Computer-Guided Deep Brain Stimulation Programming for Parkinson's Disease

Abstract

Objective: Pilot study to evaluate computer-guided deep brain stimulation (DBS) programming designed to optimize stimulation settings using objective motion sensor-based motor assessments.

Materials and methods: Seven subjects (five males; 54-71 years) with Parkinson's disease (PD) and recently implanted DBS systems participated in this pilot study. Within two months of lead implantation, the subject returned to the clinic to undergo computer-guided programming and parameter selection. A motion sensor was placed on the index finger of the more affected hand. Software guided a monopolar survey during which monopolar stimulation on each contact was iteratively increased followed by an automated assessment of tremor and bradykinesia. After completing assessments at each setting, a software algorithm determined stimulation settings designed to minimize symptom severities, side effects, and battery usage.

Results: Optimal DBS settings were chosen based on average severity of motor symptoms measured by the motion sensor. Settings chosen by the software algorithm identified a therapeutic window and improved tremor and bradykinesia by an average of 35.7% compared with baseline in the "off" state (p < 0.01).

Conclusions: Motion sensor-based computer-guided DBS programming identified stimulation parameters that significantly improved tremor and bradykinesia with minimal clinician involvement. Automated motion sensor-based mapping is worthy of further investigation and may one day serve to extend programming to populations without access to specialized DBS centers.

Keywords: Deep brain stimulation (DBS); Parkinson's disease; motion sensing; objective measures; programming strategies.

Conflict of interest statement

Conflict of Interest: Dr. Heldman, Dr. Pulliam, and Dr. Giuffrida have received compensation from Great Lakes NeuroTechnologies for employment. Dr. Urrea Mendoza, Ms. Gartner, Dr. Montgomery, Dr. Espay, and Dr. Revilla have received compensation from Great Lakes NeuroTechnologies for consulting. Great Lakes NeuroTechnologies both hold and has submitted patent applications related to this work.

© 2015 International Neuromodulation Society.

Figures

Figure 1. Automated programming algorithm

This block diagram illustrates the algorithm used to guide programming. PA, Pulse amplitude; F, frequency; PW, pulse width.

Figure 2. Parameter Space Search Algorithm

The software chose IPG settings that minimized average severity of all symptoms. If symptoms were ameliorated equally at multiple settings, the software selected the set of settings with the lowest stimulation amplitude, pulse width, and frequency.

Figure 3. Symptom Response Map

A) For Subject 7, motor symptoms severity scores based on the motion sensor-based assessments are plotted as a function of contact and stimulation amplitude. The color corresponds to symptom severity (0, normal; 4 most severe). The four columns for each contact correspond to tremor (t), finger tapping speed (s), finger tapping amplitude (a), and finger tapping rhythm (r). Settings with white triangles in the top left indicate the presence of persistent side effects. The black box indicates the optimal settings chosen by the algorithm and the brackets indicate a therapeutic window. B) Scores for the four motor symptoms shown in (A) are averaged and converted to percent change from baseline.