Étude des mécanismes neurologiques de la modulation de la rapidité d'action par la TMS et l'EEG // Exploring Neural Mechanisms Underlying Action Speed Modulation through TMS and EEG

L'accident vasculaire cérébral (AVC) constitue une cause majeure de handicap fonctionnel et est fréquemment associé à des déficits cognitifs, notamment un ralentissement de l'action et des dysfonctions exécutives, qui contribuent à un mauvais pronostic. Ce projet multimodal vise à identifier les régions cérébrales clés impliquées dans la modulation de la rapidité d'action et à développer des modèles personnalisés de connectivité effective à l'aide de la modélisation causale dynamique (DCM) et de l'EEG haute résolution.
Dans un premier temps, les interactions entre les régions cérébrales impliquées dans la rapidité d'action seront modélisées par DCM, en induisant des perturbations focales et temporaires via la stimulation magnétique transcrânienne (TMS) chez 40 participants sains. Par la suite, l'effet de la stimulation par thêta burst intermittente (TBI) sur l'amélioration de la rapidité d'action sera évalué, avec un focus spécifique sur les régions identifiées comme ayant des effets modulatoires significatifs. Bien que ce projet soit mené exclusivement sur des individus sains, les résultats obtenus seront utilisés pour optimiser les protocoles de TBI, notamment en vue d'améliorer la rapidité d'action chez les patients post-AVC. Cette approche innovante, combinant la modélisation causale et les technologies de neuromodulation, contribuerait ainsi à l'avancement des connaissances en neuromodulation et à l'amélioration des stratégies de réhabilitation neurologique.
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Stroke is a major cause of functional disability and is frequently associated with cognitive deficits, including slowed action and executive dysfunctions associated with poor prognosis. This multimodal project aims to identify key brain regions involved in action speed modulation and to develop personalized effective connectivity models using dynamic causal modeling (DCM) and high-resolution EEG in two steps. First, interactions between brain regions involved in action speed will be modeled using DCM by inducing focal and temporary perturbations via transcranial magnetic stimulation (TMS) in 40 healthy participants. Subsequently, the effect of intermittent theta-burst stimulation (iTBS) on improving action speed will be assessed, with a specific focus on regions identified as having significant modulatory effects. Although this project is conducted exclusively on healthy individuals, the findings will be used to optimize iTBS protocols, particularly with the aim of enhancing action speed in post-stroke patients.This innovative approach, combining causal modeling and neuromodulation technologies, will contribute to advancing knowledge in neuromodulation and improving neurological rehabilitation strategies.
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Début de la thèse : 01/10/2025

Contrat doctoral

Concours pour un contrat doctoral

Université de Picardie - Jules Verne

585 Sciences, Technologie, Santé

The doctoral candidate should possess a relevant MSc degree in biomedical engineering, neuroscience, engineering, or other related fields that demonstrate a strong foundation in the scientific and technical principles required for advanced research in brain function, neuromodulation, and cognitive rehabilitation. A solid understanding of the human brain's structure and function, particularly in the context of neurological disorders, is essential. In addition, the ideal candidate will have practical experience in MRI image analysis, including the processing and interpretation of brain imaging data, such as functional and structural MRI, to assess brain activity and connectivity. Experience in EEG (electroencephalography) is also highly beneficial, as this technique will be integral to the research, enabling the candidate to record and analyze brain wave patterns to understand cognitive processes in real-time. Familiarity with brain connectivity analysis methods, such as Dynamic Causal Modeling (DCM) or other network analysis techniques, will be a key advantage in exploring how different brain regions interact and how these interactions may be influenced by neuromodulation. Strong coding skills in programming languages like Matlab or Python are crucial for data processing, statistical analysis, and building computational models. These coding skills will be necessary for developing and running algorithms for processing EEG and MRI data, as well as implementing machine learning approaches to predict and analyze outcomes. Finally, experience with machine learning techniques will be an added advantage. This could involve the use of machine learning models to analyze large datasets, make predictions about brain activity, and potentially optimize neuromodulation interventions. The candidate should also be comfortable with integrating these technologies and methodologies to design and conduct experiments, analyze results, and contribute to advancing research in the field of brain connectivity and rehabilitation.
• Master's degree in a relevant field, such as neuroscience, informatics, electrical and biomedical engineering • Strong programming skills (Matlab and Python) • Good command of the English language, both written and spoken • High degree of independence and commitment • Experience with neuroimaging and EEG analysis especially brain connectivity analysis is a plus