Modélisation et contrôle d'oscillations cérébrales à partir de données de l'optogénétique

par Jakub OrŁOwski

Projet de thèse en Automatique

Sous la direction de Antoine Chaillet et de Alain Destexhe.

Thèses en préparation à Paris Saclay , dans le cadre de Sciences et Technologies de l'Information et de la Communication , en partenariat avec L2S - Laboratoire des signaux et systèmes (laboratoire) et de Université Paris-Sud (établissement de préparation de la thèse) depuis le 01-10-2016 .


  • Résumé

    Neuronal oscillations are ubiquitous in the brain, both in health and disease. Nonetheless, the precise role of these oscillations is still a matter of debate, and the mechanisms by which they are generated are still poorly understood. Technological advances offer unprecedented ways to acquire and influence these oscillations. Electrode arrays and electrodes with dense recording plots now provide excellent spatiotemporal resolution of local brain activity. Moreover, the recent advent of optogenetics is revolutionizing the way of stimulating brain structures. The combination of electrophysiological recordings and optogenetics is thus particularly appealing to decipher the mechanisms of oscillation generation, their role in brain functioning, and the development of closed-loop strategies to steer brain oscillations (especially pathological ones). This project aims at developing and validating ad hoc methodologies to model, identify, analyze and control brain oscillations with these experimental tools. The challenges in that direction are numerous due to the nonlinear and spatiotemporal nature of the processes involved. To address these challenges, this project proposes to adapt or develop methodologies from control theory to the brain dynamics specificities. The performance of the developed methodologies will be confronted to experimental data of pathological oscillations linked to parkinsonian symptoms, that were collected on healthy and parkinsonian primates under optogenetics in the ANR project SynchNeuro. The aim of this PhD thesis is to develop a methodological framework, based on control engineering, to address three challenges: 1) development of a spatiotemporal model of specific brain oscillations, whose parameters are identified based on experimental data 2) formal analysis of brain oscillations onset and characterization of their dynamics in terms of magnitude, frequency and phase 3) development of closed-loop photostimulation strategies to disrupt, attenuate, or steer brain oscillations.

  • Titre traduit

    Modeling and steering brain oscillations based on in vivo optogenetics data


  • Résumé

    Neuronal oscillations are ubiquitous in the brain, both in health and disease. Nonetheless, the precise role of these oscillations is still a matter of debate, and the mechanisms by which they are generated are still poorly understood. Technological advances offer unprecedented ways to acquire and influence these oscillations. Electrode arrays and electrodes with dense recording plots now provide excellent spatiotemporal resolution of local brain activity. Moreover, the recent advent of optogenetics is revolutionizing the way of stimulating brain structures. The combination of electrophysiological recordings and optogenetics is thus particularly appealing to decipher the mechanisms of oscillation generation, their role in brain functioning, and the development of closed-loop strategies to steer brain oscillations (especially pathological ones). This project aims at developing and validating ad hoc methodologies to model, identify, analyze and control brain oscillations with these experimental tools. The challenges in that direction are numerous due to the nonlinear and spatiotemporal nature of the processes involved. To address these challenges, this project proposes to adapt or develop methodologies from control theory to the brain dynamics specificities. The performance of the developed methodologies will be confronted to experimental data of pathological oscillations linked to parkinsonian symptoms, that were collected on healthy and parkinsonian primates under optogenetics in the ANR project SynchNeuro. The aim of this PhD thesis is to develop a methodological framework, based on control engineering, to address three challenges: 1) development of a spatiotemporal model of specific brain oscillations, whose parameters are identified based on experimental data 2) formal analysis of brain oscillations onset and characterization of their dynamics in terms of magnitude, frequency and phase 3) development of closed-loop photostimulation strategies to disrupt, attenuate, or steer brain oscillations.