ED Sciences Chimiques
Design of dynamic polymer networks based on N-heterocyclic carbenes: from catalysis to 3D printing
by Karine ABOU EZZE (Laboratoire de Chimie des Polymères Organiques)
The defense will take place at 14h00 - Amphi 1 16 Avenue Pey Berland, 33600 Pessac
in front of the jury composed of
- Daniel TATON - Professeur - Université de Bordeaux - Directeur de these
- Eric DROCKENMULLER - Professeur - Université Claude Bernanrd Lyon 1 - Rapporteur
- Julien PINAUD - Maître de conférences - Université de Montpellier - Rapporteur
- Marc GUERRE - Chargé de recherche - Université de Toulouse - Examinateur
- Corinne SOULIé-ZIAKOVIC - Professeure - École Supérieure de Physique et de Chimie Industrielles de la ville de Paris - Examinateur
- Audrey LLEVOT - Maîtresse de conférences - Université de Bordeaux - CoDirecteur de these
N-heterocyclic carbenes (NHCs) may offer new perspectives for the design of dynamic, multifunctional polymer networks. In a first part of this thesis work, copolymers functionalized with benzimidazolium units were designed, then reversibly crosslinked via dimerization of supported NHCs units generated by deprotonation. The dynamic bond thus created can be broken by addition of CO₂, enabling chemical recycling of the network. Under the effect of temperature, these same copolymers release active NHCs units to catalyze the benzoin condensation reaction. In a second section, the integration of benzimidazolium acetate salts into vitrimers based on transesterification reactions was explored, in order to ensure autocatalysis. Formulated from acrylate units and 3D printed, these materials feature tunable mechanical properties, as well as the ability to be reshaped at high temperatures. Finally, polymer networks cross-linked by (NHC)2-PdII complexes have been developed. Endowed with dynamic properties, these metallopolymers proved to be excellent catalysts for the Suzuki-Miyaura reaction, particularly under mechanical activation using a ball mill. Their insolubility makes them easy to recover after reaction, for reuse in new catalytic cycles.
ED Sciences de la Vie et de la Santé
Relevance of disconnectome approaches for prognostication following ischemic cerebral infarct
by Anna MATSULEVITS (Neurocentre Magendie)
The defense will take place at 14h00 - Ground floor Centre Broca Nouvelle Aquitaine, 146 Rue Léo Saignat, 33000 Bordeaux
in front of the jury composed of
- Thomas TOURDIAS - Professeur des universités - praticien hospitalier - Université de Bordeaux - Directeur de these
- Mallar CHAKRAVARTY - Full professor - Department of Psychiatry, McGill University - Examinateur
- Marine LUNVEN - Associate Professor - Département d'Études Cognitives, École Normale Supérieure-PSL - Examinateur
- Michel THIEBAUT DE SCHOTTEN - Directeur de recherche - University of Bordeaux - CoDirecteur de these
- Daniel MARGULIES - Directeur de recherche - Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique - Rapporteur
- Stephanie FORKEL - Assistant professor - Max Planck Institute for Psycholinguistics - Rapporteur
The human brain is both the seat of self and the instrument of inquiry, uniquely positioned as both the subject and object of scientific investigation. In this thesis, I explore how the disruption of brain architecture, particularly white matter connectivity, following ischemic stroke accelerates neurodegenerative processes and impacts recovery trajectories. By leveraging the power of artificial intelligence (AI), this work aims to synthesize, model, and predict the cascading effects of vascular insults on the structural and functional integrity of the brain. To address the complexity and urgency inherent in stroke care, the thesis presents a series of methodologically interconnected studies. These span from the acute to the long-term phase of stroke, unified by the goal of enhancing diagnosis, understanding, and prognosis. First, I developed DeepDisco, a deep learning-based framework capable of synthesizing individualized maps of white matter disconnectivity from binary lesion data. By reproducing disconnection patterns within milliseconds, DeepDisco provides a scalable and clinically viable alternative to traditional tractography-based approaches and improves symptom prediction by reducing noise in connectomic profiles. Second, I introduced SynthPerf, an AI-based tool designed to generate perfusion-like maps critical for identifying salvageable brain tissue, using routinely available MRI sequences. This innovation eliminates the need for contrast agents and reduces imaging delays, making perfusion assessment more accessible, particularly in resource-limited settings. To assess the real-world relevance of these tools, the thesis coordinated the NeuralCup, a multi-institutional competition inviting leading stroke outcome prediction teams to apply their methods to a shared dataset. Through a systematic comparison of diverse modeling strategies, we identified not a single best approach, but a context-dependent framework: cognitive, motor, and global outcome predictions benefited from different combinations of imaging modalities and analytical strategies. This suggests the need to move beyond one-size-fits-all models towards tailored, domain-specific prediction recipes. Expanding beyond stroke, the thesis introduces the concept of microstructural neurobiological signatures, multi-dimensional fingerprints capturing disease-specific alterations in tissue architecture. Using large-scale datasets and dimensionality reduction techniques, I propose a morphospace that situates these profiles within a continuum of neurodegenerative processes. By modeling how these signatures evolve over time and correlate with structural disconnection, we lay the foundation for spatiotemporal gradients, a framework capable of tracking disease progression dynamically across both space and time. Altogether, this thesis illustrates how AI-driven tools can augment neuroimaging to render invisible pathophysiological cascades visible, thereby enhancing clinical workflows and enabling more personalized care. The proposed models and conceptual frameworks pave the way for early diagnosis, targeted intervention, and long-term monitoring of neurological diseases, ultimately contributing to the broader goals of precision medicine. By integrating biology, imaging, and computation, the work conducted during the PhD seeks to transform our capacity to understand, predict, and eventually prevent the chain reactions of brain disease.