StimFit—A Data‐Driven Algorithm for Automated Deep Brain Stimulation Programming

• Published in: Movement disorders Vol. 37; no. 3; pp. 574 - 584
• Main Authors: Roediger, Jan, Dembek, Till A., Wenzel, Gregor, Butenko, Konstantin, Kühn, Andrea A., Horn, Andreas
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; Automation; DBS programming; DBS sweet spot; Deep Brain Stimulation; Electrical stimuli; Humans; image‐guided DBS; Medical personnel; Movement disorders; Neurodegenerative diseases; Neuroimaging; Parkinson Disease - therapy; Parkinson's disease; Patients; Prediction models; Solitary tract nucleus; Subthalamic nucleus; Subthalamic Nucleus - physiology; subthalamic nucleus‐deep brain stimulation; Treatment Outcome; DBS sweet spot; DBS programming; image-guided DBS; subthalamic nucleus-deep brain stimulation
• ISSN: 0885-3185; 1531-8257; 1531-8257
• DOI: 10.1002/mds.28878

Background Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly trained medical personnel. Objective We developed an automated algorithm to identify optimal stimulation settings in Parkinson's disease (PD) patients treated with subthalamic nucleus (STN) DBS based on imaging‐derived metrics. Methods Electrode locations and monopolar review data of 612 stimulation settings acquired from 31 PD patients were used to train a predictive model for therapeutic and adverse stimulation effects. Model performance was then evaluated within the training cohort using cross‐validation and on an independent cohort of 19 patients. We inverted the model by applying a brute‐force approach to determine the optimal stimulation sites in the target region. Finally, an optimization algorithm was established to identify optimal stimulation parameters. Suggested stimulation parameters were compared to the ones applied in clinical practice. Results Predicted motor outcome correlated with observed outcome (R = 0.57, P < 10−10) across patients within the training cohort. In the test cohort, the model explained 28% of the variance in motor outcome differences between settings. The stimulation site for maximum motor improvement was located at the dorsolateral border of the STN. When compared to two empirical settings, model‐based suggestions more closely matched the setting with superior motor improvement. Conclusion We developed and validated a data‐driven model that can suggest stimulation parameters leading to optimal motor improvement while minimizing the risk of stimulation‐induced side effects. This approach might provide guidance for DBS programming in the future. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society