Thomas, George; (2023) Using neuroimaging to track symptom severity and dysfunctional brain networks in Parkinson’s disease progression. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Parkinson’s disease (PD), although traditionally thought of as a motor condition, also causes significant non-motor symptoms, among which cognitive and neuropsychiatric changes are some of the most common and distressing. Approximately 50% of people with PD will develop dementia within 10 years of diagnosis, and dementia is six times more common in PD than in the general population. PD is, however, highly heterogenous in its progression, with the incidence and rate of decline of particular symptoms varying widely between individuals. Measures to reliably predict those individuals who are at high risk of developing dementia, or to detect the earliest changes associated with this progression, are vitally needed, especially with the advent of potentially disease-modifying treatments for neurodegeneration. Such measures are, so far, unfortunately, lacking. How effective neuroimaging can be in this context is a key question, both in terms of clinical application and our understanding of PD progression. Conventional atrophy-based neuroimaging metrics are relatively insensitive in PD, as these relate to cell death which only occurs late in disease stage - after the point at which potentially disease-modifying interventions might be most useful. However, metrics sensitive to changes in tissue microstructure and changes in network dynamics show more promise in this context, particularly in relation to cognitive changes in PD. In this thesis, I will use advanced neuroimaging methods to track the presence and severity of clinical symptoms in PD, with particular attention to cognitive symptoms and symptoms associated with progression to dementia. I will utilise two complimentary neuroimaging approaches to this effect: quantitative susceptibility mapping (QSM), and dynamic causal modelling (DCM). QSM, the primary technique, will be used as a proxy measure for brain iron accumulation, which is increasingly thought of as a key pathological mechanism in PD. I will relate brain iron changes measured using QSM to the severity of cognitive and motor symptoms in a deeply phenotyped PD cohort, and will assess the predictive ability of QSM in the 5 same group after three years. Additionally, I will use transcriptomic data from the Allen Institute to show how differences in regional gene expression in health render specific areas of the brain more vulnerable to iron accumulation in PD. In doing so, I will shed light on the biological and cellular mechanisms driving selective vulnerability to neurodegeneration. DCM, the secondary technique, allows for the description of causal functional interactions between brain regions. I will use DCM in PD to probe mechanistic hypotheses about the origin of visual hallucinations, a symptom strongly associated with progression to dementia. The findings presented reveal how changes in brain iron concentration are associated with current and future cognitive and motor symptom severity in PD, and how changes in network dynamics are associated with the incidence of visual hallucinations. Moreover, being able to relate imaging changes to underlying differences in regional gene expression provides a link to potential cellular mechanisms, offering insights about the potential drivers of neurodegeneration in PD.

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