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

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

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.

Study Overview

• Status: Completed
• Conditions: Parkinson Disease
• Intervention / Treatment: Other: electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Actual): 30
• Phase: Not Applicable

Contacts and Locations

Study Locations

• France
  • Bobigny, France, 93009
    • DEGOS

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

Patients :

1. Patient over 18 years of age
2. Patient meeting the clinical diagnostic criteria for Parkinson's disease (Postuma et al, Mov Dis, 2015)
3. Signed consent to participate in the study
4. Absence of cognitive impairment (MoCA>24)
5. Affiliation to a French social security scheme

Healthy volunteer :

1. Healthy volunteer over 18 years of age
2. Signed consent to participate in the study
3. Absence of cognitive disorders (MoCA>24)
4. Affiliation to a French social security system

Exclusion Criteria:

Patients :

1. Patient refusal to participate
2. Pregnancy or breastfeeding in progress
3. Participation in another therapeutic interventional study
4. Patient under guardianship or curatorship
5. Person subject to a legal protection measure

Healthy volunteers :

1. Refusal of the healthy volunteer to participate
2. Pregnancy or breastfeeding in progress
3. Participation in another therapeutic interventional study.
4. Patient under guardianship or curatorship
5. Person subject to a legal protection measure

Study Plan

How is the study designed?

Design Details

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

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: parkinsonian patients with and without dopaminergic drug treatment; 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; electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements
Experimental: parkinsonian patients with and without High-frequency stimulation of the subthalamic nucleus; 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; electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements
Active Comparator: control subjects; 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; electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Assistance Publique - Hôpitaux de Paris
• Collaborators: Institut National de la Santé Et de la Recherche Médicale, France

Publications and helpful links

General Publications

• Ashkan K, Rogers P, Bergman H, Ughratdar I. Insights into the mechanisms of deep brain stimulation. Nat Rev Neurol. 2017 Sep;13(9):548-554. doi: 10.1038/nrneurol.2017.105. Epub 2017 Jul 28.
• Liao K, Xiao R, Gonzalez J, Ding L. Decoding individual finger movements from one hand using human EEG signals. PLoS One. 2014 Jan 8;9(1):e85192. doi: 10.1371/journal.pone.0085192. eCollection 2014.
• Limousin P, Pollak P, Benazzouz A, Hoffmann D, Le Bas JF, Broussolle E, Perret JE, Benabid AL. Effect of parkinsonian signs and symptoms of bilateral subthalamic nucleus stimulation. Lancet. 1995 Jan 14;345(8942):91-5. doi: 10.1016/s0140-6736(95)90062-4.

Study record dates

Study Major Dates

• Study Start (Actual): May 9, 2022
• Primary Completion (Actual): May 9, 2022
• Study Completion (Actual): April 21, 2023

Study Registration Dates

• First Submitted: February 17, 2022
• First Submitted That Met QC Criteria: March 9, 2022
• First Posted (Actual): March 17, 2022

Study Record Updates

• Last Update Posted (Actual): August 23, 2023
• Last Update Submitted That Met QC Criteria: August 21, 2023
• Last Verified: May 1, 2022

More Information

Terms related to this study

• Keywords: EEG; Montreal Cognitive Assessment; MDS-UPDRS III; MDS-UPDRS IV
• Additional Relevant MeSH Terms: Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Parkinsonian Disorders; Basal Ganglia Diseases; Movement Disorders; Synucleinopathies; Neurodegenerative Diseases; Parkinson Disease
• Other Study ID Numbers: APHP211327

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No