Frameworks for improving brain network interventions through computational models

Abstract

Disorders which impact the whole brain, such as epilepsy, depression and schizophrenia, have a severe impact on people's lives. Pharmaceutical therapies act on a systemic level and can result in numerous unpleasant side effects. Targeted clinical treatments, like epilepsy surgery, have variable success rates. Alternative targeted therapies such as transcranial direct current stimulation (tDCS) have shown promise for treating brain disorders. tDCS involves applying small electric felds to the brain with minimal reported side effects. However, tDCS is hampered by variability in efficacy, which has prevented widespread clinical adoption. In this thesis I implement computational models that can take patient-specifc data to predict treatment efficacy. In chapter 3 I show an implementation of a model of tDCS at the level of brain tissue. Current finite element models (FEMs) can provide patient-specific predictions of current ow, but they cannot predict the functional impact on neural activity. I develop a model that takes a FEM electric field as an input and predicts its effect on neural tissue. I validate this model with comparison to published experimental data. In chapter 4 I apply the model to predict the impact of electric fields on tissue exhibiting beta oscillations. Beta oscillations are linked to a number of disorders, including movement disorders (Basha et al., 2014; Little and Brown, 2014) and epilepsy (Koelewijn et al., 2015; Hamandi et al., 2011). The model is additionally extended to have multiple regions. I show that stimulation effects can spread to, and accumulate in, untargeted regions. Finally, in chapter 5 I move to a whole brain model using patient derived connectivity. This model provides a framework for personalising surgery procedures based on patient connectivity data. The models I present provide frameworks for computational approaches to improve clinical practice. By using computer modelling patient-specific approaches become tractable. I hope that this work will facilitate patient-specific treatment planning, leading to improvement in clinical treatment for a range of neurological disorders.