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The objective of this project is to develop an innovative computational medical imaging strategy for optimizing clinical outcome of Deep Brain Stimulation (DBS) for Parkinson’s disease.
Rennes MediCIS
Paris ICM
Paris Neurospin
Boston BWH
The objective of this project is to develop an innovative computational medical imaging strategy for optimizing clinical outcome of Deep Brain Stimulation (DBS) for Parkinson’s disease. We aim at developing models that outline relationships between structure and function of the basal ganglia and its surroundings based on population and patient specific analysis of multimodal data. Knowing from post operative images where the electrode and the activated contact are, fiber tracts activated by DBS will be correlated to function and clinical data including electro-physiological signals acquired during surgery and clinical scores. For such purpose, we will propose a new adapted tractography approach from Diffusion Tensor Imaging. Additionally, we will study non-linear machine learning approaches to express such correlation and validate it on clinical data sets. The resulting models will allow both prediction and understanding of clinical outcome and side effects.