Identifying Local Field Potential Biomarkers for Obsessive-compulsive Disorder Treatment With Deep Brain Stimulation
NCT06542224 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 16
Last updated 2024-08-07
Summary
Obsessive-compulsive disorder (OCD) is a complex and severe mental illness characterized by multiple symptoms and is considered a leading cause of non-fatal health loss. However, nearly 20% of patients do not respond to standard pharmacological or psychological treatments. Currently, we lack objective brain-based biomarkers. To address this issue, we used a novel device for electrophysiology recording and applied deep brain stimulation (DBS) to 16 OCD patients. In this study, we aim to use long-term invasive neural signal collection and machine learning techniques to reveal the complex relationship between these signals and OCD symptoms. By applying advanced machine learning algorithms, our goal is to establish highly accurate prediction models to identify biomarkers associated with the occurrence and progression of OCD. The research will focus on the spatiotemporal features of neural signals and build personalized OCD decoding models based on individual differences through the integration and analysis of large-scale data. By delving into the information contained in neural signals, we hope to provide academic and practical innovations for the development of personalized treatment approaches for OCD.
Conditions
- Obsessive-Compulsive Disorder
Interventions
- PROCEDURE
-
Deep Brain stimulation
All subjects will receive bilateral surgical implantation of DBS system.
Sponsors & Collaborators
-
Beijing Pins Medical Co., Ltd
collaborator INDUSTRY -
West China Hospital
lead OTHER
Study Design
- Allocation
- NA
- Purpose
- TREATMENT
- Masking
- NONE
- Model
- SINGLE_GROUP
Eligibility
- Min Age
- 18 Years
- Max Age
- 65 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2024-08-03
- Primary Completion
- 2026-07-03
- Completion
- 2026-12-03
Countries
- China
Study Locations
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