COMPUTERIZED TOOLS: A SUBSTITUTE OR A SUPPLEMENT WHEN DIAGNOSING ALZHEIMER'S DISEASE?

Alzheimer’s disease (AD) is the most common form of dementia in the elderly characterized by difficulties in memory, disturbances in language, changes in behavior, and impairments in daily life activities. By the time cognitive impairment manifests, substantial synaptic and neuronal degeneration has already occurred. Therefore, patients need to be diagnosed as early as possible at a preclinical or presymptomatic stage. This will be important when diseasemodifying treatments exist in the future. The main focus of this thesis is on the study of structural neuroimaging in AD and in prodromal stages of the disease. We emphasize the use of statistical learning for the analysis of structural neuroimaging data to achieve individual prediction of disease status and conversion from prodromal stages. The main aims of the thesis were to develop and validate computerized tools to identify patterns of atrophy with the potential of becoming markers of AD pathology using structural magnetic resonance imaging (sMRI) data and to develop a segmentation tool for Computed Tomography (CT). Using automated neuroanatomical software we measured multiple brain structures that were given to statistical learning techniques to create discriminative models for prediction of presence of disease and conversion from prodromal stages. Building statistical models based on sMRI data we investigated optimal normalization strategies for the combination of structural measures such as cortical thickness, cortical and subcortical volumes (Study I). A baseline model was created based on the optimal normalization strategy and combination of structural measures. This model was used to compare the discrimination ability of different statistical learning algorithms (decision trees, artificial neural networks, support vector machines and orthogonal partial least squares (OPLS)). Additionally, the addition of age, years of education and APOE phenotype was added to the baseline model to assess the impact on discrimination ability (Study II). The OPLS classification algorithm was trained on the baseline model to produce a structural index reflecting information about AD-like patterns of atrophy from each individual’s sMRI data. Additional longitudinal information at one-year follow-up was used to characterize the temporal evolution of the derived index (Study III). Since total intracranial volume (ICV) remains a morphological measure of interest and CT is today widely used in routine clinical investigations, we developed and validated an automated segmentation algorithm to estimate ICV from CT scans (Study IV). We believe computerized tools (automated neuroimaging software and statistical discriminative algorithms) have significantly enriched our knowledge and understanding of associated neurodegenerative pathology, its effects on cognition and interaction with age. These tools were mainly developed for research purposes but we believe all accumulated knowledge and insights could be translated into clinical settings, however, that is a challenge that remains open for future studies. LIST OF SCIENTIFIC PAPERS I. Westman, E. , Aguilar, C. , et al., 2013. Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topography, Volume 26, Issue 1, p. 9-23. II. Aguilar, C. , Westman, E. , et al., 2013. Different multivariate techniques for automated classification of MRI data in Alzheimer’s disease and mild cognitive impairment. Psychiatry Research: Neuroimaging, Volume 212, Issue 2, p. 89-98. III. Aguilar, C. , Muehlboeck, S. , et al., 2014. Application of a MRI based index to longitudinal atrophy change in Alzheimer disease, mild cognitive impairment and healthy older individuals in the AddNeuroMed cohort. Frontiers in Aging Neuroscience. doi: 10.3389/fnagi.2014. 00145. IV. Aguilar, C. , Edholm, K. , et al. 2015. Automated CT-based segmentation and quantification of total intracranial volume. European Ragiology, doi: 10.1007/s00330-015-3747-7 (epub ahead of print). Publications not included in thesis Pereira JB, Cavallin L, Spulber G, Aguilar C, et al., 2013. Influence of age, disease onset and ApoE4 on visual medial temporal lobe atrophy cut-offs. Journal of Internal Medicine. Volume 275, Issue 3, p. 317-330. Lebedev AV, Westman E, Beyer MK, Kramberger MG, Aguilar C, et al., 2013. Multivariate classification of patients with Alzheimer's and dementia with Lewy bodies using high-dimensional cortical thickness measurements: an MRI surface-based morphometric study. Journal of Neurology. Volume 260, Issue 4, p. 1104-1015. Paper I © 2012, The Author(s). Open-access article distributed under the terms of the Creative Commons Attribution License. Paper II © 2012. Reprinted with permission from Elsevier. Paper III © 2014, The Authors. Open-access article distributed under the terms of the Creative Commons Attribution License. Paper IV © 2015. Reprinted with permission from Springer. CONTENTS 1 Aging and Alzheimer’s disease 7 1.1 Neuropathology of Alzheimer’s disease 7 1.2 Modulators of phenotypic expression 9 1.2.1 Aging and neuropathology 10 1.2.2 APOE and AD pathogenesis 10 1.2.3 Education and brain reserve 10 1.3 Mild cognitive impairment 11 1.4 Evaluation of cognitive function 11 2 Imaging Alzheimer’s disease 13 2.1 Computed Tomography and Magnetic Resonance Imaging 13 2.2 Automated neuroimaging pipelines 14 2.3 Individual classification based on statistical learning 15 3 Statistical learning 17 3.1 Estimation of a functional relationship 17 3.2 Linear regression 18 3.3 Principal component analysis 19 3.4 Partial least squares 20 3.5 Orthogonal PLS 20 3.6 Linear classification 20 3.7 Maximum-likelihood estimation 21 3.8 Clustering 22 4 Aims of the thesis 23 5 Methodology 24 5.1 Ethical considerations 24 5.2 Studies I-III 24 5.3 Study IV 27 5.4 Preliminary studies 29 6 Results 32 7 Discussion 37 8 Conclusions and future perspectives 42 9 Acknowledgements 43 10 References 45 LIST OF ABBREVIATIONS