TY - JOUR
T1 - Autosomal dominantly inherited alzheimer disease
T2 - Analysis of genetic subgroups by machine learning
AU - Castillo-Barnes, Diego
AU - Su, Li
AU - Ramírez, Javier
AU - Salas-Gonzalez, Diego
AU - Martinez-Murcia, Francisco J.
AU - Illan, Ignacio A.
AU - Segovia, Fermin
AU - Ortiz, Andres
AU - Cruchaga, Carlos
AU - Farlow, Martin R.
AU - Xiong, Chengjie
AU - Graff-Radford, Neil R.
AU - Schofield, Peter R.
AU - Masters, Colin L.
AU - Salloway, Stephen
AU - Jucker, Mathias
AU - Mori, Hiroshi
AU - Levin, Johannes
AU - Gorriz, Juan M.
AU - (DIAN), Dominantly Inherited Alzheimer Network
N1 - Funding Information:
This work was supported by the MINECO/FEDER under the TEC2015-64718-R and RTI2018-098913-B-I00 projects and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Project P11-TIC-7103. LS is supported by Alzheimer's Research UK Senior Research Fellowship (ARUK-SRF2017B-1). HM is supported by Japan AMED. Data collection and sharing for this project was supported by The Dominantly Inherited Alzheimer's Network (DIAN, U19AG032438) funded by the National Institute on Aging (NIA), the German Center for Neurodegenerative Diseases (DZNE), Raul Carrea Institute for Neurological Research (FLENI), Partial support by the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development, AMED, and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI). This manuscript has been reviewed by DIAN Study investigators for scientific content and consistency of data interpretation with previous DIAN Study publications. We acknowledge the altruism of the participants and their families and contributions of the DIAN research and support staff at each of the participating sites for their contributions to this study.
Funding Information:
HM is supported by Japan AMED.
Funding Information:
LS is supported by Alzheimer’s Research UK Senior Research Fellowship ( ARUK-SRF2017B-1 ).
Funding Information:
This work was supported by the MINECO / FEDER under the TEC2015-64718-R and RTI2018-098913-B-I00 projects and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía under the Excellence Project P11-TIC-7103 .
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/6
Y1 - 2020/6
N2 - 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.
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.
KW - Alzheimer's disease (AD)
KW - DIAN
KW - Dominantly-inherited Alzheimer's disease (DIAD)
KW - Machine learning
KW - Neuroimaging
UR - http://www.scopus.com/inward/record.url?scp=85077807763&partnerID=8YFLogxK
U2 - 10.1016/j.inffus.2020.01.001
DO - 10.1016/j.inffus.2020.01.001
M3 - Article
C2 - 32284705
AN - SCOPUS:85077807763
SN - 1566-2535
VL - 58
SP - 153
EP - 167
JO - Information Fusion
JF - Information Fusion
ER -