study rationale
Deep brain stimulation (DBS) in the globus pallidus internus (GPi) is effective for treating primary idiopathic dystonia, yet identifying optimal stimulation parameters remains challenging and time-consuming. This study aims to simplify DBS programming by using evoked resonant neural activity (ERNA), a high-frequency oscillation linked to therapeutic effects, to guide parameter selection, thus enhancing treatment efficiency and outcomes for dystonia patients.
hypothesis
ERNA may serve as a biomarker to optimize DBS programming in dystonia. The study hypothesizes that ERNA amplitude and frequency can be tuned to specific stimulation parameters, enabling the development of an automated algorithm to identify ideal DBS settings.
study design
Aim 1: Map the spatial distribution of GPi ERNA in dystonia patients using neuroimaging and DBS contact heatmaps.
Aim 2: Analyze the effects of different DBS stimulation parameters on ERNA characteristics.
Aim 3: Develop an AI-based algorithm to predict optimal DBS settings using ERNA features, with validation against clinically optimized parameters at six months post-surgery
impact on dystonia treatment
This approach seeks to streamline DBS programming, reducing time and subjective decision-making in parameter selection, ultimately improving patient outcomes and quality of life. Automated parameter selection could provide faster and more precise symptom relief.
next steps for development
Results from this pilot study will provide foundational data for a larger NIH R01 grant application. Future work may extend this AI-driven approach to optimize DBS in other disorders treated with GPi DBS, such as Parkinson’s disease and Tourette syndrome.
additional information
A total budget of $38,438 has been requested to cover salaries for Dr. Johnson, a research assistant, and publication fees. This funding supports critical data collection, analysis, and open-access publication to facilitate broader dissemination.
collaboration
The study team includes Dr. Kara Johnson, a postdoctoral associate specializing in neurophysiology and computational models; Dr. Joshua Wong, a neurologist with expertise in DBS and AI applications; and Dr. de Hemptinne, who oversees neurophysiological data analysis and contributes to manuscript preparation. This collaboration combines expertise in AI, neurophysiology, and DBS, driving innovation in automated, patient-specific DBS programming for dystonia