Investigators
Specific AIms
The project seeks to uncover neurophysiological signatures for Parkinson’s disease (PD) motor subtypes—tremor-dominant, postural instability and gait disturbance (PIGD), and akinetic/intermediate subtypes—to tailor DBS treatment to specific symptom profiles.
- Aim 1: Identify unique neurophysiological markers in local field potentials (LFP) for each PD subtype using AI models.
- Aim 2: Map local and network topography of neural activity to determine optimal DBS targets for symptom improvement in each subtype.
research strategy
This study utilizes intraoperative LFP recordings from DBS and neuroimaging in a large cohort of PD patients. AI will analyze frequency bands to classify motor subtypes, and imaging will map neural activity “hotspots” across brain networks to find optimal stimulation sites. This multimodal approach aims to improve DBS treatment by targeting specific symptoms through data-driven, subtype-specific methods.
collaborators
The team includes Dr. Johnson, Dr. de Hemptinne, and Dr. Wong, who contribute expertise in neurophysiology, AI, and DBS, combining clinical and computational insights to classify and treat PD motor subtypes effectively.
justification
Parkinson’s disease affects each patient differently, with diverse motor symptoms. Current DBS methods do not distinguish between subtypes, which limits treatment effectiveness. By identifying subtype-specific brain networks, this study could establish targeted DBS protocols, improving patient outcomes and advancing personalized medicine in Parkinson’s disease care.