Can brain signals and anatomy refine contact choice for deep brain stimulation in Parkinson's disease?. Issue 12 (19th May 2022)

• Record Type: Journal Article
• Authors: Xu, San San; Lee, Wee-Lih; Perera, Thushara; Sinclair, Nicholas C; Bulluss, Kristian J; McDermott, Hugh J; Thevathasan, Wesley
• Is Part Of: Journal of neurology, neurosurgery and psychiatry. Volume 93:Issue 12(2022)
• Page Start: 1338
• Page End: 1341
• Publication Date: 2022-05-19
• Subjects: EVOKED POTENTIALS -- PARKINSON'S DISEASE -- ELECTRICAL STIMULATION -- NEUROSURGERY -- NEUROPHYSIOLOGY; Neurology -- Periodicals; Nervous system -- Surgery -- Periodicals; Psychiatry -- Periodicals; 616.8
• DOI: 10.1136/jnnp-2021-327708
• Languages: English
• ISSNs: 0022-3050

Abstract

Abstract : Introduction: Selecting the ideal contact to apply subthalamic nucleus deep brain stimulation (STN-DBS) in Parkinson's disease is time-consuming and reliant on clinical expertise. The aim of this cohort study was to assess whether neuronal signals (beta oscillations and evoked resonant neural activity (ERNA)), and the anatomical location of electrodes, can predict the contacts selected by long-term, expert-clinician programming of STN-DBS. Methods: We evaluated 92 hemispheres of 47 patients with Parkinson's disease receiving chronic monopolar and bipolar STN-DBS. At each contact, beta oscillations and ERNA were recorded intraoperatively, and anatomical locations were assessed. How these factors, alone and in combination, predicted the contacts clinically selected for chronic deep brain stimulation at 6 months postoperatively was evaluated using a simple-ranking method and machine learning algorithms. Results: The probability that each factor individually predicted the clinician-chosen contact was as follows: ERNA 80%, anatomy 67%, beta oscillations 50%. ERNA performed significantly better than anatomy and beta oscillations. Combining neuronal signal and anatomical data did not improve predictive performance. Conclusion: This work supports the development of probability-based algorithms using neuronal signals and anatomical data to assist programming of deep brain stimulation.