Identifying the functional and anatomical circuitry of Parkinson’s disease motor dysfunction for closed-loop deep brain stimulation

Lauro, Peter Maxwell

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Overview

Year:: 2021
Contributor:: Lauro, Peter Maxwell (creator); Asaad, Wael (Advisor); Frank, Michael (Reader); Hochberg, Leigh (Reader); Richardson, Robert Mark (Reader); Brown University. Department of Neuroscience (sponsor)
Genre:: theses
Subject:: Parkinson's disease; Deep Brain Stimulation; Subthalamic nucleus; Sensorimotor cortex
Extent:: 16 (xvi), 103 p.

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Abstract:: Parkinson’s disease (PD) is a common neurodegenerative disease characterized by distinct motor symptoms such as tremor, bradykinesia, and rigidity. These symptoms present heterogeneously across patients and fluctuate within patients, but respond well to interventions such as deep brain stimulation (DBS). DBS is typically administered in an “open-loop” fashion where stimulation is continuously delivered regardless of a patient’s motor state. Efforts to improve DBS include “closed-loop” DBS where stimulation is delivered when β frequency oscillations/bursts (15–30 Hz) are detected. While effective for some symptoms (e.g. bradykinesia), β-driven closed-loop DBS can actually worsen others (tremor). Thus, patient outcomes could be improved by designing closed-loop DBS in a symptom- and patient-specific fashion. To do so, we enlisted patients with PD to perform an goal-directed behavioral task that continuously measured several motor metrics (tremor amplitude, slowness) that reflected symptoms of PD (tremor, bradykinesia). By simultaneously acquiring intracranial recordings from the subthalamic nucleus (STN) and sensorimotor cortex, we sought to understand the functional and anatomical circuitry of individual motor symptoms. In the first part of this work, we focused on defining the subthalamic-cortical neurophysiology of tremor and slowness. Here we revealed that tremor is a two-step process: while the onset of tremor was characterized by tremor frequencies (θ) emerging in the STN and propagating to motor cortex; sustained tremor was driven by motor cortex, while cortico-cortical synchrony shifted from γ to β frequencies. In contrast, slowness was a top-down phenomenon, with cortical β driving β oscillations and spiking in the STN. In the second part of this work, we designed patient-specific biomarkers and paradigms of closed-loop DBS. Specifically, we trained support vector regression (SVR) models to continuously decode individual motor metrics and overall motor dysfunction on short timescales (1–7 second estimates). When training SVR models on specific metrics, we revealed distinct frequency profiles across the local field potential spectrum for each metric, which occupied distinct subregions of the STN. Moreover, we decoded overall motor dysfunction (as a composite measure of motor metrics) well above chance for all patients. Using this metric-agnostic approach, we implemented patient-specific closed-loop DBS by training and testing a patient-specific DBS model in the operating room. Preliminary results from these closed-loop experiments suggest that our closed-loop approach may better improve motor dysfunction than open-loop stimulation.
Notes:: Thesis (Ph. D.)--Brown University, 2021

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Citation

Lauro, Peter Maxwell, "Identifying the functional and anatomical circuitry of Parkinson’s disease motor dysfunction for closed-loop deep brain stimulation" (2021). Neuroscience Theses and Dissertations. Brown Digital Repository. Brown University Library. https://repository.library.brown.edu/studio/item/bdr:mj63vkbf/

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Neuroscience Theses and Dissertations

Theses and Dissertations for the Neuroscience department.
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