Operative neurosurgery (Hagerstown, Md.) · 2025
Characterization of Subthalamic Nucleus Boundary and Trajectory Recommendations From a Commercially Available Microelectrode Recording Algorithm During Deep Brain Stimulation Surgery for Parkinson Disease.
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Roy François D, Afsharipour Babak, King Aleksandra, Waldron Michelle, Ba Fang, Shetty Aakash, Sankar Tejas
Аннотация
BACKGROUND AND OBJECTIVES: Microelectrode recordings (MER) within the subthalamic nucleus (STN) are routinely performed during deep brain stimulation (DBS) surgery for Parkinson disease. Commercially available algorithms have been developed to detect STN boundaries and recommend an optimal DBS lead trajectory based on MER data. We aimed to characterize the variance of a broadly used algorithm's STN border estimates and trajectory recommendations.
METHODS: MER data from 37 STN-DBS implants in 21 patients were analyzed offline using a semiautomated algorithm making use of oscillatory activity in MER data (HaGuide, Alpha Omega). Software recommendations were computed using the default STN settings across 3 different 'Site Sizes' and 2 'Waiting Times'. For each of the 6 trials, values for the STN Entrance, STN dorsolateral oscillatory region Exit, STN Exit, STN Length, dorsolateral oscillatory region ratio (%), Stimulation Depth, and trajectory recommendations were analyzed.
RESULTS: Even with different input parameters, the algorithm's estimates of STN Exit and STN Entrance within the chosen trajectory had low intrasubject variability and were highly correlated with the depth of the final DBS lead as chosen by the clinical team (STN Exit: r = 0.86 and STN Entrance: r = 0.70; both P < .001). However, the algorithm's trajectory recommendations were more sensitive to input parameters, with the algorithm recommending more than 1 trajectory in 42% of implants.
CONCLUSION: Semiautomated identification of STN boundaries by a commonly used algorithm is relatively less sensitive to algorithm input parameters and well-correlated with final STN-DBS lead depth as determined by an expert surgical team. However, algorithm-generated optimal trajectory recommendations are more strongly influenced by input parameters and should be interpreted with more caution during DBS implantation.
METHODS: MER data from 37 STN-DBS implants in 21 patients were analyzed offline using a semiautomated algorithm making use of oscillatory activity in MER data (HaGuide, Alpha Omega). Software recommendations were computed using the default STN settings across 3 different 'Site Sizes' and 2 'Waiting Times'. For each of the 6 trials, values for the STN Entrance, STN dorsolateral oscillatory region Exit, STN Exit, STN Length, dorsolateral oscillatory region ratio (%), Stimulation Depth, and trajectory recommendations were analyzed.
RESULTS: Even with different input parameters, the algorithm's estimates of STN Exit and STN Entrance within the chosen trajectory had low intrasubject variability and were highly correlated with the depth of the final DBS lead as chosen by the clinical team (STN Exit: r = 0.86 and STN Entrance: r = 0.70; both P < .001). However, the algorithm's trajectory recommendations were more sensitive to input parameters, with the algorithm recommending more than 1 trajectory in 42% of implants.
CONCLUSION: Semiautomated identification of STN boundaries by a commonly used algorithm is relatively less sensitive to algorithm input parameters and well-correlated with final STN-DBS lead depth as determined by an expert surgical team. However, algorithm-generated optimal trajectory recommendations are more strongly influenced by input parameters and should be interpreted with more caution during DBS implantation.