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Completed

NCT Number: NCT05284526

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.

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Key information

Conditions

Parkinson Disease Basal Ganglia Diseases Brain Diseases Central Nervous System Diseases Movement Disorders Nervous System Diseases Neurodegenerative Diseases Parkinsonian Disorders Synucleinopathies

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

DEGOS

Bobigny, 93009, France

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements

Other

electroencephalogram (EEG) of control and parkinsonian subjects during the preparation and execution of movements

Primary outcomes

  1. 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).

    Time frame: 18 months

    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.

Sponsors and collaborators

Lead sponsor

Assistance Publique - Hôpitaux de Paris

Other

Collaborators

  • Institut National de la Santé Et de la Recherche Médicale, France

Registry information

Official study title

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

Acronym: ElectroPARK

Important dates

Study start
2022
Primary completion
2022
Study completion
2023
First posted
Mar 17, 2022
Registry last updated
Aug 23, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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