Phenotypic heterogeneity and preclinical change in familial Alzheimer's disease

This thesis investigates relationships between clinical, neuroimaging and neuropathological features in autosomal dominant familial Alzheimer’s disease (FAD), with the aim of studying phenotypic heterogeneity and preclinical change. Chapters 1 and 2 introduce the background to the problem to be addressed in this thesis with an emphasis on current understanding of clinical and imaging changes in AD, and specifically in FAD. The FAD phenotype can be highly variable and, although it shares many clinical features with sporadic AD, it also possesses important differences. The clinical spectrum of FAD is first investigated, through analysis of all symptomatic cases studied at our research centre over the past twenty-five years (Chapter 3). Associations between phenotypic and pathological heterogeneity are then explored, with a study investigating genetic determinants of white matter hyperintensities and cerebral amyloid angiopathy (CAA) in FAD (Chapter 4). CAA is a common but variable feature of AD that appears to be an important factor in amyloid-modifying therapy and the term ‘ARIA’ has been coined to describe amyloid-related imaging abnormalities, thought to relate to vascular amyloid, that have been observed in a variety of amyloid-modifying therapy trials. Spontaneous changes of ARIA in FAD and the genetic risk factors that may provoke them are then described (Chapter 5). The recent launch of preclinical treatment trials for FAD necessitates better understanding of the trajectory of biomarker changes early in the disease. Observations from amyloid imaging studies, of presymptomatic amyloid deposition in the thalamus and striatum, motivated the final study, which examines changes in volume and diffusivity of these subcortical structures and their connecting white matter tracts in symptomatic and presymptomatic FAD mutation carriers (Chapter 6). Together, these studies demonstrate that exploring phenotypic heterogeneity and preclinical imaging changes can illuminate aspects of the underlying disease process, informing our understanding of FAD and potential effects of treatment

Multi-Modal Magnetic Resonance Imaging Predicts Regional Amyloid Burden in the Brain

Alzheimer’s disease (AD) is the most common cause of dementia and identifying early markers of this disease is important for prevention and treatment strategies. Amyloid- β (Aβ) protein deposition is one of the earliest detectable pathological changes in AD. But in-vivo detection of Aβ using positron emission tomography (PET) is hampered by high cost and limited geographical accessibility. These factors can become limiting when PET is used to screen large numbers of subjects into prevention trials when only a minority are expected to be amyloid-positive. Structural MRI is advantageous; as it is non-invasive, relatively inexpensive and more accessible. Thus it could be widely used in large studies, even when frequent or repetitive imaging is necessary. We used a machine learning, pattern recognition, approach using intensity-based features from individual and combination of MR modalities (T1 weighted, T2 weighted, T2 fluid attenuated inversion recovery [FLAIR], susceptibility weighted imaging) to predict voxel-level amyloid in the brain. The MR- Aβ relation was learned within each subject and generalized across subjects using subject–specific features (demographic, clinical, and summary MR features). When compared to other modalities, combination of T1-weighted, T2-weighted FLAIR, and SWI performed best in predicting the amyloid status as positive or negative. A combination of T2-weighted and SWI imaging performed the best in predicting change in amyloid over two timepoints. Overall, our results show feasibility of amyloid prediction by MRI and its potential use as an amyloid-screening tool

Evaluation of the potentials for optical coherence tomography (OCT) to detect early signs of retinal neurodegeneration

Among neuroretinal degenerations, glaucoma and age-related macular degeneration (AMD) have become the most frequent reasons for irreversible blindness globally. Among the causes of the elderly and senile dementia, Alzheimer’s disease (AD) has the leading position, the early ocular symptoms of which can potentially be a prognostic factor. The aim of this thesis was the early in vivo ligand-free detection of degenerative changes in the inner and outer retinal layers, which was possible using high-resolution optical coherence tomography (OCT) with the machine learning (ML) algorithms: support vector machine (SVM) and principal component analysis (PCA). Prior to the application of SVM and PCA for the classification of human OCT images, evaluation of the classifiers was performed in the classification of optical phantoms, the accuracy of which was in the range of 82-100%. This was the first attempt to measure the textural properties of various polystyrene and silica beads optical phantoms. To identify optical changes that characterise early apoptosis, OCT imaging of axotomised retinal ganglion cells (RGCs) in ex vivo retinal murine explants was performed. Substantial optical alterations in RGC dendrites in the early stages of apoptosis (up to 2 hours) were detected. ML algorithms correctly classified the retinal texture of the inner plexiform layer (IPL) of transgenic AD mice in all cases, indicating the potential for further investigation in in vivo animal and human studies. Not only the optical signature but also the transparency of the dissected murine retinal explants was investigated. Moreover, ML classification of 3xTg mice IPL layer was studied in terms of optical changes due to the RGD dendritic atrophy. ML classifiers’ accuracy in the detection of early and neovascular AMD was 93-100% for the texture of retinal pigment epithelium, 69-67% for the outer nuclear layer, 70% for the inner segment and 60-90% for the outer segment of photoreceptors. Classification of AMD stages and comparison with the age-matched healthy controls was carried out in the outer retina and RPE. Grey-level co-occurrence, run-length matrices, local binary patterns features were extracted from the IPL of the macula to classify glaucoma OCT images. The accuracy of linear and non-linear SVMs, linear and quadratic discriminant analyses, decision tree and logistic regression was between 55-70%. Based on the classifiers’ precision, recall and F1-score, Gaussian SVM outperformed other ML techniques. In this study, the observation of early glaucomatous subtle optical changes of human IPL was conducted. Also, the significance of various supervised ML algorithms was investigated. Understanding the optical signature of cumulative inherent speckle of OCT scans arising from apoptotic retinal ganglion cells and photoreceptors may provide vital information for the prevention of retinal neurodegeneration

The clinical impact of multidetector SPET technology

Introduction: Single photon emission tomography (SPET) is an established technique in Nuclear Medicine. Recent advances in SPET technology have now permitted the development of multidetector gamma cameras. This thesis evaluates some of these new gamma cameras and their impact on clinical practice. Aim: (a) To assess four new multidetector SPET gamma cameras (IGE Neurocam, Toshiba GCA-9300A, IGE Optima and Sopha DST). (b) To establish appropriate acquisition and analytical clinical protocols. Methodology: For each instrument, the tomographic spatial resolution, contrast and sensitivity were measured. The capability of a new slant hole collimator (IGE Optima) to perform radionuclide ventriculography (RNV) was assessed. To evaluate the utility of these systems, a total of 1215 patient studies were performed (1007 cardiac, 85 skeletal, 73 renal and 50 brain studies). The effect of 8, 16 and 32 minutes data acquisition on image quality and clinical relevance was evaluated. In addition, a new cardiac SPET protocol for rest/stress myocardial perfusion scintigraphy (thallium-201/Tc-99m tetrofosmin) was tested. Results: Tomographic spatial resolution of the order of 10 mm FWHM was achieved by all four systems. System sensitivity was related to the number of detectors and ranged between 9.2–11.2 Kcps/(MBq/ml)/cm per detector. The slant hole collimator with cephalic tilt gave highly reproducible results (r=0.98,SEE=+2) for ejection fraction measurements in 75 patients. There was no significant difference in the clinical information obtained using 8 min, 16 min and 32 min acquisitions. Based on patient studies and experience with these multidetector SPET systems, optimum acquisition and analysis protocols for commonly performed SPET studies were documented for routine clinical use. Artefacts due to patient movement during Tl-201 myocardial SPET studies were less frequent on a dual-detector system compared with a single detector system (0.7% and 4% respectively); while artefacts due to poor positioning or shift in centre of rotation were more. The rest/stress thallium-201/Tc-99m tetrofosmin study protocol (acquisition and analysis) was completed in 90 min. This protocol gave a sensitivity of 80% and specificity of 70% for the detection of coronary artery disease. Conclusion: For the first time a comprehensive comparison of multidetector SPET systems has been documented. Optimum acquisition and analysis protocols have been identified. The study also shows that the new generation of multidetector SPET systems offer adequate resolution and sensitivity for routine clinical imaging. Increased sensitivity can be translated into an increased patient throughput. This can increase the cost-effectiveness of this new technology

Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study

Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not