Alzheimer's disease heterogeneity assessment using high dimensional clustering techniques

This thesis sets out to investigate the Alzheimer's disease (AD) heterogeneity in an unsupervised framework. Different subtypes of AD were identified in the past from a number of studies. The major objective of the thesis is to apply clustering methods that are specialized in coping with high dimensional data sets, in a sample of AD patients. The evaluation of these clustering methods and the interpretation of the clustered groups from a statistical and a medical point of view, are some of the additional objectives. The data consist of 271 MRI images of AD patients from the AddNeuroMed and the ADNI cohorts. The raw MRI's have been preprocessed with the software Freesurfer and 82 cortical and subcortical volumes have been extracted for the needs of the analysis. The effect of different strategies in the initialization of a modified Gaussian Mixed Model (GMM) (Bouveyron et al, 2007) has been studied. Additionally, the GMM and a Bayesian clustering method proposed by Nia (2009) have been compared with respect to their performances in various distance based evaluation criteria. The later method resulted in the most compact and isolated clusters. The optimal numbers of clusters was evaluated with the Hopkins statistic and 6 clusters were decided while 2 observations formed an outlier cluster. Different patterns of atrophy were discovered in the 6 clusters. One cluster presented atrophy in the medial temporal area only (n=37,~13.65%). Another cluster resented atrophy in the lateral and medial temporal lobe and parts of the parietal lobe (n=39,~14.4%). A third cluster presented atrophy in temporoparietal areas but also in the frontal lobe (n=74,~27.3%). The remaining three clusters presented diffuse atrophy in nearly all the association cortices with some variation in the patterns (n1=40,~14.7%,n2=58,~21.4,n3=21,7.7%). The 6 subtypes also differed in their demographical, clinical and pathological features