2026-04-13T06:26:11Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/492582025-12-21T20:46:57Zcom_2072_6col_2072_452954
RECERCAT
author
Pattarone, Natalia Karina
2021-12-20T11:47:03Z2021-12-20T11:47:03Z2021-07
Treball fi de màster de: Master in Intelligent Interactive SystemsTutor: Gemma PiellaAlzheimer’s disease (AD) is clinically highly heterogeneous, varying in terms of
rates of progression, test and cognitive symptoms among patients, as well as from
a neuroimaging perspective. In the datasets provided by The Alzheimer’s Disease
Neuroimaging Initiative (ADNI), researchers collect, validate and utilize data, including
MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers
as predictors of the disease. Data coming from these datasets allow discovering
phenotypes that could help to better understand the disease and provide targeted
treatment.
The objective of this thesis is to identify data-driven phenotypes using manifold
learning and unsupervised clustering on multimodal longitudinal imaging and nonimaging
data. First, we apply a novel approach for dimensionality reduction called
PHATE that captures both local and global nonlinear structure using an informationgeometric
distance between datapoints that would facilitate the discovery of possible
AD phenotypes. Over PHATE output space, we performed a multiple-kernel unsupervised
clustering to obtain profiles and describe AD phenotypes where features are
weighted to construct kernels. Our results show that our approach can reveal AD
progression trajectories in a lower dimensionality space, improving the results of the
profiling where we obtained 4 possible profile subgroups using MRI cross-sectional
baseline data and 8 possible profile subgroups when using longitudinal data. Furthermore,
longitudinal data established clearer separation among profiles and higher
significance for cognitive tests and general volumetric cerebral values than baseline
data. Identifying these profiles could be useful for more personalized treatment of
such a heterogeneous disease as AD.
© Tots els drets reservats info:eu-repo/semantics/openAccess
MRI
Imaging Techniques
Alzheimer
Manifolds
Longitudinal Data
Cross-sectional Data
Understanding Alzheimer’s disease progression through phenotypes discovery using manifold learning techniques
info:eu-repo/semantics/masterThesis