Real-time machine learning classification of pallidal borders during deep brain stimulation surgery.

Journal: Journal of neural engineering
PMID: 31675740

Abstract

OBJECTIVE: Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease and dystonia improves motor symptoms and quality of life. Traditionally, pallidal borders have been demarcated by electrophysiological microelectrode recordings (MERs) during DBS surgery. However, detection of pallidal borders can be challenging due to the variability of the firing characteristics of neurons encountered along the trajectory. MER can also be time-consuming and therefore costly. Here we show the feasibility of real-time machine learning classification of striato-pallidal borders to assist neurosurgeons during DBS surgery.

Authors

  • Dan Valsky
    The Edmond and Lily Safra Center for Brain Research (ELSC), The Hebrew University, Jerusalem, Israel.
  • Kim T Blackwell
    Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, USA.
  • Idit Tamir
  • Renana Eitan
    Department of Psychiatry, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.
  • Hagai Bergman
    The Edmond and Lily Safra Center for Brain Research (ELSC), The Hebrew University, Jerusalem, Israel.
  • Zvi Israel
    Center for Functional & Restorative Neurosurgery, Department of Neurosurgery, Hadassah-Hebrew University Medical Center, Jerusalem, Israel.

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