Premium
Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy
Author(s) -
Elias Gavin J. B.,
Boutet Alexandre,
Joel Suresh E.,
Germann Jürgen,
Gwun Dave,
Neudorfer Clemens,
Gramer Robert M.,
Algarni Musleh,
Paramanandam Vijayashankar,
Prasad Sreeram,
Beyn Michelle E.,
Horn Andreas,
Madhavan Radhika,
Ranjan Manish,
Lozano Christopher S.,
Kühn Andrea A.,
Ashe Jeff,
Kucharczyk Walter,
Munhoz Renato P.,
Giacobbe Peter,
Kennedy Sidney H.,
Woodside D. Blake,
Kalia Suneil K.,
Fasano Alfonso,
Hodaie Mojgan,
Lozano Andres M.
Publication year - 2021
Publication title -
annals of neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.764
H-Index - 296
eISSN - 1531-8249
pISSN - 0364-5134
DOI - 10.1002/ana.25975
Subject(s) - deep brain stimulation , magnetic resonance imaging , probabilistic logic , dystonia , cohort , movement disorders , medicine , neuroimaging , stimulation , depression (economics) , neuroscience , physical medicine and rehabilitation , psychology , parkinson's disease , disease , computer science , artificial intelligence , radiology , macroeconomics , economics
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 2021;89:426–443