Personalised network modelling in epilepsy

TL;DR

This paper reviews how personalised brain network models, based on patient data, can predict seizure dynamics and treatment outcomes in epilepsy, aiming to improve individualized clinical decision-making.

Contribution

It introduces the use of patient-derived network models constrained by structural or functional data for personalised seizure prediction and treatment planning in epilepsy.

Findings

Patient-specific network models can predict seizure propagation patterns.

Models constrained by patient data can forecast surgical outcomes.

Future directions include integrating multiple data modalities and predicting long-term disease evolution.

Abstract

Epilepsy is a disorder characterised by spontaneous, recurrent seizures. Both local and network abnormalities have been associated with epilepsy, and the exact processes generating seizures are thought to be heterogeneous and patient-specific. Due to the heterogeneity, treatments such as surgery and medication are not always effective in achieving full seizure control and choosing the best treatment for the individual patient can be challenging. Predictive models constrained by the patient's own data therefore offer the potential to assist in clinical decision making. In this chapter, we describe how personalised patient-derived networks from structural or functional connectivity can be incorporated into predictive models. We focus specifically on dynamical systems models which are composed of differential equations capable of simulating brain activity over time. Here we review recent studies which have used these models, constrained by patient data, to make personalised patient-specific predictions about seizure features (such as propagation patterns) or treatment outcomes (such as the success of surgical resection). Finally, we suggest future research directions for patient-specific network models in epilepsy, including their application to integrate information from multiple modalities, to predict long-term disease evolution, and to account for within-subject variability for treatment.