Category: Parkinson's Disease: Surgical Therapy
Objective: To test the feasibility and performance of algorithmic image-guided programming for deep brain stimulation (DBS) using a digital health technology, Quantitative Digitography (QDG), as an assessment tool.
Background: Optimization of DBS programming in Parkinson’s disease (PD) can be time-consuming, often requiring multiple prolonged clinical visits. Brain imaging and automated DBS programming algorithms can potentially reduce programming time and improve clinical efficacy. The algorithm provides stimulation configurations that maximize overlap of the volume of tissue activated with the target region of interest (ROI) and minimize stimulation of regions of avoidance (ROA), leveraging patient-specific imaging and directional leads [1]. QDG provides validated, quantitative metrics for all motor symptoms of PD in real time from 30 seconds of repetitive alternative finger presses on adjacent tensioned engineered levers of a digitography device (KeyDuo) [2]. A validated algorithm detects overall tremor severity and removes those strikes when calculating a composite Mobility Score.
Method: Inclusion criteria: people with clinically established PD implanted with DBS systems in STN or GPi. Algorithm-guided programming (AGP) was conducted using DBS lead location reconstruction from preoperative neuroimaging. ROI and ROAs were selected along with weighting ratios to determine the priority of stimulation regions. Participants performed the QDG mobility task on the KeyDuo OFF DBS before and ON DBS after AGP.
Results: Preliminary results showed the QDG Mobility Score after AGP was higher than OFF DBS for 3/3 people evaluated with bilateral STN DBS, indicating alleviated motor symptoms by AGP DBS settings [figure1]. One person with bilateral GPi leads was tested at 40% of the AGP suggested current and did not show improvement. For participants with tremor, QDG Tremor Severity Score decreased after AGP compared to OFF DBS. Other QDG metrics corresponding to bradykinesia, rigidity, and gait impairment improved from OFF DBS to after AGP.
Conclusion: Results from this study indicate that AGP led to enhanced motor performance, evident by improved QDG metrics in comparison to OFF DBS. This study highlights the potential of applying AGP in clinical practice, which can reduce burden on clinicians and increase accessibility of DBS programming to other providers.
Figure 1
References: 1. Malekmohammadi M, Mustakos R, Sheth S, et al. Automated optimization of deep brain stimulation parameters for modulating neuroimaging-based targets. J Neural Eng. 2022 Jul 20;19(4):10.1088/1741-2552/ac7e6c. doi: 10.1088/1741-2552/ac7e6c.
2. Wilkins KB, Petrucci MN, Kehnemouyi Y, et al. Quantitative Digitography Measures Motor Symptoms and Disease Progression in Parkinson’s Disease. J Parkinsons Dis. 2022;12(6):1979-1990. doi: 10.3233/JPD-223264. PMID: 35694934; PMCID: PMC9535590.
To cite this abstract in AMA style:
P. Acharyya, K. Daley, C. Casselton, A. Abay, A. Negi, S. Karjagi, K. Wilkins, M. Ferris, H. Bronte-Stewart. Digital Health Technology Demonstrates Efficacy of Algorithmic DBS Programming [abstract]. Mov Disord. 2025; 40 (suppl 1). https://www.mdsabstracts.org/abstract/digital-health-technology-demonstrates-efficacy-of-algorithmic-dbs-programming/. Accessed October 5, 2025.« Back to 2025 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/digital-health-technology-demonstrates-efficacy-of-algorithmic-dbs-programming/