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Email :
Address : MediCIS - LTSI UMR1099 INSERM
Faculté de Médecine, Université de Rennes 1
2 avenue du Prof. Léon Bernard
CS 34317
35043 Rennes cedex - France


Thesis subject

Data-driven methods to support decision making in Deep Brain Stimulation for Parkinson's Disease

Thesis abstract

Deep Brain Stimulation (DBS) is a successful and encouraging way of treating abnormal movement diseases, such as Parkinson’s Disease (PD). The success of the surgical procedure depends on many variables, most of which are derivative from a great number of modalities. Various problems gravitate throughout the care of the patient, from its screening, to the procedure itself and the stimulation follow-up, creating an urging need to develop computer assisting tools. In this thesis, we used data-driven methods to design two systems in order to address two concrete clinical applications. Firstly, we propose a tool able to assist clinicians in decision making for selecting patients and stimulation targets. It consists in a data-driven method which is able to predict the clinical outcomes (motor, neuropsychologic, cognitive etc.) of the surgery, from pre-operative multimodal biomarkers. Secondarily, we propose to greatly fasten the surgical procedure by automatizing the localization of the target nucleus via a real time treatment of the electrophysiological signal arising from the patient’s brain, from micro-electrode recordings (MER). Our method is able to accurately analyse the MER and tell if the electrode lead is inside the STN or not in one second, and does not require any parameter tuning nor calibration to work on a new data source.

inserm rennes1 ltsi