Browsing by Subject "Parkinson's Disease"

Now showing 1 - 7 of 7
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    Advances in Bidirectional Deep Brain Stimulation Interfaces
    (2014-12) Connolly, Allison
    Deep brain stimulation (DBS) is a neurosurgical therapy for Parkinson's disease that involves the implantation of a four contact lead into subcortical brain structures for delivering continuous, high frequency electrical stimulation. This doctoral dissertation has aimed to advance DBS technology for the treatment of Parkinson's disease by: 1) elucidating biomarkers of the disease and DBS therapy, 2) evaluating novel, 32 contact high-density electrode arrays to improve sensing and stimulation within the basal ganglia, and 3) developing computational algorithms that can capture complex neurophysiological interactions in high-dimensional feature spaces of these biomarkers. The primary studies employed the MPTP non-human primate model of Parkinsonism to invasively probe how neural oscillations in the form of local field potentials (LFPs) are modulated in conjunction with disease severity, therapies, and behavior. These results demonstrate that high-density electrode arrays are superior to the current state- of-the-art, because they improve the spatial selectivity of sensing LFPs and enable the delivery of directional stimulation. Subsequently, I have shown how non-invasive imag- ing techniques and commercially available implantable devices could be used to study Parkinson's disease in patients. Ultimately, these results motivate the use of higher-density DBS leads for sensing and stimulation, and facilitate the implementation of more complex therapeutic algorithms, such as closed-loop stimulation.
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    Characterization of evoked compound action potentials in targets of deep brain stimulation for Parkinson’s disease
    (2022-10) Rosing, Joshua
    Deep brain stimulation (DBS) for Parkinson’s disease relies on accurate targeting of stimulation to provide the best therapeutic outcomes for patients. Current clinical practices typically rely on a brute force approach to finding the ideal stimulation electrode, and despite improvements to lead geometries such as the inclusion of rows of directional electrodes for precise targeting of stimulation, time constraints often prevent clinicians from making good use of these advancements. Additionally, although research has uncovered specific stimulation targets in common implant areas that are ideal for the treatment of specific symptoms or the avoidance of certain side effects, the clinical capacity for localizing a lead after implantation is not sufficient for confident declarations of implant location, and even the best imaging techniques can only be confirmed with post-mortem histology. Evoked compound action potentials (ECAPs) have been shown to vary by brain region, and to be linked to therapeutic outcomes, but a detailed investigation of their spatiotemporal properties has not yet been conducted. Through a series of experiments, in parkinsonian non-human primates instrumented with scaled-down clinical DBS leads, ECAP responses to changes in stimulation amplitudes, pulse widths, and electrode configurations were systematically investigated. Additionally, a novel DBS lead with a liquid crystal polymer (LCP) substrate and a high-density array of electrodes with a rough platinum-iridium site coating was evaluated for improved spatial resolution in ECAP and local field potential recordings in DBS targets. Project 1: One challenge with optimizing DBS therapy for a given patient is knowing where electrodes are located relative to the neural pathways around the DBS lead. We tested the hypothesis that ECAP features would differ between electrodes within gray matter (subthalamic nucleus, STN) and white matter (lenticular fasciculus, LF) for STN-DBS implants. ECAPs in these targets were characterized by short-latency ‘primary’ features (within 1.6 ms of stimulus pulse onset) and longer-latency ‘secondary’ features (>1.6 ms after stimulus pulse onset). We observed that ECAP primary feature responses were significantly larger in amplitude for LF/LF stimulation/record sites than for STN/STN stimulation/record sites. Furthermore, the number of secondary features detected in the STN (for STN or LF stimulation) was higher than that in LF (for LF stimulation). This supports the concept that ECAP primary features derive from direct axonal activation and secondary features result from post-synaptic axonal activation in the basal ganglia network. Primary feature amplitude was able to accurately predict electrode location at the border of the lenticular fasciculus and STN within and across all four subjects. Project 2: Another challenge with optimizing DBS therapy for Parkinson’s disease has been finding biomarkers that align with the seconds to minutes wash-in effects of DBS therapy on parkinsonian motor signs. ECAP features were found to adapt over the duration of the applied high-frequency DBS pulse train. Primary features habituated over time, while secondary features increased in latency over the first 30 seconds of stimulation, and trended toward earlier latencies at higher stimulation amplitudes. The total increase in secondary feature latency over the 30 seconds following stimulation onset also increased with increasing stimulation amplitude. In comparison to the instantaneous changes in spectral local field potential (LFP) power observed during STN-DBS, the temporal wash-in dynamics of ECAP responses seem to better align with the temporal wash-in profiles of DBS therapy on parkinsonian motor signs, and future studies will need to further investigate correlations between ECAPs and motor signs. Project 3: With the advent of microfabricated technology come opportunities to create bidirectional DBS lead technology to sense and modulate neural activity with higher spatial resolution. To further investigate the spatial features of ECAPs in the basal ganglia, we designed, developed, and evaluated a novel high-density LCP substrate DBS array. The arrays provided improvements in electrode longevity over previous high-density DBS arrays while also providing increased spatial resolution for both ECAP responses and LFP activity compared to state-of-the-art clinical electrodes.
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    Development of High-Throughput and High-Content Analysis Assays for Neurodegeneration-Related Intrinsically Disorderd Proteins
    (2020) Nathan, Noah
    We developed a FRET-based protein-protein biosensor of Fused in Sarcoma (FUS), an Amyotrophic Lateral Sclerosis and Frontotemporal Dementia related protein. The FUS biosensor had a robust signal, yielding a FRET efficiency of 7.61% with low signal to noise ratio. Based on high-throughput FRET measurements, we determined a standard deviation of 0.0158 ns for the donor/acceptor fluorescent lifetime. In future drug screens for compounds that modulate FUS aggregation, the threshold for hits will be set at 2.45 ± 0.0474 ns (3 SD). In addition, we implemented a MATLAB script that quantifies the ratio of cytoplasmic to nuclear FUS-rich stress granules from fluorescent images of FUS-GFP-expressing N2a cells. We showed that sorbitol, which has been shown to cause FUS mislocalization via hypertonic stress, caused a shift in the cytoplasmic to nuclear ratio of FUS as compared to untreated cells. We implemented a second MATLAB automated algorithm that quantifies total neurite outgrowth from neurospheres expressing the Parkinson's disease-related protein alpha-synuclein. We found that the mutant aSyn A53T caused a reduction of 41% in total neurites as compared to WT aSyn-expressing neurospheres. This result was validated by counting neurites manually with ImageJ, which yielded a reduction in neurites of 49%.
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    Does coffee protect you against Parkinson’s Disease?
    (2012-07-23) Stevens, James
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    The Experience of care-giving for a person with Parkinson’s Disease.
    (2010-05) Bogard, Connie Lynn
    As the population continues to become more aged and at risk for chronic illness, there will be a growing need for caregivers. Caregivers to persons with Parkinson's disease (PD) face the challenge of providing care over many years due to the chronic progressive nature of this neurological disorder. The purpose of this study was to understand and discover the multi-dimensional cognitive, affective and psychomotor capabilities and attributes of informal care-giving for individuals with PD. The research question was: What is the meaning of the caring experience from the perspective of the informal caregiver in the day-to-day interactions with the person with PD? This phenomenological investigation looks into the lives of 13 caregivers who perceived that they were in a caring relationship with a person diagnosed with Parkinson's disease. Caregivers participated in two in-depth, open ended interview sessions that were audiotaped and then transcribed verbatim for analysis. Three themes emerged from the analysis: (a) Care-giving is an unplanned journey; (b) I am living with the disease too; and (c) My relationshiop with the person I care for and others is changing. Subthemes for each were described. The findings suggested implications for clinical practice and future research. First, caregivers should be provided formal guidance and educational opportunities from health care providers over the course of the disease process. Second, caregivers and persons with PD should be viewed from a holistic perspective to ensure optimal care for the person with PD and support for the caregiver. Third, a multidisciplinary collaborative team approach should be used to facilitate communication across disciplines with the management of PD. Fourth, caregivers should be provided opportunity and connections with support groups. Fifth, caregiver health and wellness should be optimized over the course of the disease process to keep the caregiver-care recipient relationship intact and strong. Sixth, caregiver vigilance and concern for safety should be fostered and evaluated. Seventh, caregiver stressors and protective factors should be identified and appropriate internventions instigated.
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    Machine Learning for Deep Brain Stimulation
    (2020-02) Grado, Logan
    Deep brain stimulation (DBS) is an effective treatment for a variety of neurological disorders, including Parkinson’s disease (PD). However, the success of DBS relies on selecting stimulation parameters which relieve symptoms while simultaneously avoiding stimulation-induced side-effects. Currently, DBS is programmed through a time-intensive trial-and-error process in which the clinician systematically evaluates stimulation settings, requiring hours of effort and multiple patient visits. Additionally, advances in DBS lead technology and stimulation algorithms are adding additional free parameters, further increasing the difficulty of programming these devices. This doctoral thesis advanced the programming of DBS arrays by: (1) developing the slid- ing windowed infinite Fourier transform (SWIFT), an efficient method of extracting oscillatory neural features which can be used to program DBS systems, (2) developing the Bayesian adaptive dual controller (ADC), a type of Active Learning DBS which can be used to learn optimal stimulation parameters, and (3) demonstrating the ef- ficacy of the Bayesian ADC in an animal model of PD. The primary findings of this dissertation suggest that the Bayesian ADC is capable of efficiently and autonomously learning stimulation parameters for DBS in order to optimize a selected biomarker. Furthermore, it was demonstrated that parameters learned by the Bayesian ADC performed as well as control parameters identified through a standard trial-and-error programming process. Together, these results suggest that the Bayesian ADC should be clinically translatable for tuning DBS in future studies.
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    Wearable Sensor System for Home-Based Individualized Analysis of Postural Instability in Parkinson’s Disease Patients
    (2023-07) Nouriani, Ali
    This thesis presents the design, development, and evaluation of a wearable sensor system aimed at providing home-based, individualized analysis of postural instability in movement disorder patients such as subjects with Parkinson’s Disease (PD). Past studies have shown that these patients' behaviors vary significantly in clinical settings as compared to their natural home environment. Prior methods based on fall diaries and smartphone-based mobility monitoring have previously been utilized but have been found to be of limited value. Inertial Measurement Units (IMUs) are inexpensive, wearable, and readily available on smart phones and smart watches in recent years. But these consumer-grade sensors suffer from noise and drift and are usually used in combination with other measurements such as the Global Positioning System (GPS) or cameras. However, for the purpose of everyday human body motion tracking and step length estimation, GPS is not accurate enough and does not work well indoors. Camera systems are relatively expensive, impractical to widely implement and raise many privacy concerns. Hence, this study leverages advanced estimation and artificial intelligence algorithms to solely use IMU sensors for human motion analysis. This study centers on automatic human activity recognition, accurate step length estimation, human pose estimation and finally, fall risk assessment of patients over the next one year based on one week of data collected by IMU devices in their home environments. The research starts by examining the estimation of step length and other gait variables using IMU sensors. Accurate step length estimation has a number of useful health applications, including its use in characterizing the postural instability of Parkinson’s disease patients. Three different sensor configurations are studied using sensors on the shank and/or thigh of a human subject. A nonlinear estimation problem is formulated that aims to estimate shank angle, thigh angle, bias parameters of the inertial sensors and step lengths. A nonlinear observer is designed using Lyapunov analysis and requires solving an LMI to find a stabilizing observer gain. It turns out that global stability over the entire operating region can only be obtained by using switched gains, one gain for each piecewise monotonic region of the nonlinear output function. Experimental results are presented on the performance of the nonlinear observer and compared with gold standard reference measurements from an infrared camera capture system. The observer's estimates are used to fuel state-of-the-art machine learning and deep learning models, such as Convolutional Neural Networks and Long Short-Term Memory cells (CNN-LSTM). These models enable high-accuracy activity recognition. However, deep learning algorithms typically need large training datasets to be able to generalize to rare events such as near-falls in PD patients. To address this limitation, a novel algorithm combining a high-gain nonlinear observer and transfer learning, using deep learning computer vision classification algorithms, is developed for human activity recognition. The nonlinear high-gain observer precisely estimates the attitude of the human subject's chest using data from a single Inertial Measurement Unit (IMU). The signals processed by the observer are then transformed into spectrograms to create "images" of the signals' frequency response. Deep learning computer vision algorithms, pre-trained on millions of images, are fine-tuned through transfer learning. This process illustrates how to train a robust deep learning network for activity recognition even with limited datasets. Moreover, the algorithm that employs the high gain observer is demonstrated to achieve superior performance compared to the algorithm based on just raw accelerometer and gyro signals. A different activity recognition algorithm based on the use of transfer functions to represent various daily living activities that require very limited training data is also shown to work very effectively. The thesis validates the activity recognition algorithms in real-world environments and also discusses the development of behavioral biomarkers of falls. These biomarkers offer a stronger correlation with prospective fall frequency in patients than standard clinical tests, hence improving fall prediction. The research further introduces a novel method for human pose estimation that combines a high-gain observer with deep learning and kinematic modeling, providing superior full-body joint position and body-segment angle estimations from a sparse set of IMUs attached to a few locations on the subject. Lastly, the study compares home-based motion biomarkers against standard clinical metrics in predicting future falls, revealing that home data yields a higher predictive accuracy. The outcome underscores the value of assessing patients in their natural home environment, paving the way for improved treatment and fall prevention strategies for Parkinson’s disease patients in the future.