RT Journal Article
T1 Autosomal Dominantly Inherited Alzheimer Disease: Analysis of genetic subgroups by Machine Learning.
A1 Castillo-Barnes, Diego
A1 Su, Li
A1 Ramírez, Javier
A1 Salas-Gonzalez, Diego
A1 Martinez-Murcia, Francisco J
A1 Illan, Ignacio A
A1 Segovia, Fermin
A1 Ortiz, Andres
A1 Cruchaga, Carlos
A1 Farlow, Martin R
A1 Xiong, Chengjie
A1 Graff-Radford, Neil R
A1 Schofield, Peter R
A1 Masters, Colin L
A1 Salloway, Stephen
A1 Jucker, Mathias
A1 Mori, Hiroshi
A1 Levin, Johannes
A1 Gorriz, Juan M
A1 Dominantly Inherited Alzheimer Network (DIAN), 
K1 Alzheimer’s Disease (AD)
K1 DIAN
K1 Dominantly-Inherited Alzheimer’s Disease (DIAD)
K1 Machine Learning
K1 Neuroimaging
AB Despite subjects with Dominantly-Inherited Alzheimer's Disease (DIAD) represent less than 1% of all Alzheimer's Disease (AD) cases, the Dominantly Inherited Alzheimer Network (DIAN) initiative constitutes a strong impact in the understanding of AD disease course with special emphasis on the presyptomatic disease phase. Until now, the 3 genes involved in DIAD pathogenesis (PSEN1, PSEN2 and APP) have been commonly merged into one group (Mutation Carriers, MC) and studied using conventional statistical analysis. Comparisons between groups using null-hypothesis testing or longitudinal regression procedures, such as the linear-mixed-effects models, have been assessed in the extant literature. Within this context, the work presented here performs a comparison between different groups of subjects by considering the 3 genes, either jointly or separately, and using tools based on Machine Learning (ML). This involves a feature selection step which makes use of ANOVA followed by Principal Component Analysis (PCA) to determine which features would be realiable for further comparison purposes. Then, the selected predictors are classified using a Support-Vector-Machine (SVM) in a nested k-Fold cross-validation resulting in maximum classification rates of 72-74% using PiB PET features, specially when comparing asymptomatic Non-Carriers (NC) subjects with asymptomatic PSEN1 Mutation-Carriers (PSEN1-MC). Results obtained from these experiments led to the idea that PSEN1-MC might be considered as a mixture of two different subgroups including: a first group whose patterns were very close to NC subjects, and a second group much more different in terms of imaging patterns. Thus, using a k-Means clustering algorithm it was determined both subgroups and a new classification scenario was conducted to validate this process. The comparison between each subgroup vs. NC subjects resulted in classification rates around 80% underscoring the importance of considering DIAN as an heterogeneous entity.
SN 1566-2535
YR 2020
FD 2020-01-07
LK https://hdl.handle.net/10668/26584
UL https://hdl.handle.net/10668/26584
LA en
DS RISalud
RD Apr 5, 2025