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 d...

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Main Author: Poulakis, Konstantinos
Format: Others
Language:English
Published: Linköpings universitet, Statistik 2016
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129923
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spelling ndltd-UPSALLA1-oai-DiVA.org-liu-1299232016-07-02T05:27:52ZAlzheimer's disease heterogeneity assessment using high dimensional clustering techniquesengPoulakis, KonstantinosLinköpings universitet, StatistikLinköpings universitet, Filosofiska fakulteten2016Alzheimer'sheterogeneityatrophyclusteringBayesianmorphometrydimensionThis 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. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129923application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Alzheimer's
heterogeneity
atrophy
clustering
Bayesian
morphometry
dimension
spellingShingle Alzheimer's
heterogeneity
atrophy
clustering
Bayesian
morphometry
dimension
Poulakis, Konstantinos
Alzheimer's disease heterogeneity assessment using high dimensional clustering techniques
description 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.
author Poulakis, Konstantinos
author_facet Poulakis, Konstantinos
author_sort Poulakis, Konstantinos
title Alzheimer's disease heterogeneity assessment using high dimensional clustering techniques
title_short Alzheimer's disease heterogeneity assessment using high dimensional clustering techniques
title_full Alzheimer's disease heterogeneity assessment using high dimensional clustering techniques
title_fullStr Alzheimer's disease heterogeneity assessment using high dimensional clustering techniques
title_full_unstemmed Alzheimer's disease heterogeneity assessment using high dimensional clustering techniques
title_sort alzheimer's disease heterogeneity assessment using high dimensional clustering techniques
publisher Linköpings universitet, Statistik
publishDate 2016
url http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129923
work_keys_str_mv AT poulakiskonstantinos alzheimersdiseaseheterogeneityassessmentusinghighdimensionalclusteringtechniques
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