TY - JOUR
T1 - Multivariate prediction of Hippocampal atrophy in Alzheimer's disease
AU - Liedes, Hilkka
AU - Lötjönen, Jyrki
AU - Kortelainen, Juha M.
AU - Novak, Gerald
AU - van Gils, Mark
AU - Gordon, Mark Forrest
N1 - Funding Information:
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimag-ing Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Neu-roRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
Funding Information:
This work was carried out in the project European Medical Information Framework (EMIF) which receives support from the Innovative Medicines Initiative Joint Undertaking (IMI-JU) under grant agreement n◦ 115372, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and the European Federation of Pharmaceutical Industries and Associations’ (EFPIA) in kind contribution.
Publisher Copyright:
© 2019 - IOS Press and the authors. All rights reserved.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/4/23
Y1 - 2019/4/23
N2 - BACKGROUND: Hippocampal atrophy (HA) is one of the biomarkers for Alzheimer's disease (AD).OBJECTIVE: To identify the best biomarkers and develop models for prediction of HA over 24 months using baseline data.METHODS: The study included healthy elderly controls, subjects with mild cognitive impairment, and subjects with AD, obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) databases. Predictor variables included cognitive and neuropsychological tests, amyloid-β, tau, and p-tau from cerebrospinal fluid samples, apolipoprotein E, and features extracted from magnetic resonance images (MRI). Least-mean-squares regression with elastic net regularization and least absolute deviation regression models were tested using cross-validation in ADNI 1. The generalizability of the models including only MRI features was evaluated by training the models with ADNI 1 and testing them with AIBL. The models including the full set of variables were not evaluated with AIBL because not all needed variables were available in it.RESULTS: The models including the full set of variables performed better than the models including only MRI features (root-mean-square error (RMSE) 1.76-1.82 versus 1.93-2.08). The MRI-only models performed well when applied to the independent validation cohort (RMSE 1.66-1.71). In the prediction of dichotomized HA (fast versus slow), the models achieved a reasonable prediction accuracy (0.79-0.87).CONCLUSIONS: These models can potentially help identifying subjects predicted to have a faster HA rate. This can help in selection of suitable patients into clinical trials testing disease-modifying drugs for AD.
AB - BACKGROUND: Hippocampal atrophy (HA) is one of the biomarkers for Alzheimer's disease (AD).OBJECTIVE: To identify the best biomarkers and develop models for prediction of HA over 24 months using baseline data.METHODS: The study included healthy elderly controls, subjects with mild cognitive impairment, and subjects with AD, obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) and the Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing (AIBL) databases. Predictor variables included cognitive and neuropsychological tests, amyloid-β, tau, and p-tau from cerebrospinal fluid samples, apolipoprotein E, and features extracted from magnetic resonance images (MRI). Least-mean-squares regression with elastic net regularization and least absolute deviation regression models were tested using cross-validation in ADNI 1. The generalizability of the models including only MRI features was evaluated by training the models with ADNI 1 and testing them with AIBL. The models including the full set of variables were not evaluated with AIBL because not all needed variables were available in it.RESULTS: The models including the full set of variables performed better than the models including only MRI features (root-mean-square error (RMSE) 1.76-1.82 versus 1.93-2.08). The MRI-only models performed well when applied to the independent validation cohort (RMSE 1.66-1.71). In the prediction of dichotomized HA (fast versus slow), the models achieved a reasonable prediction accuracy (0.79-0.87).CONCLUSIONS: These models can potentially help identifying subjects predicted to have a faster HA rate. This can help in selection of suitable patients into clinical trials testing disease-modifying drugs for AD.
KW - Alzheimer's disease
KW - Atrophy
KW - Decision support techniques
KW - Disease progression
KW - Hippocampus
KW - Magnetic resonance imaging
KW - Regression analysis
KW - Statistical models
UR - http://www.scopus.com/inward/record.url?scp=85064859223&partnerID=8YFLogxK
U2 - 10.3233/JAD-180484
DO - 10.3233/JAD-180484
M3 - Article
C2 - 30909211
AN - SCOPUS:85064859223
VL - 68
SP - 1453
EP - 1468
JO - Journal of Alzheimer's Disease
JF - Journal of Alzheimer's Disease
SN - 1387-2877
IS - 4
ER -
