Peter M Lauro1,2, Shane Lee2,3, Minkyu Ahn4, Andrei Barborica5,6, Wael F Asaad2,3,7,8,9. 1. The Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA. 2. Department of Neuroscience, Brown University, Providence, Rhode Island, USA. 3. Brown Institute for Brain Science (BIBS), Brown University, Providence, Rhode Island, USA. 4. School of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea. 5. FHC Inc., Bowdoin, Maine, USA. 6. Physics Department, Bucharest University, Bucharest, Romania. 7. Department of Neurosurgery, The Warren Alpert Medical School, Providence, Rhode Island, USA. 8. Department of Neurosurgery, Rhode Island Hospital, Providence, Rhode Island, USA. 9. Norman Prince Neurosciences Institute, Lifespan, Providence, Rhode Island, USA.
Abstract
BACKGROUND/ OBJECTIVES: To create an open-source method for reconstructing microelectrode recording (MER) and deep brain stimulation (DBS) electrode coordinates along multiple parallel trajectories with patient-specific DBS implantation platforms to facilitate DBS research. METHODS: We combined the surgical geometry (extracted from WayPoint Planner), pre-/intra-/postoperative computed tomography (CT) and/or magnetic resonance (MR) images, and integrated them into the Analysis of Functional NeuroImages (AFNI) neuroimaging analysis environment using functions written in Python. Electrode coordinates were calculated from image-based electrode surfaces and recording trajectory depth values. Coordinates were translated into appropriate trajectories, and were tested for proximity to patient-specific or atlas-based anatomical structures. Final DBS electrode coordinates for 3 patient populations (ventral intermediate nucleus [VIM], subthalamic nucleus [STN], and globus pallidus pars interna [GPi]) were calculated. For STN cases, MER site coordinates were then analyzed to see whether they were inside or outside the STN. RESULTS: Final DBS electrode coordinates were described for VIM, STN, and GPi patient populations. 115/169 (68%) STN MER sites were within 1 mm of the STN in AFNI's Talairach and Tournoux (TT) atlas. CONCLUSIONS: DBStar is a robust tool kit for understanding the anatomical location and context of electrode locations, and can easily be used for imaging, behavioral, or electrophysiological analyses.
BACKGROUND/ OBJECTIVES: To create an open-source method for reconstructing microelectrode recording (MER) and deep brain stimulation (DBS) electrode coordinates along multiple parallel trajectories with patient-specific DBS implantation platforms to facilitate DBS research. METHODS: We combined the surgical geometry (extracted from WayPoint Planner), pre-/intra-/postoperative computed tomography (CT) and/or magnetic resonance (MR) images, and integrated them into the Analysis of Functional NeuroImages (AFNI) neuroimaging analysis environment using functions written in Python. Electrode coordinates were calculated from image-based electrode surfaces and recording trajectory depth values. Coordinates were translated into appropriate trajectories, and were tested for proximity to patient-specific or atlas-based anatomical structures. Final DBS electrode coordinates for 3 patient populations (ventral intermediate nucleus [VIM], subthalamic nucleus [STN], and globus pallidus pars interna [GPi]) were calculated. For STN cases, MER site coordinates were then analyzed to see whether they were inside or outside the STN. RESULTS: Final DBS electrode coordinates were described for VIM, STN, and GPipatient populations. 115/169 (68%) STN MER sites were within 1 mm of the STN in AFNI's Talairach and Tournoux (TT) atlas. CONCLUSIONS: DBStar is a robust tool kit for understanding the anatomical location and context of electrode locations, and can easily be used for imaging, behavioral, or electrophysiological analyses.
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