Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease.
Background: Deep brain stimulation (DBS) is a treatment option for Parkinson's disease patients when medication does not sufficiently manage their symptoms. DBS can be a highly effect therapy, but only after a time-consuming trial-and-error stimulation parameter adjustment process that is susceptibl...
| Published in: | Journal of NeuroEngineering & Rehabilitation (JNER) Vol. 18; no. 1; pp. 1 - 17 |
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| Main Authors: | , , , , , , , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
5/21/2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150429170&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150429170 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17430003 1CUC jtl: Journal of NeuroEngineering & Rehabilitation (JNER) issn: 17430003 maglogo: N pubinfo: dt: 5/21/2021 vid: 18 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 150429170 150429170 NLM34020662 150429170 10.1186/s12984-021-00873-9 NLM34020662 150429170 ppf: 1 ppct: 16 formats: tig: atl: Semi-automated approaches to optimize deep brain stimulation parameters in Parkinson's disease. aug: au: Louie, Kenneth H. Petrucci, Matthew N. Grado, Logan L. Lu, Chiahao Tuite, Paul J. Lamperski, Andrew G. MacKinnon, Colum D. Cooper, Scott E. Netoff, Theoden I. affil: Department of Biomedical Engineering, University of Minnesota, 312 Church St. SE, 55455, Minneapolis, MN, US sug: subj: Algorithms Parkinson Disease Therapy Deep Brain Stimulation Methods Probability Female Pilot Studies Middle Age Male Adult Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Middle Aged: 45-64 years Adult: 19-44 years Female Male ab: Background: Deep brain stimulation (DBS) is a treatment option for Parkinson's disease patients when medication does not sufficiently manage their symptoms. DBS can be a highly effect therapy, but only after a time-consuming trial-and-error stimulation parameter adjustment process that is susceptible to clinician bias. This trial-and-error process will be further prolonged with the introduction of segmented electrodes that are now commercially available. New approaches to optimizing a patient's stimulation parameters, that can also handle the increasing complexity of new electrode and stimulator designs, is needed.Methods: To improve DBS parameter programming, we explored two semi-automated optimization approaches: a Bayesian optimization (BayesOpt) algorithm to efficiently determine a patient's optimal stimulation parameter for minimizing rigidity, and a probit Gaussian process (pGP) to assess patient's preference. Quantified rigidity measurements were obtained using a robotic manipulandum in two participants over two visits. Rigidity was measured, in 5Hz increments, between 10-185Hz (total 30-36 frequencies) on the first visit and at eight BayesOpt algorithm-selected frequencies on the second visit. The participant was also asked their preference between the current and previous stimulation frequency. First, we compared the optimal frequency between visits with the participant's preferred frequency. Next, we evaluated the efficiency of the BayesOpt algorithm, comparing it to random and equal interval selection of frequency.Results: The BayesOpt algorithm estimated the optimal frequency to be the highest tolerable frequency, matching the optimal frequency found during the first visit. However, the participants' pGP models indicate a preference at frequencies between 70-110 Hz. Here the stimulation frequency is lowest that achieves nearly maximal suppression of rigidity. BayesOpt was efficient, estimating the rigidity response curve to stimulation that was almost indistinguishable when compared to the longer brute force method.Conclusions: These results provide preliminary evidence of the feasibility to use BayesOpt for determining the optimal frequency, while pGP patient's preferences include more difficult to measure outcomes. Both novel approaches can shorten DBS programming and can be expanded to include multiple symptoms and parameters. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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