Doctor of Philosophy
St Lawrence, Keith
Frontotemporal dementia (FTD) is the second most common type of early-onset neurodegenerative dementia. This thesis investigated how hybrid positron emission tomography (PET) and magnetic resonance imaging (MRI) could benefit neuroimaging of FTD. Quantification of PET requires measuring a tracer’s arterial input function (AIF), which is invasive and challenging. An alternative non-invasive method is to extract an image-derived input function (IDIF) from PET images. My first project was to validate a software called caliPER designed to extract IDIFs by using high soft-tissue contrast MR images. Validation studies involved a porcine model as well as human controls. Good agreement of the area between extracted IDIFs and AIFs were found with an overestimation of 8 ± 5% in the animals and 6 ± 8% for the healthy human controls.
PET provides the ability to image neuroinflammation using tracers that target the 18KDa translocator protein (TSPO) in microglia. However, quantification is challenging due to the lack of a reference region. To overcome this, I investigated the application of a simultaneous estimation (SIME) method to dynamic PET data acquired with the TSPO tracer, [18F]FEPPA. The SIME method used IDIF and venous blood samples to correct for metabolites. The method was applied to a [18F]FEPPA data from healthy controls that included AIF to directly measure the binding potential and showed that the imaging duration could be reduced to 90 min without compromising the precision of the binding potential.
Metabolic connectivity mapping combines [18F] fluorodeoxyglucose (FDG) PET with resting-state functional MRI (rsfMRI) to estimate direction of signalling between brain regions, which is termed effective connectivity. Metabolic connectivity mapping is based on the theory that ~75% of glucose consumed is by post-synaptic activity. Therefore, the at the target region FDG activity is higher. For my third project, I used Metabolic connectivity mapping to investigate effective connectivity disruptions in behavioural variant FTD patients. Using healthy controls, we determined that neuronal signaling is from the anterior insula (AI) to the dorsal anterior cingulate cortex (dorsal ACC). This neuronal communication between dorsal ACC and AI provides insight into the neurobiological mechanisms related to emotional processing, decision-making, and self-regulation that are linked with bvFTD's behavioural manifestations.
Summary for Lay Audience
Frontotemporal dementia is a common neuro degenerative disease that affects behavioral, language and motor skills. Neuro imaging techniques like positron emission tomography and magnetic resonance imaging are helpful to see structural and functional changes in the brains of patients with FTD. For PET quantification, the gold standard is to acquire arterial blood samples, which is a painful and invasive procedure. An alternative method would be to use PET images to extract similar information non-invasively, called image-derived input functions (IDIFs). In project 1, we validated a software tool called caliPER that can be used to extract IDIFs. The tool was validated in pigs and humans against arterial blood samples. PET imaging can also visualize immune cells called microglia that are activated in conditions like neuroinflammation. However, the current method requires longer scan durations and invasive blood sampling. In project 2, using [18F]FEPPA PET tracer and novel simultaneous estimation techniques, I developed a less invasive approach to quantify tracer uptake. This approach reduced the scan duration from two hours to 90 minutes. One advantage of combined [18F] fluorodeoxyglucose PET and resting state functional MRI is its ability to investigate disrupted neuronal communication in FTD patients compared to controls. In project 3, we observed disrupted neuronal communication in two brain regions linked with FTD symptoms like altered behavioural and motor skills.
Dassanayake, Praveen S.B, "Applications of Hybrid PET/MRI to the Study of Frontotemporal Dementia" (2023). Electronic Thesis and Dissertation Repository. 9795.
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