StimFit-A Data-Driven Algorithm for Automated Deep Brain Stimulation Programming

Jan Roediger; Till A Dembek; Gregor Wenzel; Konstantin Butenko; Andrea A Kühn; Andreas Horn

doi:10.1002/mds.28878

StimFit-A Data-Driven Algorithm for Automated Deep Brain Stimulation Programming

Mov Disord. 2022 Mar;37(3):574-584. doi: 10.1002/mds.28878. Epub 2021 Nov 27.

Authors

Jan Roediger^{1

2}, Till A Dembek³, Gregor Wenzel¹, Konstantin Butenko⁴, Andrea A Kühn^{1

5

6

7}, Andreas Horn¹

Affiliations

¹ Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany.
² Einstein Center for Neurosciences Berlin, Charité University Medicine Berlin, Charitéplatz 1, Berlin, 10117, Germany.
³ Department of Neurology, Faculty of Medicine, University of Cologne, Cologne, Germany.
⁴ Institute of General Electrical Engineering, University of Rostock, Rostock, Germany.
⁵ Berlin School of Mind and Brain, Charité University Medicine, Berlin, Germany.
⁶ NeuroCure Clinical Research Centre, Charité University Medicine, Berlin, Germany.
⁷ DZNE, German Center for Degenerative Diseases, Berlin, Germany.

PMID: 34837245
DOI: 10.1002/mds.28878

Abstract

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.

Keywords: DBS programming; DBS sweet spot; image-guided DBS; subthalamic nucleus-deep brain stimulation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Deep Brain Stimulation*
Humans
Parkinson Disease* / therapy
Subthalamic Nucleus* / physiology
Treatment Outcome