Semi-automated application for estimating subthalamic nucleus boundaries and optimal target selection for deep brain stimulation implantation surgery

OBJECTIVE Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become standard care for the surgical treatment of Parkinson’s disease (PD). Reliable interpretation of microelectrode recording (MER) data, used to guide DBS implantation surgery, requires expert electrophysiological evaluation. Recent efforts have endeavored to use electrophysiological signals for automatic detection of relevant brain structures and optimal implant target location. The authors conducted an observational case-control study to evaluate a software package implemented on an electrophysiological recording system to provide online objective estimates for entry into and exit from the STN. In addition, they evaluated the accuracy of the software in selecting electrode track and depth for DBS implantation into STN, which relied on detecting changes in spectrum activity. METHODS Data were retrospectively collected from 105 MER-guided STN-DBS surgeries (4 experienced neurosurgeons; 3 sites), in which estimates for entry into and exit from the STN, DBS track selection, and implant depth were compared post hoc between those determined by the software and those determined by the implanting neurosurgeon/neurophysiologist during surgery. RESULTS This multicenter study revealed submillimetric agreement between surgeon/neurophysiologist and software for entry into and exit out of the STN as well as optimal DBS implant depth. CONCLUSIONS The results of this study demonstrate that the software can reliably and accurately estimate entry into and exit from the STN and select the track corresponding to ultimate DBS implantation.