Subject Ph. D.

Background Schizophrenic disorders (SCZ) are psychiatric disorders that affect approximately 1% of the world's population. According to the DSM-V [1], they are characterized by a range of behavioral and emotional symptoms, some of which are poorly managed by pharmacological treatments [2]. One of the main obstacles to the development of effective therapies remains our limited understanding of the underlying pathophysiological mechanisms. In order to overcome this deficiency, animal models of SCZ have been developed for several years, allowing to demonstrate perturbations of hippocampal synaptic properties [3]. These involve in particular the excitation-inhibition balance with a crucial role played by alterations in ion channels, essentially potassium and calcium channels [4,5]. However, animal models alone do not provide complete answers to these questions because it is impossible to fully dissociate the cellular, synaptic and topological properties of the neuronal mechanisms and networks involved in the pathology.

Objectives The objective of this work is to study the pathophysiological mechanisms of SCZ using mathematical modeling, simulation and signal processing approaches based on an animal model of the pathology. This multidisciplinary approach will allow us to analyze the individual contribution of ion channels (excitation-inhibition balance), synaptic perturbations (neurotransmitters) and structural connectivity modifications (topology of connections, projections, etc). We hypothesize that the phenomena observed in animal models could be the result of a combination of these factors. If this hypothesis were to be confirmed, it would open the way to new individualized therapeutic targets.

Methodology and techniques used Modeling The work will be based on a mathematical model of the hippocampus [11,12] already developed in the framework of the theses of F Giovannini and A Aussel, co-supervised by L Buhry (LORIA) and R Ranta (CRAN). The 1st step will consist in adapting this human model to a mouse model by using the connectome data of the Allen Institute, then to complete it by integrating different types of interneurons, which can play a crucial role in the synchronization of the neural network activities. The 2nd step will aim at implementing synaptic plasticity mechanisms and will involve parallel programming skills for the optimized implementation of networks and the solution of nonlinear differential equations in very high dimensional graphs. In this perspective, the PhD student will interact with J Gaidamour (IECL), but also with the developers of Brian (Inst. de la Vision, Paris) used to implement our initial model. Once the model has been designed under non-pathological conditions, we will explore through simulation different pathophysiological scenarios by confronting its outputs to strictly controlled electrophysiological recordings on electrically stimulated hippocampal slices performed by our collaborators (COMETE UMR 1075 INSERM, Univ. de Caen), on an animal model of SCZ [3]. Some of these mechanisms are currently being explored in the framework of the thesis prepared by L Raison-Aubry and the postdoctoral work of L Naudin, under the supervision of L Buhry [10, 13]. Electrophysiological signals In order to confront the computational model with real signals, it is necessary to add a step that will allow the generation of electric fields depending on the neuronal morphology and anatomy. In our previous work, this step essentially included dipolar synaptic contributors [12] that we wish to enrich here by integrating action potentials. First results [6] indicate that the contribution of the latter in the high frequencies can be significant (see also [7]) and we wish to compare these models with in vitro recordings. To do so, these signals will require a preliminary treatment, in order to separate the different contributors that generate them. Recent developments (P Jurczinsky's thesis and related work) on the separation of spikes-LFP (action potentials- synaptic currents) [8] and on near vs. propagated activity [9] will be adapted to the context of multidimensional recordings on slice (multi-electrode arrays MEA).