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Real-time classification of target borders during deep brain stimulation surgery

Human patients with advanced Parkinson’s disease (PD) and dystonia are often treated by Deep Brain Stimulation (DBS), which can alleviate the clinical symptoms of the disease. The most common brain targets are the subthalamic nucleus (STN) and the globus pallidus internus (GPi). These targets have been proven to be surgically safe and beneficial over time. Achieving a positive therapeutic response without adverse side effects depends to a large extent on the DBS lead location. Many medical centers use electrophysiological mapping techniques; i.e., multiple (1-5) track microelectrode recordings (MER) to optimize target selection during the implantation of DBS leads. Currently, the demarcation of the borders and subdomains of DBS targets must be done manually by a neurophysiologist. The process of delineation of the STN-substantia nigra pars reticulata (SNr) and the striato-pallidal transitions along pre-planned trajectories is time-consuming and may be biased, leading to a sub-optimal location of the DBS lead. Part one of my PhD reports the development of a real-time machine learning algorithm for the classification of both the uninterrupted STN-SNr transition and striato-pallidal borders. Striatal dysfunctions play a key role in the pathophysiology of PD and dystonia, but current understanding of the changes in the discharge rate and pattern of striatal projection neurons (SPNs) remains limited. The basal ganglia (BG) are often divided into low versus high frequency discharge nuclei; i.e., the striatum versus the STN, globus pallidus externus (GPe), GPi, and the SNr. Circuitry models of PD are based on striatal dopamine loss and aberrant striatal inputs to the BG network. To date, striatal activity has not been extensively studied during electrophysiology-guided mapping in patients undergoing DBS procedures primarily because of unsuitable mapping techniques or surgical time restrictions on recordings in the striatum. By analyzing the spatial and spectral characteristics of the striatum, GPe and GPi MERs, we discovered that pathological beta-oscillations comprised a continuous stretch within the GPi in PD. This contrasts with the GPe in PD that forms gradient beta-oscillations as a function of the distance from the ventral border. We then observed that the spatial distribution of low-frequency activity for both the GPe and GPi was nearly identical across genetic and non-genetic dystonia patients. In contrast, the GPe and GPi in PD exhibited both tremor (4-5 Hz) and beta frequency oscillations. We discovered a similar striatal spatial oscillatory distribution across the PD, genetic and non-genetic dystonia patients.After pooling data across all subjects and averaging across electrode trajectories, we found a unique signature in the striatum of both the non-genetic and genetic dystonia classes under light anesthesia consisting of an alpha (8-12 Hz) oscillation. In contrast, the striatum in awake genetic and awake non-genetic dystonia patients displayed no oscillations. We then observed a peak in the high beta range (20-30 Hz) of the GPi in the non-genetic awake dystonia patients. In contrast, the power spectra of the GPi in the genetic dystonia patients in both the lightly anesthetized and awake patients displayed decreased beta-oscillatory activity. These properties contribute to the goal of identifying the accurate DBS lead placement in an automatic and unbiased way. To better understand striatal firing abnormalities, in part two of my research, I recorded and examined striatal firing abnormalities in PD and dystonic patients undergoing DBS surgeries. I utilized a machine learning algorithm to automatically detect the striato-GPe border, and applied objective methods for the identification of striatal units and the quantification of their isolation quality and stationarity. Contrary to long-standing assumptions, I found no evidence for SPNs dysfunction, and that spike sorting errors were the likely cause of the perceived changes in neuronal firing rates in previous studies. The results showed that the firing rates and firing patterns of striatal neurons in PD patients did not differ significantly from those in patients with dystonia. Although some disease-dependent differences were initially observed in firing rate and burst firing, these disappeared when the analysis was restricted to well-isolated stationary recordings. Overall, the findings from this PhD thesis reveal that machine learning methods can provide an accurate confirmation of the STN-ventral and striato-pallidal borders without requiring input from an expert physiologist. These algorithms can thus minimize operating time and lead to outcome optimization in both STN and GPi DBS surgery. This machine learning method also helps resolve the long-standing controversy over the nature of striatal dysfunction in PD and suggests that changes in BG pathophysiology likely arise from structures outside the striatum.