StimFit—A Data‐Driven Algorithm for Automated Deep Brain Stimulation Programming
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...
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| Published in | Movement disorders Vol. 37; no. 3; pp. 574 - 584 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.03.2022
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0885-3185 1531-8257 1531-8257 |
| DOI | 10.1002/mds.28878 |
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| 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 |
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| AbstractList | 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.BACKGROUNDFinding 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.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.OBJECTIVEWe 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.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.METHODSElectrode 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.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.RESULTSPredicted 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.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.CONCLUSIONWe 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. BackgroundFinding 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.ObjectiveWe 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.MethodsElectrode 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.ResultsPredicted 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.ConclusionWe 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 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 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. 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. 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. Predicted motor outcome correlated with observed outcome (R = 0.57, P < 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. 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. |
| Author | Butenko, Konstantin Roediger, Jan Wenzel, Gregor Horn, Andreas Dembek, Till A. Kühn, Andrea A. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34837245$$D View this record in MEDLINE/PubMed |
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| Copyright | 2021 The Authors. published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. 2021. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Keywords | DBS sweet spot DBS programming image-guided DBS subthalamic nucleus-deep brain stimulation |
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| Notes | J.R., T.A.D., G.W., and K.B. have nothing to disclose. A.A.K. declares that she is on the advisory board of Boston Scientific and Medtronic and has received honoraria from Boston Scientific, Medtronic, Zambon, and Stadapharm. A.H. has received grants from the German Research Council. Relevant conflicts of interest/financial disclosures Funding agencies This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—project ID 4247788381‐TRR 295 grant and under Germany's Excellence Strategy—EXC‐2049‐390688087. J.R. is a fellow of the Berlin Einstein Center for Neurosciences PhD program. T.A.D. was supported by the Cologne Clinician Scientist Program (CCSP)/Faculty of Medicine/University of Cologne. This study was funded by the German Research Foundation (DFG, FI 773/15‐1). Andrea A. Kühn and Andreas Horn contributed equally to this article. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and... Finding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and requires highly... BackgroundFinding the optimal deep brain stimulation (DBS) parameters from a multitude of possible combinations by trial and error is time consuming and... |
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| SubjectTerms | 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 |
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| Title | StimFit—A Data‐Driven Algorithm for Automated Deep Brain Stimulation Programming |
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