AUTHOR=Sand Daniel , Arkadir David , Abu Snineh Muneer , Marmor Odeya , Israel Zvi , Bergman Hagai , Hassin-Baer Sharon , Israeli-Korn Simon , Peremen Ziv , Geva Amir B. , Eitan Renana 

TITLE=Deep Brain Stimulation Can Differentiate Subregions of the Human Subthalamic Nucleus Area by EEG Biomarkers

JOURNAL=Frontiers in Systems Neuroscience

VOLUME=Volume 15 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/systems-neuroscience/articles/10.3389/fnsys.2021.747681

DOI=10.3389/fnsys.2021.747681

ISSN=1662-5137

ABSTRACT=Introduction
Precise lead localization is crucial for optimal clinical outcome of subthalamic nucleus (STN) deep-brain stimulation (DBS) treatment in patients with Parkinson’s disease (PD). Currently, anatomical measures as well as invasive intraoperative electrophysiological recordings are used to locate DBS electrodes. The objective of this study was to find an alternative electrophysiology tool for STN DBS lead localization.

Methods
Sixty-one postoperative electrophysiology recording sessions were obtained from 17 DBS-treated PD patients. An intraoperative physiological method automatically detected STN borders and sub-regions. Postoperative EEG cortical activity was measured while STN low frequency stimulation (LFS) was applied to different areas inside and outside the STN. Machine-learning models were used to differentiate the stimulation locations, based on EEG analysis of engineered features.

Results 
A machine-learning algorithm identified the top 25 evoked response potentials (ERPs), engineered features that can differentiate inside and outside STN stimulation locations as well as within STN stimulation locations. Evoked responses at the medial and ipsilateral fronto-central areas were found to be most significant for predicting the location of STN stimulation. Two-class linear support vector machine (SVM) predicted the inside (dorso-lateral region (DLR) and ventro-medial region (VMR)) vs. outside (Zona Incerta (ZI) STN stimulation classification with accuracy of 0.98 and 0.82 for ZI vs. VMR and ZI vs. DLR, respectively, and accuracy of 0.77 for the within STN (DLR vs. VMR). Multiclass linear SVM predicted all areas with accuracy of 0.82 for the outside and within STN stimulation locations (ZI vs. DLR vs. VMR).

Conclusions 
EEG biomarkers can use low-frequency STN stimulation to localize STN DBS electrodes to ZI, DLR, and VMR STN sub-regions. These models can be used for both intraoperative electrode localization and postoperative stimulation programming sessions, and have potential to improve STN DBS clinical outcomes.