Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution

Epileptic seizures are characterised by abnormal brain dynamics at multiple scales, engaging single neurons, neuronal ensembles and coarse brain regions. Key to understanding the cause of such emergent population dynamics, is capturing the collective behaviour of neuronal activity at multiple brain scales. 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

Network Properties Revealed during Multi-Scale Calcium Imaging of Seizure Activity in Zebrafish.

Seizures are characterized by hypersynchronization of neuronal networks. Understanding these networks could provide a critical window for therapeutic control of recurrent seizure activity, i.e., epilepsy. However, imaging seizure networks has largely been limited to microcircuits in vitro or small "windows" in vivo. Here, we combine fast confocal imaging of genetically encoded calcium indicator (GCaMP)-expressing larval zebrafish with local field potential (LFP) recordings to study epileptiform events at whole-brain and single-neuron levels in vivo. Using an acute seizure model (pentylenetetrazole, PTZ), we reliably observed recurrent electrographic ictal-like events associated with generalized activation of all major brain regions and uncovered a well-preserved anterior-to-posterior seizure propagation pattern. We also examined brain-wide network synchronization and spatiotemporal patterns of neuronal activity in the optic tectum microcircuit. Brain-wide and single-neuronal level analysis of PTZ-exposed and 4-aminopyridine (4-AP)-exposed zebrafish revealed distinct network dynamics associated with seizure and non-seizure hyperexcitable states, respectively. Neuronal ensembles, comprised of coactive neurons, were also uncovered during interictal-like periods. Taken together, these results demonstrate that macro- and micro-network calcium motifs in zebrafish may provide a greater understanding of epilepsy

Multiscale Modelling of Neuronal Dynamics and Their Dysfunction in the Developing Brain

Over the last few decades, an increasing number of neurodevelopmental disorders has been associated with molecular causes – such as genetic mutations, or autoantibodies affecting synaptic transmission. Yet understanding the pathophysiology that leads from particular molecular disruptions at the synapse to patients’ signs and symptoms remains challenging, even today. The work presented in this thesis illustrates how computational models can help bridge the explanatory gap between disruptions at the molecular scale and brain dysfunction at the level of integrated circuits. I utilise computational models at different scales of neuronal function, ranging from the neuronal membrane, to integrated cortical microcircuits and whole-brain sensory processing networks. These computational models are informed with, and further constrained by both empirical data derived from a number of model systems of neurodevelopmental disorders, and clinical patient data. The worked examples in this thesis include the biophysical characterisation of an epilepsy-causing mutation in the voltage-gated sodium channel gene SCN1A, calcium imaging in a larval zebrafish model of epileptic seizures in the immature brain, electrophysiological recordings from patients with NMDA receptor antibody encephalitis as well as from a mouse model of the disorder, and pharmacologically induced NMDA receptor blockade in young adults that captures features of acute psychosis and schizophrenia. The combination of this diverse range of empirical data and different computational models offers a mechanistic, multi-scale account of how specific phenotypic features in neurodevelopmental disorders emerge. This provides novel insights both in regard to the specific conditions included here, but also concerning the link between molecular determinants and their neurodevelopmental phenotypes more broadly

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

Linking fast and slow: the case for generative models

A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multi-scale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature.Comment: 20 pages, 5 figure

Cortical circuits for visual processing and epileptic activity propagation

The thesis focuses on the relationship between cortical connectivity and cortical function. The first part investigates how the fine scale connectivity between visual neurons determines their functional responses during physiological sensory processing. The second part ascertains how the mesoscopic scale connectivity between brain areas constrains the spread of abnormal activity during the propagation of focal cortical seizures. Part 1: Neurons in the primary visual cortex (V1) are tuned to retinotopic location, orientation and direction of motion. Such selectivity stems from the integration of inputs from hundreds of presynaptic neurons distributed across cortical layers. Yet, the functional principles that organize such presynaptic networks have only begun to be understood. To uncover them, I used monosynaptic rabies virus tracing to target a single pyramidal neuron in L2/3 (starter neuron) and trace its presynaptic partners. I combined this approach with two-photon microscopy in V1 to investigate the relationship between the activity of the starter cell, its presynaptic neurons and the surrounding excitatory population across cortical layers in awake animals. Part 2: Focal epilepsy involves excessive and synchronous cortical activity that propagates both locally and distally. Does this propagation follow the same functional circuits as normal cortical activity? I induced focal seizures in primary visual cortex (V1) of awake mice, and compared their propagation to the retinotopic organization of V1 and higher visual areas. I measured activity through simultaneous local field potential recordings and widefield calcium imaging, and observed prolonged seizures that were orders of magnitude larger than normal visual responses. I demonstrate that seizure start as standing waves (synchronous elevated activity in the focal V1 region and in corresponding retinotopic locations in higher areas) and then propagate both locally and into distal regions. These regions matched each other in retinotopy. I conclude that seizure propagation respects the connectivity underlying normal visual processing

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)