protocols.