Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy

Deep brain stimulation (DBS) depends on precise delivery of electrical current to target tissues. However, the specific brain structures responsible for best outcome are still debated. We applied probabilistic stimulation mapping to a retrospective, multidisorder DBS dataset assembled over 15 years at our institution (n(total) = 482 patients; n(Parkinson disease) = 303; n(dystonia) = 64; n(tremor) = 39; n(treatment-resistant depression/anorexia nervosa) = 76) to identify the neuroanatomical substrates of optimal clinical response. Using high-resolution structural magnetic resonance imaging and activation volume modeling, probabilistic stimulation maps (PSMs) that delineated areas of above-mean and below-mean response for each patient cohort were generated and defined in terms of their relationships with surrounding anatomical structures. Our results show that overlap between PSMs and individual patients' activation volumes can serve as a guide to predict clinical outcomes, but that this is not the sole determinant of response. In the future, individualized models that incorporate advancements in mapping techniques with patient-specific clinical variables will likely contribute to the optimization of DBS target selection and improved outcomes for patients. ANN NEUROL 2020

Automated summary

Deep brain stimulation (DBS) relies on precise delivery of electrical current to target tissues, with the identification of neuroanatomical substrates playing a key role in predicting clinical outcomes. Probabilistic stimulation maps (PSMs) can guide the prediction of clinical responses by overlapping with individual patients' activation volumes. Future advancements in individualized models incorporating mapping techniques and patient-specific clinical variables are likely to enhance DBS target selection and improve patient outcomes.