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Quantitative imaging of mitochondrial and synaptic function in the human brain with 18F-BCPP-EF, 11C-SA-4503, and 11C-UCB-J
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Mansur-A-2020-PhD-Thesis.pdf | Thesis | 8.24 MB | Adobe PDF | View/Open |
Title: | Quantitative imaging of mitochondrial and synaptic function in the human brain with 18F-BCPP-EF, 11C-SA-4503, and 11C-UCB-J |
Authors: | Mansur, Ayla |
Item Type: | Thesis or dissertation |
Abstract: | The ‘Molecular Imaging of Neurodegenerative Disease: Mitochondria, Associated Proteins & Synapses’ (MIND-MAPS) Consortium was formed to investigate three potential molecular markers of the mitochondrial/endoplasmic reticulum/synaptic axis dysfunction associated with neurodegeneration using positron emission tomography (PET) in the healthy human brain as well as across a number of neurodegenerative diseases. Mitochondrial complex-I (MC-I), sigma 1 receptor (S1R) and synaptic vesicle protein 2A (SV2A) can be quantified using PET radioligands 18F-BCPP-EF, 11C-SA-4503 and 11C-UCB-J, respectively, provided these ligands are selective for their targets, have suitable kinetics and produce a signal that can be analysed using an appropriate tracer kinetic model. The main contribution of this thesis was the identification and development of a set of optimal tracer kinetic models and PET image derived outcome measures for each of these radioligands to be implemented across the MIND-MAPS cohorts. The work presented on 18F-BCPP-EF (Chapter 4) was the first published quantification of this ligand in the human brain, and showed that the volume of distribution (VT), the VT normalised by the free plasma fraction (fp) and the distribution volume ratio (DVR) derived using either multilinear analysis 1 (MA1) or the two-tissue compartment model (2TC) can be reliably used to quantify MC-I with good reproducibility. The work presented on 11C-SA-4503 (Chapter 5) established MA1 as the optimal kinetic model for the quantification of S1R in the healthy human brain using either VT or VT/fp as outcome measures. Some of the drawbacks of the ligand including unreliable metabolite measurements and associated high intersubject variability of the outcome measures were elucidated and improvements suggested. The characterisation of 11C-UCB-J (Chapter 6) supported previous literature results by showing that the 1TC model is the optimal compartmental model to estimate VT with time stability results showing that scan time could be reduced to 60 minutes. VT/fp, DVR-1 (BPND=binding potential) and semiquantitative outcome measure SUVr-1 derived from a 20 minute static PET scan were also established as reliable outcome measures, adding confidence to the excellence of 11C-UCB-J as a tool for quantifying SV2A, and its suitability for regular use in clinical settings. In addition to establishing the appropriate tracer quantification methods, an investigation on the effects of healthy ageing on MC-I, S1R and SV2A (Chapter 7) demonstrated that there is age-related reduction of MC-I in the caudate as well in SV2A in both the caudate and thalamus regions, while S1R is mostly stable with age in healthy individuals. Altogether, the work presented in this thesis generated the optimal set of tracer kinetic modelling pipelines and outcome measures for the quantification of 18F-BCPP-EF, 11C-SA-4503 and 11C-UCB-J in humans, allowing for the implementation of consistent analytical methods across MIND-MAPS cohorts to enable the study of changes in the mitochondrial/endoplasmic reticulum/synaptic axis in ageing and neurodegeneration. |
Content Version: | Open Access |
Issue Date: | May-2020 |
Date Awarded: | Nov-2020 |
URI: | http://hdl.handle.net/10044/1/84574 |
DOI: | https://doi.org/10.25560/84574 |
Copyright Statement: | Creative Commons Attribution Non-Commercial No Derivatives licence |
Supervisor: | Gunn, Roger |
Sponsor/Funder: | Engineering and Physical Sciences Research Council |
Funder's Grant Number: | WQAA G01735 |
Department: | Department of Brain Sciences |
Publisher: | Imperial College London |
Qualification Level: | Doctoral |
Qualification Name: | Doctor of Philosophy (PhD) |
Appears in Collections: | Department of Brain Sciences PhD Theses |
This item is licensed under a Creative Commons License