A retrospective evaluation of automated optimization of deep brain stimulation parameters

Abstract

Background: The automatic determination of optimal deep brain stimulation (DBS) parameters is of great importance to improve the stimulation control of the classic quadripolar and the increasingly popular segmented DBS leads. Several algorithms to fulfill this goal have been presented recently, but few data exist validating the computationally predicted settings against patient data. Objective: We perform a retrospective analysis of a proposed optimization algorithm to show that its application in practice has the potential to lead to an equal or better stimulation of the target region as manual programming, while reducing the time effort for programming sessions. Methods: For three patients (five electrodes) diagnosed with essential tremor, we derived the voltage thresholds at which side effects occur for each contact during the initial programming session. Based on these voltage thresholds, we automatically derived optimal stimulation settings using an approach that can be applied in clinical practice. Results: We observe a high degree of agreement between the findings of manual programming and the results of the optimization algorithm, with the algorithm predicting a clinically utilized electrode contact in 80% of the cases. Additionally, the optimized stimulation settings lead to the activation of an equal or larger volume fraction of the target compared to the manually determined settings in all cases. Conclusions: Our results validate the automatic determination of optimal DBS settings against patient data, as we find that the optimization results reliably agree with the manually determined optimal stimulation settings. Thereby, we * Corresponding author Email addresses: jvorwerk@sci.utah.edu (Johannes Vorwerk), butson@sci.utah.edu (Christopher R. Butson ) All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/393900 doi: bioRxiv preprint first posted online Aug. 17, 2018; demonstrate the feasibility and benefit of applying this algorithm in clinical practice.

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