title: Prediction of STN-DBS Electrode Implantation Track in Parkinson's Disease by Using Local Field Potentials.
abstract: Optimal electrophysiological placement of the DBS electrode may lead to better long term clinical outcomes. Inter-subject anatomical variability and limitations in stereotaxic neuroimaging increase the complexity of physiological mapping performed in the operating room. Microelectrode single unit neuronal recording remains the most common intraoperative mapping technique, but requires significant expertise and is fraught by potential technical difficulties including robust measurement of the signal. In contrast, local field potentials (LFPs), owing to their oscillatory and robust nature and being more correlated with the disease symptoms, can overcome these technical issues. Therefore, we hypothesized that multiple spectral features extracted from microelectrode-recorded LFPs could be used to automate the identification of the optimal track and the STN localization. In this regard, we recorded LFPs from microelectrodes in three tracks from 22 patients during DBS electrode implantation surgery at different depths and aimed to predict the track selected by the neurosurgeon based on the interpretation of single unit recordings. A least mean square (LMS) algorithm was used to de-correlate LFPs in each track, in order to remove common activity between channels and increase their spatial specificity. Subband power in the beta band (11-32 Hz) and high frequency range (200-450 Hz) were extracted from the de-correlated LFP data and used as features. A linear discriminant analysis (LDA) method was applied both for the localization of the dorsal border of STN and the prediction of the optimal track. By fusing the information from these low and high frequency bands, the dorsal border of STN was localized with a root mean square (RMS) error of 1.22 mm. The prediction accuracy for the optimal track was 80%. Individual beta band (11-32 Hz) and the range of high frequency oscillations (200-450 Hz) provided prediction accuracies of 72 and 68% respectively. The best prediction result obtained with monopolar LFP data was 68%. These results establish the initial evidence that LFPs can be strategically fused with computational intelligence in the operating room for STN localization and the selection of the track for chronic DBS electrode implantation.
authors.affiliation: Clinical Neural Engineering Lab., Biomedical Engineering Department, University of Houston Houston, TX, USA.
authors.forename: Ilknur
authors.initials: I
authors.lastname: Telkes
authors.affiliation: Department of Neurology, Baylor College of Medicine Houston, TX, USA.
authors.forename: Joohi
authors.initials: J
authors.lastname: Jimenez-Shahed
authors.affiliation: Department of Neurosurgery, Baylor College of Medicine Houston, TX, USA.
authors.forename: Ashwin
authors.initials: A
authors.lastname: Viswanathan
authors.affiliation: Department of Neurosurgery, University of Colorado Aurora, CO, USA.
authors.forename: Aviva
authors.initials: A
authors.lastname: Abosch
authors.affiliation: Clinical Neural Engineering Lab., Biomedical Engineering Department, University of Houston Houston, TX, USA.
authors.forename: Nuri F
authors.initials: NF
authors.lastname: Ince
keywords: LDA classification; least mean square algorithm; local field potentials; microelectrode recordings; subthalamic nucleus