Browsing by Subject "deep brain stimulation"
Now showing 1 - 4 of 4
Results Per Page
Sort Options
Item Characterization of the cortical electrophysiological effects of motor thalamic DBS and assessment of a pharmacological model for essential tremor(2021-02) Bello, EdwardDeep brain stimulation (DBS) of the cerebellar-receiving area of motor thalamus has proven to be a highly effective neurosurgical treatment for Essential tremor (ET). Previous clinical studies, however, have also indicated that the overall efficacy and efficiency of the therapy can vary from patient to patient and that the physiological rationale for this outcome variability is not well understood. Functional imaging studies have shown that the pathological state of ET and thalamic DBS treatment each exert a distributed effect on the motor control network including the primary motor cortex (M1). What this effect is on the neuronal level in M1 is not known. Through a series of electrophysiological experiments in a large preclinical animal model, we investigated first how neuronal spike rates and patterns in M1 change during thalamic DBS and second how such changes might explain the clinical observations that (1) higher frequency pulse trains for thalamic DBS are more effective in suppressing tremor and (2) electrode contacts at the ventral pole of motor thalamus are more efficient at reducing tremor. Higher frequency thalamic DBS resulted in a mild increase in the population averaged neuronal spike rate in M1, a significant decrease in population-averaged spike pattern entropy, and a strong increase in the proportion of neurons with phase-locked spike activity. In contrast, high-frequency DBS through electrodes at the ventral pole of motor thalamus at low current amplitudes (in comparison to electrodes within motor thalamus proper) was found to predominantly affect phase-locked spike activity but not spike rate and spike-pattern entropy. Together, these data suggest that M1 phase-locked spike activity may be a useful biomarker for future studies seeking to develop and assess new approaches for optimizing DBS therapy for action and postural tremors. Toward this goal, we also characterized and assessed the suitability of the alkaloid harmaline in generating a robust and consistent tremor in a large preclinical animal for future translational efforts developing technology to help individuals living with ET.Item Development of model-based and sensor-based programming techniques for optimizing directional deep brain stimulation therapy for movement disorders(2022-01) Brinda, AnneMarieDeep brain stimulation (DBS) therapy is a programmable neurosurgical intervention that can significantly improve quality of life for individuals with medication-refractory movement disorders, such as Essential Tremor and Parkinson’s Disease. However, clinical outcomes with DBS therapy still vary across patients, and the clinical time and effort necessary to program the stimulation settings to each patient’s symptoms presents practical challenges in the clinic. With the advent of directional lead technology and independent multi-channel current-controlled stimulation, the scope of possible DBS configurations is now substantially larger than it was even five years ago. This has greatly increased the time to determine the most effective electrode configuration, and in reality, much of the stimulation parameter space is left unexplored during a clinical visit. This thesis addressed the gap between the directional lead technology and its clinical implementation by developing three promising techniques to program directional DBS lead systems. The first programming technique involved developing subject-specific computational models of DBS based on individual MRI/CT scans. Comparing model predictions to clinical outcomes from patients with Essential Tremor revealed that lateral and medial parcellations of the motor-thalamic afferents of the cerebellothalamic tract were differentially associated with stimulation-induced therapy and side effects, respectively. Second, sensor-based evaluation of DBS in Essential Tremor patients revealed that directional contacts were superior to ring-mode contacts in providing optimized tremor reduction with reduced dysarthria. The third programming technique involved using neurophysiological feedback to guide the selection of which electrode(s) to use during DBS. In Parkinson’s disease, for example, stimulation through electrodes with higher resting-state beta-band oscillatory power in the subthalamic nucleus generally results in better clinical outcomes. Using a non-human primate model, we tracked how beta-band power changed spatially and temporally between intraoperative and chronic time points and showed that the strongest variability occurred within the first two weeks after lead implantation. This suggested that neurofeedback-based programming may be most consistent after the immune tissue response settles. Together, these results showed how model- and sensor-based programming techniques can limit the parameter space for programming directional DBS enabling more efficient and effective clinical outcomes in the future.Item Investigating the effects of subthalamic nucleus stimulation on gait and pedunculopontine nucleus activity in a preclinical animal model of Parkinson’s disease(2022-02) Doyle, AlexandraDopamine-replacement therapy and deep brain stimulation therapy can reliably manage several cardinal motor signs of Parkinson’s disease including tremor, rigidity, and bradykinesia. The efficacy of these treatments on gait and postural dysfunction, however, are often variable and wane over time. This doctoral dissertation advanced our understanding of parkinsonian gait dysfunction by (1) defining spatiotemporal progression of gait changes with increasing parkinsonian severity in the MPTP non-human primate model of Parkinson’s disease, (2) characterizing changes in gait parameters with targeted subthalamic deep brain stimulation, and (3) defining how targeted subthalamic deep brain stimulation differentially affects neuronal spike rate and pattern changes in the pedunculopontine nucleus, which is a key structure in the mesencephalic locomotor region. The major findings were that the MPTP non-human primate model displays progressive bradykinetic gait that align with severity of other cardinal motor signs; however, asymmetric and disordered gait patterns only appeared in the more advanced parkinsonian state. Deep brain stimulation of the subthalamic nucleus showed a spatial map in of improving and worsening bradykinetic gait, and this map aligned with a differential effect on pedunculopontine nucleus modulation. These results suggest that deep brain stimulation can impart therapeutic effects on gait symptoms, but the effects depend on how one modulates pathways involved in locomotion. Such findings will be useful for future efforts to optimize deep brain stimulation for individuals with Parkinson’s disease.Item Personalized computational models of deep brain stimulation(2016-12) Teplitzky, BenDeep brain stimulation (DBS) therapy is used for managing symptoms associated with a growing number of neurological disorders. One of the primary challenges with delivering this therapy, however, continues to be accurate neurosurgical targeting of the DBS lead electrodes and post-operative programming of the stimulation settings. Two approaches for addressing targeting have been advanced in recent years. These include novel DBS lead designs with more electrodes and computational models that can predict cellular modulation during DBS. Here, we developed a personalized computational modeling framework to (1) thoroughly investigate the electrode design parameter space for current and future DBS array designs, (2) generate and evaluate machine learning feature sets for semi-automated programming of DBS arrays, (3) study the influence of model parameters in predicting behavioral and electrophysiological outcomes of DBS in a preclinical animal model of Parkinson’s disease, and (4) evaluate feasibility of a novel endovascular targeting approach to delivering DBS therapy in humans. These studies show how independent current controlled stimulation with advanced machine learning algorithms can negate the need for highly dense electrode arrays to shift, steer, and sculpt regions of modulation within the brain. Additionally, these studies show that while advanced and personalized computational models of DBS can predict many of the behavioral and electrophysiological outcomes of DBS, there are remaining inconsistencies that suggest there are additional physiological mechanisms of DBS that are not yet well understood. Finally, the results show how computational models can be beneficial for prospective development of novel approaches to neuromodulation prior to large-scale preclinical and clinical studies.