For causal estimation in brain pathological conditions, models offer crucial insights into neurophysiological mechanisms. Model-based inference involves constructing a statistical or mechanistic model that captures the essential features of the data-generating process. In this context, simulation-based inference (SBI) using deep neural networks provides an invertible map between parameters and data features. Therefore, there is a need to automatically extract the low-dimensional informative data features, to train the neural networks, the so-called Normalizing-Flows. In this project, our goal is to utilize machine learning algorithms to provide input into the SBI pipeline. The data for this project will be, simulated data from models at different scales and corresponding electrophysiological data such as patch-clamp and intracranial stereoelectroencephalography (SEEG) recordings. The objective is to classify seizure onset and bifurcation patterns from the complex biophysical models. The trainee will benefit from the support of the various skills available within our teams.

There are over 30 million people with drug-resistant epilepsy worldwide. When neuroimaging and non-invasive neural recordings fail to localise seizure onset zones (SOZ), intracranial recordings become the best chance for localisation and seizure-freedom in those patients. However, intracranial neural activities remain hard to visually discriminate across recording channels, which limits the success of intracranial visual investigations. In this presentation, I present methods which quantify intracranial neural time series and combine them with explainable machine learning algorithms to localise the SOZ in the epileptic brain. I present the potentials and limitations of our methods in the localisation of SOZ in epilepsy providing insights for future research in this area.

How seizures emerge from the abnormal dynamics of neural networks within the epileptogenic tissue remains an enigma. Are seizures random events, or do detectable changes in brain dynamics precede them? Are mechanisms of seizure emergence identical at the onset and later stages of epilepsy? Is the risk of seizure occurrence stable, or does it change over time? A myriad of questions about seizure genesis remains to be answered to understand the core principles governing seizure genesis. The last decade has brought unprecedented insights into the complex nature of seizure emergence. It is now believed that seizure onset represents the product of the interactions between the process of a transition to seizure, long-term fluctuations in seizure susceptibility, epileptogenesis, and disease progression. During the lecture, we will review the latest observations about mechanisms of ictogenesis operating at multiple temporal scales. We will show how the latest observations contribute to the formation of a comprehensive theory of seizure genesis, and challenge the traditional perspectives on ictogenesis. Finally, we will discuss how combining conventional approaches with computational modeling, modern techniques of in vivo imaging, and genetic manipulation open prospects for exploration of yet hidden mechanisms of seizure genesis.

A myriad of pathological changes associated with epilepsy, including the loss of specific cell types, improper expression of individual ion channels, and synaptic sprouting, can be recast as decreases in cell and circuit heterogeneity. In recent experimental work, we demonstrated that biophysical diversity is a key characteristic of human cortical pyramidal cells, and past theoretical work has shown that neuronal heterogeneity improves a neural circuit’s ability to encode information. Viewed alongside the fact that seizure is an information-poor brain state, these findings motivate the hypothesis that epileptogenesis can be recontextualized as a process where reduction in cellular heterogeneity renders neural circuits less resilient to seizure onset. By comparing whole-cell patch clamp recordings from layer 5 (L5) human cortical pyramidal neurons from epileptogenic and non-epileptogenic tissue, we present the first direct experimental evidence that a significant reduction in neural heterogeneity accompanies epilepsy. We directly implement experimentally-obtained heterogeneity levels in cortical excitatory-inhibitory (E-I) stochastic spiking network models. Low heterogeneity networks display unique dynamics typified by a sudden transition into a hyper-active and synchronous state paralleling ictogenesis. Mean-field analysis reveals a distinct mathematical structure in these networks distinguished by multi-stability. Furthermore, the mathematically characterized linearizing effect of heterogeneity on input-output response functions explains the counter-intuitive experimentally observed reduction in single-cell excitability in epileptogenic neurons. This joint experimental, computational, and mathematical study showcases that decreased neuronal heterogeneity exists in epileptogenic human cortical tissue, that this difference yields dynamical changes in neural networks paralleling ictogenesis, and that there is a fundamental explanation for these dynamics based in mathematically characterized effects of heterogeneity. These interdisciplinary results provide convincing evidence that biophysical diversity imbues neural circuits with resilience to seizure and a new lens through which to view epilepsy, the most common serious neurological disorder in the world, that could reveal new targets for clinical treatment.

Seizure onset is a critically important brain state transition that has proved very difficult to predict accurately from recordings of brain activity. I will present new data acquired using a range of optogenetic and imaging tools to characterize exactly how cortical networks change in the build-up to a seizure. I will show how intermittent optogenetic stimulation ("active probing") reveals a latent change in dendritic excitability that is tightly correlated to the onset of seizure activity. This data relates back to old work from the 1980s suggesting a critical role in epileptic pathophysiology for dendritic plateau potentials. Our data show how the precipitous nature of the transition can be understood in terms of multiple, synergistic positive feedback mechanisms.

Dravet syndrome is a severe childhood epilepsy due to heterozygous loss-of-function mutation of the gene SCN1A, which encodes the type 1 neuronal voltage gated sodium (Na+) channel alpha-subunit Nav1.1. Prior studies in mouse models of Dravet syndrome (Scn1a+/- mice) at early developmental time points indicate that, in cerebral cortex, Nav1.1 is predominantly expressed in GABAergic interneurons (INs) and, in particular, in parvalbumin-positive fast-spiking basket cells (PV-INs). This has led to a model of Dravet syndrome pathogenesis whereby Nav1.1 mutation leads to preferential IN dysfunction, decreased synaptic inhibition, hyperexcitability, and epilepsy. We found that, at later developmental time points, the intrinsic excitability of PV-INs has essentially normalized, via compensatory reorganization of axonal Na+ channels. Instead, we found persistent and seemingly paradoxical dysfunction of putative disinhibitory INs expressing vasoactive intestinal peptide (VIP-INs). In vivo two-photon calcium imaging in neocortex during temperature-induced seizures in Scn1a+/- mice showed that mean activity of both putative principal cells and PV-INs was higher in Scn1a+/- relative to wild-type controls during quiet wakefulness at baseline and at elevated core body temperature. However, wild-type PV-INs showed a progressive synchronization in response to temperature elevation that was absent in PV-INs from Scn1a+/- mice immediately prior to seizure onset. We suggest that impaired PV-IN synchronization, perhaps via persistent axonal dysfunction, may contribute to the transition to the ictal state during temperature induced seizures in Dravet syndrome.