Browsing by Subject "Parkinson's"

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    Causal Network Analysis in the Human Brain: Applications in Cognitive Control and Parkinson’s Disease
    (2022-04) Avvaru, Satya Venkata Sandeep
    The human brain is an efficient organization of 100 billion neurons anatomically connected by about 100 trillion synapses over multiple scales of space and functionally interactive over multiple scales of time. The recent mathematical and conceptual development of network science combined with the technological advancement of measuring neuronal dynamics motivated the field of network neuroscience. Network science provides a particularly appropriate framework to study several mechanisms in the brain by treating neural elements (a population of neurons, a sub-region) as nodes in a graph and neural interactions (synaptic connections, information flow) as its edges. The central goal of network neuroscience is to link macro-scale human brain network topology to cognitive functions and pathology. Although interactions between any two neural elements are inherently asymmetrical, few techniques characterize directional/causal connectivity. This dissertation proposes model-free techniques to estimate and analyze nonlinear causal interactions in the human brain. The proposed methods were employed to build machine learning models that decode the network organization using electrophysiological signals. Mental disorders constitute a significant source of disability, with few effective treatments. Dysfunctional cognitive control is a common element in various psychiatric disorders. The first part of the dissertation addresses the challenge of decoding human cognitive control. To this end, we analyze local field potentials (LFP) from 10 human subjects to discover network biomarkers of cognitive conflict. We utilize cortical and subcortical LFP recordings from the subjects during a cognitive task known as the Multi-Source Interference Task (MSIT). We propose a novel method called maximal variance node merging (MVNM) that merges nodes within a brain region to construct informative inter-region brain networks. Region-level effective (causal) networks computed using convergent cross-mapping and MVNM differentiate task engagement from background neural activity with 85% median classification accuracy. We also derive task engagement networks (TENs) that constitute the most discriminative inter-region connections. Subsequent graph analysis illustrates the crucial role of the dorsolateral prefrontal cortex (dlPFC) in task engagement, consistent with a widely accepted model for cognition. We also show that task engagement is linked to the theta (4-8 Hz) oscillations in the prefrontal cortex. Thus, we decode the task engagement and discover biomarkers that may facilitate closed-loop neuromodulation to enhance cognitive control. In the second part of the dissertation, the main goal is to use network features derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson’s disease (PD) patients from healthy controls (HC). We introduce a novel causality measure called frequency-domain convergent cross-mapping (FDCCM) that utilizes frequency-domain dynamics through nonlinear state-space reconstruction. Using synthesized chaotic timeseries, we investigate the general applicability of FDCCM at different causal strengths and noise levels. We show that FDCCM is resilient to additive Gaussian noise, making it suitable for real-world data. We used FDCCM networks estimated from scalp-EEG signals to classify the PD and HC groups with approximately 97% accuracy. The classifiers achieve high accuracy, independent of the patients’ medication status. More importantly, our spectral-based causality measure can significantly improve classification performance and reveal useful network biomarkers of Parkinson’s disease. Overall, this dissertation provides valuable techniques for causal network construction and analysis. Their usage is demonstrated on two applications: decoding cognitive control and detecting Parkinson’s disease. These methods can be extended to other neurological and psychiatric conditions to elucidate their network mechanisms.
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    Leveraging Machine Learning Tools To Develop Objective, Interpretable, And Accessible Assessments Of Postural Instability In Parkinson'S Disease
    (2023-04) Herbers, Cara
    Parkinson's disease (PD) is the second most common neurodegenerative disease in the United States, affecting 1 million Americans. PD-related postural instability (PI) is one of the most disabling motor symptoms of PD since it is associated with increased falls and loss of independence. PI has little or no response to current PD treatments, the underlying mechanisms are poorly understood, and the current clinical assessments are subjective and introduce human error. There is a need for improved diagnostic tools of PI for clinicians to better characterize, understand, and treat PD-related PI. Several criteria are necessary to address this clinical need: (1) the clinical rating of PI should be quantified objectively, (2) additional postural tasks should be clinically assessed and quantified, and (3) the assessments of PI should occur more frequently than a biannual clinical assessment. This project sought to develop two novel approaches to address these criteria. First, deep learning markerless pose estimation was leveraged to assess reactive step length in response to shoulder pull and surface translation perturbations for individuals with and without PD. Reactive step length was altered in PD (significantly for treadmill perturbations, and with an insignificant trend for shoulder pull perturbations), and improved by dopamine replacement therapy. Next, insole plantar pressure sensor data from 111 subjects (44 PD, 67 controls) were collected and used to assess PD-related PI during typical daily balance tasks. Machine learning models were developed to accurately identify PD from young controls (area under the curve (AUC) 0.99 +/- 0.00), PD from age-matched controls (AUC 0.99 +/- 0.01), and PD non-fallers from PD fallers (AUC 0.91 +/- 0.08). It was seen that utilizing features from both static and active tasks significantly improved classification performances and that all tasks were useful for separating controls from PD; however, tasks with higher postural threat were preferred for separating PD non-fallers from PD fallers. This work produced numerous clinical and translational implications. Notably, (1) simple and accessible quantitative measures can be used to identify PD and individuals with PD who fall, and (2) machine learning models can be leveraged for implementing, quantifying, and interpreting these measures into something clinically useful.