My PhD thesis, done at Universitat Pompeu Fabra, November 2016-Jaunary 2021. Under the supervision of Prof. Gemma Piella and Dr. Gerard Sanromà.
Alzheimer's disease (AD), the most common form of dementia, is an incurable neurodegenerative disease that affects millions of elderly people worldwide. Detecting the disease in its early stages is the key for a more effective treatment. AD is a multifactorial disease, where several markers represent different pathophysiological processes in the brain, with distinct progression paths over time. Methods to facilitate the integration and interpretation of longitudinal, heterogeneous medical data could be of benefit for a better understanding of the disease and its progression. In this thesis, we present statistical and machine learning methods and studies for early detection and to assess disease progression.
Contributions of this thesis are as follows: First, we present a review on machine learning applications in AD using longitudinal neuroimaging: we analyze their approach to typical challenges in longitudinal data analysis and show that machine learning methods using longitudinal data have potential to improve disease progression modelling and computer-aided diagnosis. Our second contribution is a study of AD subtyping using novel plasma-based blood markers. We used a multivariate, multiple kernel learning embedding method over blood-based markers to find subgroups of patients defined by distinctive blood marker profiles, and we analyze those subgroups using cross-sectional and longitudinal neuroimaging data. Our third contribution is a novel method based on recurrent, multimodal variational autoencoders to model the progression of the disease. It can use a variable number of modalities and time-points across different subjects, and we show its performance quantitatively and qualitatively. Our fourth and final contribution is an analysis of the impact of APOE
- chapters/: Contains the source code for each of the chapters of the thesis.
- figures/: Contains the figures used in the thesis.
- supplementary/: Contains supplementary files for each chapter.
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