EEG Recordings and Analysis in Parkinson's Patients: Towards Adaptive Deep Brain Stimulation by Machine Learning

Study Overview
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Brief Summary
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The objective of this protocol is to obtain on Parkinson's disease more accessible therapeutic targets than deep brain stimulation (HFS-STN), the neurosurgical treatment for this pathology. This study will pave the way for new forms of adaptive processing for the HFS-STN. It could become functionally coupled to a minimalist EEG centred on the motor cortex and to software for decoding, live or slightly delayed, classes of movements performed. On the one hand, this device could be used as a sensor of the quality of the information transmitted by the cortical network, thus allowing the selection of the optimal parameters of the HFS-STN on the basis of the movement decoding score. On the other hand, this device could lead to adapting the HFS-STN treatment over time by regularly calculating the recognition scores of the different movements performed and comparing them to the initial scores.

Detailed Description
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One of the therapies for Parkinson's disease, a condition affecting nearly 150,000 patients in France, is the invasive neurosurgical implantation of high-frequency deep brain stimulation of the subthalamic nuclei (HFS-STN). Although HFS-STN is very effective, the underlying mechanisms are still relatively poorly understood, particularly at the cortical level, a region that could become an alternative therapeutic target because it is easier to access. This study aims to measure the changes induced by the antiparkinsonian drug treatment and the HFS-STN on the encoding and transmission of motor information at the level of the motor cortex, thanks to the recording of the electroencephalogram of patients. These recordings, made during the performance of certain movements, will be subjected to an analysis using machine learning methods that will make it possible to decode the identity of the movement performed more or less efficiently.

Official Title
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Prospective Study Evaluating the EEG Recordings and Analysis in Parkinson's Patients: Towards Adaptive Deep Brain Stimulation by Machine Learning

Conditions
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Parkinson Disease

Intervention / Treatment
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* Other: electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients : Patient over 18 years of age Patient meeting the clinical diagnostic criteria for Parkinson's disease (Postuma et al, Mov Dis, 2015) Signed consent to participate in the study Absence of cognitive impairment (MoCA>24) Affiliation to a French social security scheme Healthy volunteer : Healthy volunteer over 18 years of age Signed consent to participate in the study Absence of cognitive disorders (MoCA>24) Affiliation to a French social security system Exclusion Criteria: Patients : Patient refusal to participate Pregnancy or breastfeeding in progress Participation in another therapeutic interventional study Patient under guardianship or curatorship Person subject to a legal protection measure Healthy volunteers : Refusal of the healthy volunteer to participate Pregnancy or breastfeeding in progress Participation in another therapeutic interventional study. Patient under guardianship or curatorship Person subject to a legal protection measure

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Non-Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: parkinsonian patients with and without dopaminergic drug treatment<br>Electroencephalogram (EEG) of 10 parkinsonian subjects with and without dopaminergic drug treatment during the preparation and execution of movements | Other: electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements<br>* electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements<br>|
| Experimental: parkinsonian patients with and without High-frequency stimulation of the subthalamic nucleus<br>Electroencephalogram (EEG) of 10 parkinsonian subjects with and without High-frequency stimulation (HFS) of the subthalamic nucleus (STN) during the preparation and execution of movements | Other: electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements<br>* electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements<br>|
| Active Comparator: control subjects<br>Electroencephalogram (EEG) of control subjects during the preparation and execution of movements | Other: electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements<br>* electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| measure the encoding capacity of the cortical networks of parkinsonian patients, with or without anti-parkinsonian drug treatment, and with or without High-frequency stimulation (HFS) of the subthalamic nucleus (STN). | comparison of the success scores of motion recognition, obtained by the decoding algorithms, which will highlight differences in the encoding and transmission capabilities of cortical information between the different experimental groups. | 18 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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EEG, Montreal Cognitive Assessment, MDS-UPDRS III, MDS-UPDRS IV