Prospective Study Evaluating the EEG Recordings and Analysis in Parkinson's Patients: Towards Adaptive Deep Brain Stimulation by Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 30
- 试验地点
- 2
- 主要终点
- 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).
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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
结局指标
主要结局
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).
时间窗: 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.
次要结局
未报告次要终点