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 journalArticle

3 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

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Keywords

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

PubMed: MeSH publication types

  • Journal Article
  • Validation Study

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