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Automated 3-Dimensional Brain Atlas Fitting to Microelectrode Recordings from Deep Brain Stimulation SurgeriesLuján J.L.a · Noecker A.M.a · Butson C.R.a · Cooper S.E.b · Walter B.L.b · Vitek J.L.b, c · McIntyre C.C.a, b
aDepartment of Biomedical Engineering, bCenter for Neurological Restoration, and cDepartment of Neurosciences, Cleveland Clinic Foundation, Cleveland, Ohio, USA Corresponding Author
Cameron C. McIntyre, PhD
Department of Biomedical Engineering, Cleveland Clinic Foundation
9500 Euclid Avenue ND20
Cleveland, OH 44195 (USA)
Tel. +1 216 445 3264, Fax +1 216 444 9198, E-Mail email@example.com
Objective: Deep brain stimulation (DBS) surgeries commonly rely on brain atlases and microelectrode recordings (MER) to help identify the target location for electrode implantation. We present an automated method for optimally fitting a 3-dimensional brain atlas to intraoperative MER and predicting a target DBS electrode location in stereotactic coordinates for the patient. Methods: We retrospectively fit a 3-dimensional brain atlas to MER points from 10 DBS surgeries targeting the subthalamic nucleus (STN). We used a constrained optimization algorithm to maximize the MER points correctly fitted (i.e., contained) within the appropriate atlas nuclei. We compared our optimization approach to conventional anterior commissure-posterior commissure (AC/PC) scaling, and to manual fits performed by four experts. A theoretical DBS electrode target location in the dorsal STN was customized to each patient as part of the fitting process and compared to the location of the clinically defined therapeutic stimulation contact. Results: The human expert and computer optimization fits achieved significantly better fits than the AC/PC scaling (80, 81, and 41% of correctly fitted MER, respectively). However, the optimization fits were performed in less time than the expert fits and converged to a single solution for each patient, eliminating interexpert variance. Conclusions and Significance: DBS therapeutic outcomes are directly related to electrode implantation accuracy. Our automated fitting techniques may aid in the surgical decision-making process by optimally integrating brain atlas and intraoperative neurophysiological data to provide a visual guide for target identification.
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