"Brain Network Pattern Analysis with Positron Emission Tomography Data: Application to Parkinson’s Disease”

Event Date:
2020-05-25T09:00:00
2020-05-25T12:00:00
Event Location:
Virtual Defence
Speaker:
FANGLU JESSIE FU
Related Upcoming Events:
Intended Audience:
Public
Local Contact:

Physics andAstronomy

Event Information:

Final PhD Oral Examination

Abstract:
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, net- work pattern analyses (a subset of machine-learning methods) were 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 analysis has not been commonly used to study neurotransmitter changes using PET data. In addition, the emerging of multi-tracer imaging studies highlights the needs to develop novel joint analysis methods to ex- tract and combine complementary information from each imaging dataset to obtain a complete picture of the complex brain states. This thesis would be one of the first applications of such methods in the PET field.

Parkinsons disease (PD) is the second most common neurodegenerative disor- der with a long prodromal stage, and non-motor symptoms occur alongside or even before motor symptoms. Initially deemed to affect predominantly the dopamin- ergic system, PD is now deemed associated with alterations in several other non- dopaminergic neurotransmitter systems. Such changes, specific to PD, are some- times 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 current no effective cure for PD.

In this thesis work, we 1) explored spatial connectivity changes in the serotoner- gic system that are sensitive for detecting subtle changes in the prodromal and early disease stages and provide new insights into the mechanism of PD; 2) introduced Dynamic Mode Decomposition to extract spatio-temporal patterns of dopaminergic denervation for modeling 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 understanding of PD, but also provided more sensitive tools for the analysis of PET data in a variety of clinical applications. 

Add to Calendar 2020-05-25T09:00:00 2020-05-25T12:00:00 "Brain Network Pattern Analysis with Positron Emission Tomography Data: Application to Parkinson’s Disease” Event Information: Final PhD Oral Examination Abstract: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, net- work pattern analyses (a subset of machine-learning methods) were 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 analysis has not been commonly used to study neurotransmitter changes using PET data. In addition, the emerging of multi-tracer imaging studies highlights the needs to develop novel joint analysis methods to ex- tract and combine complementary information from each imaging dataset to obtain a complete picture of the complex brain states. This thesis would be one of the first applications of such methods in the PET field. Parkinsons disease (PD) is the second most common neurodegenerative disor- der with a long prodromal stage, and non-motor symptoms occur alongside or even before motor symptoms. Initially deemed to affect predominantly the dopamin- ergic system, PD is now deemed associated with alterations in several other non- dopaminergic neurotransmitter systems. Such changes, specific to PD, are some- times 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 current no effective cure for PD. In this thesis work, we 1) explored spatial connectivity changes in the serotoner- gic system that are sensitive for detecting subtle changes in the prodromal and early disease stages and provide new insights into the mechanism of PD; 2) introduced Dynamic Mode Decomposition to extract spatio-temporal patterns of dopaminergic denervation for modeling 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 understanding of PD, but also provided more sensitive tools for the analysis of PET data in a variety of clinical applications.  Event Location: Virtual Defence