Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery

Reuben R. Shamir, Yuval Duchin, Jinyoung Kim, Remi Patriat, Odeya Marmor, Hagai Bergman, Jerrold L Vitek, Guillermo Sapiro, Atira Bick, Ruth Eliahou, Renana Eitan, Zvi Israel, Noam Harel

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for success of this therapy, precise identification of the STN based on imaging can be challenging. We developed a method to accurately visualize the STN on a standard clinical magnetic resonance imaging (MRI). The method incorporates a database of 7-Tesla (T) MRIs of PD patients together with machine-learning methods (hereafter 7 T-ML). OBJECTIVE: To validate the clinical application accuracy of the 7 T-ML method by comparing it with identification of the STN based on intraoperative microelectrode recordings. METHODS: Sixteen PD patients who underwent microelectrode-recordings guided STN DBS were included in this study (30 implanted leads and electrode trajectories). The length of the STN along the electrode trajectory and the position of its contacts to dorsal, inside, or ventral to the STN were compared using microelectrode-recordings and the 7 T-ML method computed based on the patient's clinical 3T MRI. RESULTS: All 30 electrode trajectories that intersected the STN based on microelectrode recordings, also intersected it when visualized with the 7 T-ML method. STN trajectory average length was 6.2 ± 0.7 mm based on microelectrode recordings and 5.8 ± 0.9 mm for the 7 T-ML method. We observed a 93% agreement regarding contact location between the microelectrode-recordings and the 7 T-ML method. CONCLUSION: The 7 T-ML method is highly consistent with microelectrode-recordings data. This method provides a reliable and accurate patient-specific prediction for targeting the STN.

Original languageEnglish (US)
Pages (from-to)749-756
Number of pages8
JournalClinical Neurosurgery
Volume84
Issue number3
DOIs
StatePublished - Mar 1 2019

Bibliographical note

Funding Information:
This study was partially supported by the NIH R01-NS085188; P41 EB015894; P30 NS076408 and the University of Minnesota Udall center P50NS098573. Additional support from NSF and the Department of Defense is acknowledged. Dr Shamir, Mr Duchin, and Dr Kim are employees of Surgical Information Sciences Inc. Dr Patriat is a consultant for Surgical Information Sciences. Drs Vitek, Sapiro and Harel are shareholders of Surgical Information Sciences Inc. The other authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.

Publisher Copyright:
© 2018 by the Congress of Neurological Surgeons.

Keywords

  • Deep brain stimulation
  • High-field MRI
  • Image-based targeting
  • Machine learning
  • Microelectrode recordings
  • Subthalamic nucleus
  • Surgical planning
  • Validation

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