Automated Brain Mapping to Evaluate the Relationship between Neurodegeneration, Cerebral Small Vessel Disease and Structural Covariance Network Disruption in Alzheimer's Disease

Humans are currently living longer than any other period in history. However, gains in life expectancy do not portend a commensurate increase in quality of life years, particularly in persons with dementia. This is pertinent as the number of persons suffering from Alzheimerâ s disease (AD) is expected to double to over a million within a generation in Canada. The pathological underpinnings of AD remain unclear but are thought to target large-scale brain systems. It is posited that disease of the brainâ s small blood vessels may contribute to AD progression by disrupting structural brain networks that subserve complex cognitive routines. Therefore, the overarching aim of this dissertation is to determine the contribution of cerebral small vessel disease (SVD) versus coexistent neurodegeneration to brain network disruption in AD. The first part of this thesis presents and validates a state-of-the-art automated hippocampal segmentation technique for magnetic resonance imaging to accurately measure neurodegenerative status in mixed disease samples. The second part of this thesis assesses the relationship between hippocampal volume, SVD burden and grey matter cortico- and subneocortico-cortical network hub disruption. Finally, we extend these findings by introducing a method we call covariance-based connectomics to characterize how cortex-wide polysynaptic (grey-white matter) systems are disrupted in relation to SVD and neurodegenerative markers in AD versus age-matched controls. In summary, this thesis presents innovative human brain mapping techniques and the application of these methods to explore the relationship between SVD and AD in vivo. The SVD-signature of network disruption in AD is elucidated, and evidence is presented which supports the idea that SVD is differentially associated with neural network degradation in older adults and persons with mild AD.

Department dc:contributor.department

Medical Science

Year dc:date.issued

2016

Author dc:creator

• Nestor, Sean Michael

Advisor dc:contributor.advisor

• Black, Sandra E

• Alzheimer's disease
• Brain Networks
• Covariance-based connectomics
• Magnetic Resonance Imaging
• Small Vessel Disease
• Structural covariance

Handle dc:identifier.uri

http://hdl.handle.net/1807/76578

OAI identifier oai:identifier

oai:utoronto.scholaris.ca:1807/76578