Personalised network modelling in epilepsy
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.