Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution

Student thesis: Doctoral ThesisDoctor of Philosophy

Abstract

Epileptic seizures are characterised by abnormal brain dynamics at multiple scales, engaging single neurons, neuronal ensembles and coarse brain regions. At the single neuron level, seizure dynamics are highly heterogeneous, giving rise to complex non-linear behaviours at higher scales, such as high frequency oscillations, bifurcations and deterministic chaos. Key to understanding the cause of such emergent population dynamics, is capturing the collective behaviour of neuronal activity at the microscale. However, linking microscale neuronal dynamics with emergent macroscale dynamics using conventional approaches is challenging – macroscale recordings coarse grain the underlying microscale activity, while typical microscale recordings subsample the global network dynamics. In this thesis I make use of the larval zebrafish to capture single cell neuronal activity across the whole brain during epileptic seizures. Firstly, I make use of statistical physics methods to quantify the collective behaviour of single neuron dynamics during epileptic seizures. Here, I demonstrate a population mechanism through which single neuron dynamics organise into seizures – brain dynamics deviate from a phase transition. Secondly, I make use of single neuron network models to identify the synaptic mechanisms that actually cause this shift to occur. Here, I show that the density of neuronal connections in the network is key for driving generalised seizure dynamics. Interestingly, such changes also disrupt network response properties and flexible dynamics in brain networks, thus linking microscale neuronal changes with emergent brain dysfunction during seizures. Thirdly, I make use of non-linear causal inference methods to study the nature of the underlying neuronal interactions that enable seizures to occur. Here I show that seizures are driven by high synchrony but also by highly non-linear interactions between neurons. Interestingly, these non-linear signatures are filtered out at the macroscale, and therefore may represent a neuronal signature that could be used for microscale interventional strategies. This thesis demonstrates the utility of studying multi-scale dynamics in the larval zebrafish, to link neuronal activity at the microscale with emergent properties during seizures.
Date of Award1 Jan 2023
Original languageEnglish
Awarding Institution
  • King's College London
SupervisorRichard Rosch (Supervisor), Mark Richardson (Supervisor) & Martin Meyer (Supervisor)

Cite this

'