Defining the border of the subthalamic nucleus for deep brain stimulation: A proposed model using the symmetrical sigmoid curve function

Background: The subthalamic nucleus (STN) is an important target during deep brain stimulation (DBS). Accurate lead placement is integral to achieving satisfactory clinical outcomes; however, the STN remains a structure whose visualization is highly variable with borders often difficult to define. We aimed to develop an objective method of evaluating the visibility of the STN on preoperative magnetic resonance imaging (MRI) to standardize future comparative assessments between imaging protocols and patient-specific parameters. Methods: An imaging study of 64 prospectively collected patients undergoing bilateral DBS of the STN for various movement disorders was performed with institutional approval. MRI scans were acquired using a uniform protocol involving general anesthesia, cranial fixation in a Leksell stereotactic frame, and long acquisition times using a 3T MRI scanner. The images were analyzed using the iPlan Stereotaxy, version 2.6, workstation. High-resolution T2-weighted axial sections were evaluated, and the voxel values in the region of the presumed posterior border of the STN (as defined by the operating neurosurgeon) were obtained. A 4-parameter logistic symmetrical sigmoid curve was used to map the voxel values as they progressed from within to outside the region of the STN border. The inflection point and Hill coefficient of this symmetrical curve was calculated to provide objective information on the location and clarity of the STN border, respectively. These findings were compared with the surgeon\u27s judgment of the STN border. To demonstrate the use of the sigmoid curve, the patients\u27 head volumes were also calculated and evaluated to assess whether larger head volumes adversely affected STN visibility. Results: The symmetrical sigmoid curve model provided objective information on the visibility of the STN on T2-weighted MRI scans and could be generated in 86% of the patients. The other 14% of patients had MRI scans that generated linear graphs, indicating the poorest scoring for STN image quality. No correlation between head volume and STN visibility was identified. Conclusions: Our proposed statistical model allows for standardized examination of the visibility of the STN border for DBS and has potential for both clinical and academic applications