Martin McKeown

Prospective Graduate Students / Postdocs

This faculty member is currently not actively recruiting graduate students or Postdoctoral Fellows, but might consider co-supervision together with another faculty member.


Research Classification

Research Interests

novel treatments for Parkinson's
Video Monitoring Parkinson's
Brain Stimulation
Biomedical Engineering
Machine Learning

Relevant Thesis-Based Degree Programs

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.

Research Methodology

Brain stimulation (non-invasive)
longitudinal monitoring
machine learning
Video monitoring

Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Brain network pattern analysis with positron emission tomography data: application to Parkinson's disease (2020)

Positron emission tomography (PET) is commonly used to investigate changes within the brain due to aging and disease. Because our brain works as an integrated system where multiple brain regions work together to perform complex tasks, network pattern analyses (a subset of machine-learning methods) are often found to provide complementary, more sensitive and more robust information compared to traditional univariate analyses, especially in the field of magnetic resonance imaging (MRI). However, network pattern analyses have not been commonly used to study neurotransmitter changes using PET data. In addition, the emergence of multi-tracer imaging studies highlights the needs to develop novel joint analysis methods to extract and combine complementary information from each imaging dataset to obtain a complete picture of the complex brain states. This thesis constitutes one of the first applications of such methods in the PET field. Parkinson’s disease (PD) is the second most common neurodegenerative disorder. It has a long prodromal stage, and non-motor symptoms occur alongside or even before motor symptoms. Initially thought to affect predominantly the dopaminergic system, PD is now deemed to be associated with alterations in several other non-dopaminergic neurotransmitter systems. Such changes, specific to PD, are sometimes difficult to detect, especially in prodromal and early stages of the disease; the interactions between different disease-related mechanisms also remain largely unclear. In addition, the disease origin is unknown and there is currently no effective cure for PD. In this thesis work, we 1) explored deterministic spatial connectivity changes in the serotonergic system that are sensitive for detecting subtle changes in the prodromal and early disease stages; 2) introduced dynamic mode decomposition to extract spatio-temporal patterns of dopaminergic denervation for modelling disease progression; 3) introduced a novel joint pattern analysis approach to extract complementary information in the dopaminergic and serotonergic systems and their relationships with treatment response and treatment-induced complications. These novel methods not only lead to new understandings of PD, but also provide more sensitive and deterministic tools for the analysis of PET data in a variety of clinical applications.

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Myelin water imaging in health and disease - applications, techniques, and multimodal integration (2020)

Neuroimaging with magnetic resonance imaging (MRI) has made great contributionsto our understanding of neurological diseases. Among the many differentimaging techniques, myelin water imaging (MWI) appears to be particularlypromising for investigating white matter microstructure, particularly in terms ofits myelin content. MWI has shown great success in identifying and characterisingalterations of myelin content in neurological diseases but is still only available inresearch settings. In order to bring it closer to clinical practice, its utility, efficacy,and robustness need to be examined.In this work, we investigated the utility of MWI by applying it to Parkinsonsdisease (PD), a neurodegenerative disease with typically unremarkable changesin the white matter in a clinical setting. We show that MWI and data-drivenmultivariate analysis methods can predict distinct PD symptom domains.Furthermore, we have demonstrated a robust relation between myelin, cognitiveperformance and clinical characteristics in Multiple Sclerosis (MS) with adata fusion analysis that finds joint patterns of covariation among the differentmodalities.Additionally, we have devised new methods to analyse MWI images that notonly offer more information about the white matter microstructure, but also makeuse of complementary information of multimodal MRI experiments. We havedemonstrated a characteristic myelin pattern along major white matter fibre bundlesthat shows superior accuracy in classification of sex than traditional analysis.We have also shown that MWI can be linked to the topological organisation of functionalbrain networks, either on its own or in combination with other parameters characterising the white matter microstructure.Lastly, we have devised a novel method that makes use of spatiotemporalsimilarity of white matter voxels in order to denoise MWI data. This method leadsto spatially-smoother myelin maps and prove to be more robust in the presence ofnoise, ultimately leading to more accurate in vivo measurements of myelin in thebrain.In summary, we have shown the utility of MWI by applying it to neurodegenerativediseases, developed methods to leverage joint information of multimodalwhite matter imaging techniques, and proposed a novel method to denoise T2relaxation data.

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ROI-based brain functional connectivity using fMRI: regional signal representation, modelling and analysis (2020)

Inferring brain functional connectivity from functional magnetic resonance imaging (fMRI) data extends our understanding of systems-level functional organization of the brain. Functional connectivity can be assessed at the individual voxel or Regions of Interest (ROI) level, with pros and cons of each approach. This thesis focuses on addressing fundamental problems associated with ROI-based brain functional connectivity inference, including regional signal representation, brain functional connectivity modelling and brain functional connectivity analysis. Functional connectivity involving brainstem ROIs has been rarely studied. We propose a novel framework for brainstem-cortical functional connectivity modelling where the regional signal of brainstem nuclei is estimated by Partial Least Squares and connections between brainstem nuclei and other cortical/subcortical brain regions are reliably estimated by partial correlation. We then apply the proposed framework to assess functional connectivity of one particular brainstem nucleus - the pedunculopontine nucleus (PPN), which is important for ambulation, and is affected in diseases putting people at risk for falls (e.g., Parkinson’s Disease). A key issue for ROI-based brain functional connectivity assessment is how to summarize the information contained in the voxels of a given ROI. Currently, the signals from the same ROI voxels are simply averaged, neglecting any inhomogeneity in each ROI and assuming that the same voxels will interact with different ROIs in a similar manner. In this thesis, we develop a novel method of representing ROI activity and estimating brain functional connectivity that takes the regionally-specific nature of brain activity, the spatial location of concentrated activity, and activity in other ROIs into account. Finally, to facilitate the interpretation of the estimated brain functional connectivity networks, we propose the use of dynamic graph theoretical measures (e.g., the newly introduced graph spectral metric, Fiedler value) as potential MRI-related biomarkers.The proposed methods were applied to real fMRI datasets, with a primary focus on Parkinson’s disease. The proposed methods demonstrated enhanced robustness of brain functional connection estimation, with potential use in disease assessment and treatment evaluation. More broadly, this thesis suggests that brain functional connectivity offers a promising avenue for non-invasive and quantitative assessment of neurological diseases.

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Electrical vestibular stimulation for Parkinson's disease treatment (2019)

Parkinson's disease (PD) is a progressive movement disorder characterized by degeneration of dopaminergic neurons and abnormal brain oscillations. While invasive deep brain stimulation can improve some motor deficits by disrupting pathological brain oscillations, achieving comparable results with non-invasive brain stimulation (NIBS) remains elusive. Previous studies have suggested that electrical vestibular stimulation (EVS) may ameliorate some motor symptoms in PD. However, the investigated effects are limited to a few domains, only a handful of stimulation waveforms have been explored, and neuroimaging studies capable of probing the mechanisms are greatly lacking. The overarching objective of this thesis is to utilize biomedical engineering approaches to fully explore the EVS technique as a potential therapeutic intervention for PD. This involves development of new stimuli, development of new artifact rejection methods, and thorough investigations of brain and behavioural responses, as outlined below.To achieve the objective, noisy EVS is firstly revisited and tested with PD and healthy subjects to investigate effects on visuomotor tracking behaviours. Next, novel EVS stimuli are developed using multisine signals in distinct frequency bands and tested in the experiment where the stimuli are applied to PD and healthy subjects during rest and task conditions while EEG are being recorded. This simultaneous EVS-EEG study aims to provide insights into modulatory effects of EVS on brain oscillations and motor behaviours altered in PD and whether the effects are a function of different stimulation types. One critical challenge involved with EVS-EEG studies is that EEG recordings are severely corrupted by the stimulation artifacts. To resolve this, a quadrature regression and subsequent independent vector analysis method is developed and its superior denoising performance to conventional methods is demonstrated. Finally, underlying mechanisms of EVS effects in PD are investigated in a resting-state functional MRI study.The results from this thesis suggest that sub-threshold EVS in PD induces widespread motor changes and brain activities that are stimulus-dependent, suggesting subject-specific stimuli may ultimately be desirable to achieve a clinically meaningful effect.

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Advanced network analysis of resting-state functional connectivity to probe executive function in Parkinson's Disease and Multiple Sclerosis (2018)

This thesis is to probe the systems-level neurobiological bases for executive function in patient populations overarchingly.We focus on two representative diseases, Parkinson’s Disease (PD), and Multiple Sclerosis (MS), as although they have different pathologies, patients often result in similar cognitive deficits. We examine resting-state fMRI data from both PD and MS subjects with novel methods in a network fashion. We employ advanced connectivity analyses to evaluate graph theoretical, static and dynamic resting-state functional connectivity (rsFC) measures. Multivariate statistical methods such as Canonical Correlate Analysis (CCA) and Multiset Canonical Correlate Analysis (MCCA) are used to robustly link rsFC and cognitive performance. PD data used in the thesis research include three cohorts: Parkinson’s Progression Markers Initiative (PPMI) and two research projects conducted at UBC (project name: BCT and GFM2). For MS, two cohorts are included: OPERA MS clinical trial and COGMS research project, which are both conducted at UBC.After a general introduction in the first chapter, in the second chapter we examine multivariate relations between demographic and cognitive profiles with CCA, showing that female gender is associated with better cognitive performance in both diseases possibly due to protective effects of estrogen.In chapter 3, we use correlation to assess functional connections. Both diseases have significantly altered interhemispheric connectivity, which is associated with altered cognitive performance in MS, but not PD.In chapter 4, we utilize graph theoretical approaches and find increased segregation of rsFC in PD, supporting a previously-proposed model of vulnerability of hubs in disease populations. In MS, higher modularity of the rsFC network is correlated with better executive skills.In chapter 5, we explore dynamic rsFC and discover that longer disease duration in MS is associated with decreased dynamic rsFC. In both populations, dynamic interhemispheric connectivity is robustly associated with cognitive abilities.In chapter 6, MCCA is applied to jointly explore the associations between dynamic and stationary rsFC, and behavioural measures. In MS, better executive functioning is supported by higher education, stronger and dynamic rsFC; in PD, better memory function is related to segregated brain networks and dynamics of interhemispheric connections.Chapter 7 summarizes and concludes these chapters.

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Brain connectivity network modeling using fMRI signals (2016)

Functional magnetic resonance imaging (fMRI) is one of the most popular non-invasive neuroimaging technologies, which examines human brain at relatively good spatial resolution in both normal and disease states. In addition to the investigation of local neural activity in isolated brain regions, brain connectivity estimated from fMRI has provided a system-level view of brain functions. Despite recent progress on brain connectivity inference, there are still several challenges. Specifically, this thesis focuses on developing novel brain connectivity modeling approaches that can deal with particular challenges of real biomedical applications, including group pattern extraction from a population, false discovery rate control, incorporation of prior knowledge and time-varying brain connectivity network modeling. First, we propose a multi-subject, exploratory brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity and determination of the dominant brain connectivity patterns among a group of subjects. Furthermore, to integrate the genetic information at the population level, a framework for genetically-informed group brain connectivity modeling is developed. We then focus on estimating the time-varying brain connectivity networks. The temporal dynamics of brain connectivity assess the brain in the additional temporal dimension and provide a new perspective to the understanding of brain functions. In this thesis, we develop a sticky weighted time-varying model to investigate the time-dependent brain connectivity networks. As the brain must strike a balance between stability and flexibility, purely assuming that brain connectivity is static or dynamic may be unrealistic. We therefore further propose making joint inference of time-invariant connections and time-varying coupling patterns by employing a multitask learning model. The above proposed methods have been applied to real fMRI data sets, and the disease induced changes on the brain connectivity networks have been observed. The brain connectivity study is able to provide deeper insights into neurological diseases, complementing the traditional symptom-based diagnostic methods. Results reported in this thesis suggest that brain connectivity patterns may serve as potential disease biomarkers in Parkinson's Disease.

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Compensatory mechanisms in Parkinson's disease (2010)

Parkinson’s disease (PD) is a common movement disorder, affecting 1% of the population over the age of 65. Pathologically, PD results from degeneration of nigral dopaminergic neurons, however symptoms do not appear until an estimated 50% of these cells are lost, suggesting compensatory mechanisms exist which mask disease onset, and may later delay progression of the disease. Compensation may take place over various spatial and temporal scales, from changes in synaptic dopamine release and synthesis that take place over a period of minutes, to recruitment of novel, widespread networks of brain regions for a specific task, which may require formation of new connections over an extended period of time. Neuroimaging techniques have recently allowed the investigation of regional and network changes in activation related to motor performance in PD, however the question of whether such changes represent a downstream effect of basal ganglia degeneration, or a compensatory change, remains difficult to determine. Here, we applied an approach from research into Alzheimer’s Disease, where abnormal activation patterns are studied in the context of tasks of increasing difficulty, such that inferences regarding their compensatory nature can be made. We show that individuals with PD are able to increase the recruitment of normal networks for a motor task (motor reserve) as a form of compensation, in addition to compensatory recruitment of novel networks to accomplish the same task as healthy controls. In particular, we observe a switch from striato-thalamo-cortical (STC) motor loops to cerebello-thalamo-cortical (CTC) loops as a compensatory strategy. This compensatory recruitment involves changes in the amplitude, spatial extent, and connectivity of regions within the CTC pathway. However, this compensation does not come without a price, since we show that compensatory CTC recruitment involving disconnection between the STC and CTC loops occurs in subjects with tremor-dominant PD, but not akinetic-rigidity-dominant PD, supporting a growing body of evidence that suggests the cerebellum plays an important role in the generation of PD tremor. Together, this body of research has implications for treatments that target the symptom of tremor in PD, as therapies which minimize tremor might also reduce beneficial aspects of compensation.

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Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Neurophysiological characteristics of apathy in Parkinson's disease (2018)

Apathy in Parkinson’s disease (PD) is often resistant to therapy, difficult to quantify and poorly understood. It is commonly characterized by a lack of motivation and emotional blunting. Neural responses recorded using electroencephalography (EEG), such as neural oscillations and event-related potentials (ERPs), are often associated with motivated behaviour and emotion processing, but few studies have examined how these characteristics are affected in apathetic PD patients. To examine the behavioural and neural oscillatory characteristics of motivated movement in apathetic PD patients, we used an incentivized motor task, in which subjects could win money based on the amount of effort they produced on a hand grip. We demonstrated that PD patients with lower apathy scores could modulate their effort production to increasing rewards, whereas patients with more severe apathy could not. EEG results showed that apathetic PD patients exhibited a higher resting power in the alpha and theta frequency bands compared to non-apathetic PD subjects and healthy subjects. Furthermore, there was a significant correlation between absolute resting alpha power and relative alpha power during squeezing. These two factors were able to predict patient apathy scores, irrespective of age or disease severity. The same was true for absolute resting theta power and relative theta power during squeezing. To explore emotion processing in PD, we investigated ERPs from EEG recordings as subjects viewed emotionally evocative visual stimuli. We employed a data-driven approach to separate unique ERP time courses from one another called multiset canonical correlation analysis (MCCA). Results showed that the late positive potential (LPP), an ERP that responds to emotional stimuli, had a blunted amplitude in response to negative visual stimuli compared to healthy subjects. Interestingly, there was also a greater centro-parietal topographical representation of the LPP in PD subjects compared to healthy subjects, suggesting the presence of potential compensatory mechanisms for blunted neural reactivity to emotional stimuli in PD patients. This work lays the foundation for further understanding apathy and provides a quantitative test to measure apathy in people living with Parkinson’s. 

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Galvanic vestibular stimulation: the effects on postural instability in Parkinson's Disease (2017)

Postural instability and gait disorders (PIGD) are cardinal symptoms of Parkinson’s disease (PD) and a major source or morbidity; however, current treatments are largely ineffective. Galvanic vestibular stimulation (GVS) is a non-invasive stimulation technique previously reported to improve motor responsiveness in neurodegenerative disorders when applied at subthreshold levels. The response to GVS depends on the type of electrical signal and plane of stimulation (i.e. mediolateral (ML) or anterior/posterior (AP)). As such, the effect of subthreshold GVS in PD is not fully understood. This study is the first to investigate the comparative effects of GVS configurations by manipulating the type of electrical signal (stochastic vestibular stimulation (SVS) or multisine vestibular stimulation (MVS)) and plane of stimulation (ML or AP). Three types of stimuli are used: SVS-ML, SVS-AP and MVS-AP. Subthreshold GVS was first examined during standing, using a stable force platform, then during gait, using an electronic walkway and a dual task paradigm (serial-3 subtraction). Without stimulation, standing and gait patterns in PD were distinguishable from healthy age-matched controls (HC). Compared to HC, PD participants showed increased amplitude of postural sway, increased gait variability and decreased bilateral coordination. These baseline differences indicate that PIGD symptoms in PD are pathological, and not age-related, changes, that exist even with anti-Parkinsonian medication. Subthreshold GVS resulted in an effect at the level of the force plate, observed through significant coherences between ground reaction forces. SVS-AP reduced frequency of sway during standing and stride time variability during a dual task walk in PD participants to levels similar to those in HC. Multisine stimulation did not show superiority over stochastic stimulation. This study demonstrates the feasibility of subthreshold GVS as a potential method for improving PIGD symptoms and concludes that stochastic GVS, delivered in the AP configuration, is the most promising of the three tested methods for reducing sway frequency and improving stride time variability in people with PD.

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Personalized stimuli as treatment for resting tremor in Parkinson's disease (2017)

Resting tremor in Parkinson’s disease (PD) affects quality of life and individuals’ ability to complete activities of daily living. Resting tremor has been shown to respond to transcranial alternating current stimulation (tACS) when delivered out of phase with the tremor. The present work aimed to further investigate potential tACS-based treatment mechanisms by designing and delivering personalized stimuli and extend our understanding of Parkinsonian resting tremor. Nine participants with tremor dominant PD received fourteen unique tACS stimuli to Primary Motor Cortex (M1) and Supplementary Motor Area (SMA). Effect on tremor was measured before and during stimulation via a 9 degree of freedom (DoF) motion sensor. The first principal component score was obtained from Principal Component Analysis (PCA) of these measures and the power of the data was compared before and during stimulation using a two-sample t-test. Four custom stimuli were designed by weighted linear combination of the data with the greatest effect on tremor; two of which were designed to be suppressive and two were designed to be augmentative towards tremor. Average power was calculated following delivery of the personalized and non-personalized stimuli. Regardless of whether tACS was delivered as a personalized or non-personalized stimuli, results indicate an increased average power during stimulation compared to no stimulation and an overall trend towards augmentation of tremor across participants. Supporting analyses, including Multivariate Empirical Mode Decomposition (MEMD) reinforce this finding, showing no clear trend towards any specific frequencies contributing to tremor suppression. The present results suggest that a broad spectrum frequency-based approach is not an effective means of suppressing tremor in people with PD and a phase-based or more targeted frequency approach may have more promise as a treatment mechanism for resting tremor in PD.

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Multimodal assessment of neurodegenerative diseases (2016)

There is growing recognition that accurate assessment of brain function includes activity at multiple temporal and spatial scales. In this thesis, we explored ways to combine clinically-relevant imaging information derived from subjects with neurodegenerative disease. In the first work, we investigated a two-step framework to determine both joint and unique biomarkers from structural and functional MRI in 18 healthy control (HC) and 12 Parkinson’s disease (PD) subjects. Three matrices (structural, functional, and structural/functional interactions) were derived from a subset of features in both modalities that were likely candidates for discrimination between PD and HC subjects. Finally, Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed to determine if subjects’ clinical characteristics such as gender, smoking history, smell performance, Hoehn and Yahr Scale (H&Y Stage), and Unified Parkinson’s Disease Rating Scale (UPDRS) values, could be accurately predicted based on the imaging features. The results revealed that complementary biomarkers were most informative in predicting clinical scores in both groups. In the second work, for analyzing imaging data from subjects with Multiple Sclerosis (MS), we employed a joint Multimodal Statistical Analysis Framework, a data fusion approach that used Latent Variables (LV). We studied fusion of information from seven different imaging modalities: Myelin Water Imaging (MWI), Diffusion Tensor Imaging (DTI), resting state functional MRI (rsfMRI), cortical thickness of the right and left hemisphere, MS lesion load, and normalized brain volume from 47 subjects with MS. Decomposed common and unique information in each modality were acquired and their relationships with disease duration (DD), the Expanded Disability Status Scale (EDSS), and age, were analyzed through LASSO regression. We noted that common components of the seven modalities were the most accurate in predicting clinical indices. Results further revealed the regional importance of each modality by indicating a unique pattern of degeneration in MS and an asymmetry between the cortical thickness components in the two hemispheres. Our results demonstrate the power of utilizing multimodal imaging biomarkers in neurodegenerative diseases. Since structural imaging data is acquired along with functional data, we propose that fusion of information from both types of data should become part of routine analysis.

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A Joint Compensatory and Default Mode Network Closely Related to Motor Performance in Parkinson's Disease (2013)

The motor symptoms in Parkinson’s disease only appear after extensive dopaminergic nigral cell loss, suggesting the presence of redundancy or compensatory mechanisms that serve to delay symptom onset and maintain motor function. Previous studies have demonstrated altered activity in a premotor-parietal-cerebellar circuit, frequently interpreted, but not necessarily established to be compensatory. Unfortunately, it is difficult to differentiate compensatory from direct disease-related if a clear relationship between brain activity and motor performance is not rigorously established. Accordingly, the present thesis investigated fMRI connectivity patterns that predicted motor performance in 12 Parkinson’s patients and 11 healthy controls. Subjects performed a manual tracking task employing a rubber squeeze bulb that incorporated different sinusoidal frequencies and varying amounts of visual guidance. Motor performance was then assessed by first fitting linear dynamical systems models such that the desired tracking performance was the input and the actual tracking performance was the output. A feature of the models (damping ratio) was then used as a metric of performance. The group fMRI connectivity networks were derived by a conditional dependence statistical method, which distinguished between direct and indirect connectivity. Damping ratio from the behaviour models was then predicted by the fMRI connectivity strengths using a sparse linear regression method, and a leave-one-out validation procedure. In both patients and controls, damping ratio could be accurately predicted with fMRI connectivity patterns. In controls, premotor-cerebellar and cingulate connections were associated with increased damping ratio and enhanced performance. However, in patients the strength of premotor-cerebellar and visuomotor connections were associated with improved motor performance, while connection strengths within the default mode network were associated with worse performance. Simultaneous modelling of fMRI and behaviour is a powerful tool to assess compensatory changes in Parkinson’s subjects. The current thesis provides strong evidence that altered activity in some parts of the premotor-parietal-cerebellar network is, in fact, compensatory as previously speculated, as greater connectivity within this network contributed to maintenance of performance. Furthermore, activation of this compensatory network impairs the ability of the inferior parietal cortex to normally de-activate as part of the default mode network, possibly making patients susceptible to non-informative, extraneous stimuli.

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Electrophysiological and motoric effects of galvanic vestibular stimulation in normal and Parkinson's disease subjects (2013)

How vestibular input influences dynamical functional brain networks and sensorimotor processing is an unaddressed area of interest. Previous accounts have suggested that noisy galvanic vestibular stimulation is able to not only improve visuospatial processing in stroke patients, but also ameliorates some of the motor symptoms in Parkinson’s disease. However, the mechanisms through which these purported benefits are obtained are currently poorly understood. In Parkinson’s disease, patients suffer from symptoms of bradykinesia, or slowness of movement, as well as tremor, rigidity, postural instability and cognitive impairment. A proposed mechanism for bradykinesia is that in Parkinson’s disease cortical-basal ganglia-thalamocortical networks are “stuck” in a fixed state, resulting in poorly modulated, exaggerated oscillations resonating in the beta range (13-30 Hz). This thesis addresses a number of questions: What is the effect of external vestibular sensory input on widespread, systems-level oscillatory rhythms? When the brain is in a diseased state, as in Parkinson’s disease, can vestibular input modulate the abnormal dynamics of cortical-basal ganglia networks? Furthermore, is noisy galvanic vestibular stimulation consequently able to affect functional networks and information processing in the brain? Specifically, we investigated whether noisy galvanic vestibular stimulation was able to modulate synchrony of EEG oscillations in normal individuals and Parkinson’s disease subjects. Upon identifying significant neuromodulatory effects of noisy galvanic vestibular stimulation across broadband rhythms in the resting-state EEG activity, we speculated that information processing may be similarly affected in task-related networks in Parkinson’s disease. Subsequently, we investigated whether the same noisy vestibular stimulus would be able to improve motor performance in Parkinson’s disease subjects. We found that their dynamics of motor tracking movements were improved in a visuomotor task by stimulation. We speculate that noisy vestibular stimulation is able to reinstate the abnormal dynamics of functional networks in disease conditions. Therefore, this thesis provides a foundation for assessing the potential utility of galvanic vestibular stimulation as a novel, non-invasive, neuromodulatory therapeutic for Parkinson’s disease.

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Manual tracking in Parkinson's disease: implications for L-dopa-induced dyskinesias (2011)

Though Parkinson’s disease (PD) is considered to be a prototypical basal ganglia disorder, it has become increasingly clear that this traditional view does not capture the complexity of the disease pathophysiology. For instance, imaging studies demonstrate altered cerebellar activity in PD that may compensate for and/or contribute to the symptoms of the disease. L-dopa-induced dyskinesias (LID) are involuntary writhing movements that commonly occur as a side effect of L-dopa therapy, and despite the prevalence of LID their underlying mechanisms are poorly understood. Altered cerebellar activity in PD may contribute to the pathophysiology of LID, and due to altered ‘forward models’ lead dyskinetic subjects to more heavily rely on ambiguous visual feedback.The principal aim of this thesis was to investigate the ability of PD subjects to de-weight ambiguous visual feedback during motor performance, while examining this ability as well as subtle differences in motor performance across dyskinetic and non-dyskinetic PD subjects. To this end we designed a large-amplitude visually guided tracking task where the target ‘jittered’ about the desired trajectory, and used root mean square (RMS) error and linear dynamical system (LDS) models to quantify tracking performance. The three major findings of this work were: 1) in addition to their known susceptibility to speed, PD subjects off medication were significantly more susceptible to increasing visual uncertainty than control subjects, 2) despite similar RMS error during non-ambiguous tracking the damping ratio parameter of the LDS models was significantly lower for dyskinetic subjects off medication, and 3) dyskinetic PD subjects were significantly more susceptible to visual uncertainty than non-dyskinetic and control subjects, and though L-dopa improved their overall tracking ability, this came at the price of a greater response to and reliance on ambiguous visual feedback.From this work we conclude that PD subjects demonstrate an impaired ability to de-weight ambiguous visual input, possibly due to inadequate forward models, and which may be specific to LID pathophysiology. The presence of motor abnormalities while dyskinetic subjects are off medication and not actively experiencing LID is suggestive of persistent neural plasticity. We argue these findings are related to altered cerebellar function in PD.

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Modulation of frequency-dependent EEG connectivity in Parkinson's disease (2010)

Traditional models of Parkinson's disease (PD) have emphasized the progressive degeneration of dopaminergic projections to the basal ganglia (BG). Since the advent of deep brain stimulation (DBS) surgery for PD that allows direct BG recordings, it has become apparent that PD is also characterized by abnormal oscillatory activity within BG-thalamocortical loops. Altered β band activity in particular has been shown to correlate with bradykinesia and rigidity, and appears to be suppressed by both levodopa medication and high-frequency subthalamic nucleus (STN) DBS. However, recent animal studies have suggested that the primary site of DBS action is the cortex, thus implying that cortical areas might play a greater role than previously recognized in the modulation of abnormal PD rhythms. This thesis aimed to investigate cortical connectivity modulation in frequency bands (α, β) that have been described in oscillatory models of PD, and to understand the effects of levodopa on connectivity. We utilized a sparse Multivariate Autoregressive (mAR)-based Partial Directed Coherence (PDC) method to assess frequency-dependent EEG connectivity in PD subjects and controls performing a visually guided task previously shown to modulate abnormal oscillatory activity in the STN of PD patients. In addition, we utilized traditional spectral analysis to evaluate task-dependent power modulation in five electrode regions of interest. While spectral analysis revealed power modulation differences between PD and control subjects, it showed relatively modest differences between regions. In contrast, PDC-based analysis revealed complex, region-dependent alterations of directional connectivity in PD subjects as compared to normal subjects. Connectivity was particularly altered posteriorly, suggesting abnormalities in visual and visuo-motor processing. Moreover, connectivity measures correlated with motor Unified Parkinson’s Disease Rating Scores (UPDRS) in PD subjects withdrawn from medication. Levodopa administration only partially restored connectivity, and in some cases resulted in further exacerbation of abnormalities. Overall, we suggest that the use of a PDC-based method might be ideally suited to investigate temporally-sensitive, directional connectivity changes in both the healthy and the diseased state using non-invasive EEG. Our findings have implications for the investigation of abnormal rhythms not only in PD, but also in other conditions characterized by altered oscillatory activity, such as epilepsy or schizophrenia.

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