Optimized Programming Algorithm for Cylindrical and Directionally Segmented Deep Brain Stimulation Electrodes
Daria Nesterovich Anderson1, Braxton Osting, PhD1, Johannes Vorwerk, PhD1, Alan “Chuck” Dorval, PhD1, Christopher R. Butson, PhD1

1University of Utah, Salt Lake City, UT 84112

Introduction: Deep brain stimulation (DBS) programming is a complex process likely to become more complex with the introduction of leads with larger numbers of contacts. We developed an automated programming algorithm to optimize DBS parameter selection for targeted neural activation in a patient-specific manner. The purpose of this study is to assess algorithm performance by applying it to conventional and directional electrode geometries.

Methods: We used finite element models to solve the bioelectric field problem for a conventional 4-contact DBS lead (Medtronic 3389) and three directional leads: the directSTNacute1, and the Medtronic-Sapiens electrode2. We used values derived from the Hessian matrix of voltage second derivatives as an estimate of neural activation to maximize stimulation in the STN and limit activation of axon fibers in the internal capsule.

Results: We have demonstrated an ability to program the electrode to stimulate a target area while avoiding neural tracts that may be responsible for side effects. We tested that the algorithm settings were robust by adjusting the magnitude of optimized electrode settings. Optimal parameter settings from the algorithm show STN activation while limiting stimulation outside the target area and the internal capsule.

Conclusion: We have developed a method of optimization that can be applied to the clinical electrode and additional complex DBS lead designs. Deviation away from the optimized parameters showed more stimulation outside the target area or activation of neighboring fiber tracts. A real-time, patient-specific, automated programming algorithm may increase efficiency and positive outcomes of clinical DBS programming, as well as enable the use of more complex lead designs, which are likely to be too complex for manual programming.

Acknowledgements: This work is supported by the NSF under Grant No. 10037840, the NSF CAREER Award under Grand No. 1351112, and the NSF Graduate Research Fellowship under Grant No. 1256065.


[1] Pollo, C. Brain. 2014. p.awu102

[2] Decré, M.M.J. 2013. EP2559454 A1.

[3] Xu W. Experimental neurology. 2011. 228(2): 294-7.