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

University of British Columbia

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.

Text

2020-06-05

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/74655

Physics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/