Towards guided and automated programming of subthalamic area stimulation in Parkinson's disease. Issue 1 (13th January 2022)

• Record Type: Journal Article
• Authors: Xu, San San; Sinclair, Nicholas C.; Bulluss, Kristian J.; Perera, Thushara; Lee, Wee-Lih; McDermott, Hugh J.; Thevathasan, Wesley
• Is Part Of: Brain communications. Volume 4:Issue 1(2022)
• Publication Date: 2022-01-13
• Subjects: deep brain stimulation -- Parkinson's disease -- subthalamic nucleus -- evoked potentials -- local field potentials; 616
• DOI: 10.1093/braincomms/fcac003
• Languages: English
• ISSNs: 2632-1297

Abstract

Abstract: Selecting the ideal contact to apply subthalamic nucleus deep brain stimulation in Parkinson's disease can be an arduous process, with outcomes highly dependent on clinician expertise. This study aims to assess whether neuronal signals recorded intraoperatively in awake patients, and the anatomical location of contacts, can assist programming. In a cohort of 14 patients with Parkinson's disease, implanted with subthalamic nucleus deep brain stimulation, the four contacts on each lead in the 28 hemispheres were ranked according to proximity to a nominated ideal anatomical location and power of the following neuronal signals: evoked resonant neural activity, beta oscillations and high-frequency oscillations. We assessed how these rankings predicted, on each lead: (i) the motor benefit from deep brain stimulation applied through each contact and (ii) the 'ideal' contact to apply deep brain stimulation. The ranking of contacts according to each factor predicted motor benefit from subthalamic nucleus deep brain stimulation, as follows: evoked resonant neural activity; r 2 = 0.50, Akaike information criterion 1039.9, beta; r 2 = 0.50, Akaike information criterion 1041.6, high-frequency oscillations; r 2 = 0.44, Akaike information criterion 1057.2 and anatomy; r 2 = 0.49, Akaike information criterion 1048.0. Combining evoked resonant neural activity, beta and high-frequency oscillations ranking data yielded the strongest predictive model ( r 2 = 0.61, Akaike information criterion 1021.5). The 'ideal' contact (yielding maximal benefit) was ranked first according to each factor in the following proportion of hemispheres; evoked resonant neural activity 18/28, beta 17/28, anatomy 16/28, high-frequency oscillations 7/28. Across hemispheres, the maximal available deep brain stimulation benefit did not differ from that yielded by contacts chosen by clinicians for chronic therapy or contacts ranked first according to evoked resonant neural activity. Evoked resonant neural activity, beta oscillations and anatomy similarly predicted how motor benefit from subthalamic nucleus deep brain stimulation varied across contacts on each lead. This could assist programming by providing a probability ranking of contacts akin to a 'monopolar survey'. However, these factors identified the 'ideal' contact in only a proportion of hemispheres. More advanced signal processing and anatomical techniques may be needed for the full automation of contact selection. Abstract : Xu et al . report that structural anatomy, beta oscillations, evoked resonant neural activity and high-frequency oscillations predict motor response to subthalamic nucleus stimulation in Parkinson's disease. The predictive model improves with combining neuronal signal information. This work supports the use of objective data to assist programming. Graphical Abstract: Graphical Abstract