Multivariate prediction of Hippocampal atrophy in Alzheimer's disease

Hilkka Liedes; Jyrki Lötjönen; Juha M. Kortelainen; Gerald Novak; Mark van Gils; Mark Forrest Gordon

doi:10.3233/JAD-180484

Multivariate prediction of Hippocampal atrophy in Alzheimer's disease

Hilkka Liedes^*
, Jyrki Lötjönen
, Juha M. Kortelainen
, Gerald Novak
, Mark van Gils
, Mark Forrest Gordon

^*Corresponding author for this work

Combinostics Ltd.
Janssen Pharmaceuticals
Boehringer Ingelheim Pharmaceuticals, Inc.

Research output: Contribution to journal › Article › Scientific › peer-review

4 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	1453-1468
Number of pages	16
Journal	Journal of Alzheimer's Disease
Volume	68
Issue number	4
DOIs	https://doi.org/10.3233/JAD-180484
Publication status	Published - 23 Apr 2019
MoE publication type	A1 Journal article-refereed

Funding

1Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf 2Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (http://www.loni.usc.edu/ADNI). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at http://www.aibl.csiro.au. 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. 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.

Keywords

Alzheimer's disease
Atrophy
Decision support techniques
Disease progression
Hippocampus
Magnetic resonance imaging
Regression analysis
Statistical models

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10.3233/JAD-180484

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@article{041a1e9590904d7592f32e900d5047b2,

title = "Multivariate prediction of Hippocampal atrophy in Alzheimer's disease",

abstract = "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.",

keywords = "Alzheimer's disease, Atrophy, Decision support techniques, Disease progression, Hippocampus, Magnetic resonance imaging, Regression analysis, Statistical models",

author = "Hilkka Liedes and Jyrki L{\"o}tj{\"o}nen and Kortelainen, \{Juha M.\} and Gerald Novak and \{van Gils\}, Mark and Gordon, \{Mark Forrest\}",

note = "Funding Information: Data collection and sharing for this project was funded by the Alzheimer{\textquoteright}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{\textquoteright}s Association; Alzheimer{\textquoteright}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{\textquoteright}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{\textquoteright}s Seventh Framework Programme (FP7/2007-2013) and the European Federation of Pharmaceutical Industries and Associations{\textquoteright} (EFPIA) in kind contribution. Publisher Copyright: {\textcopyright} 2019 - IOS Press and the authors. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.",

year = "2019",

month = apr,

day = "23",

doi = "10.3233/JAD-180484",

language = "English",

volume = "68",

pages = "1453--1468",

journal = "Journal of Alzheimer's Disease",

issn = "1387-2877",

publisher = "IOS Press",

number = "4",

}

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 - https://www.scopus.com/pages/publications/85064859223

U2 - 10.3233/JAD-180484

DO - 10.3233/JAD-180484

M3 - Article

C2 - 30909211

AN - SCOPUS:85064859223

SN - 1387-2877

VL - 68

SP - 1453

EP - 1468

JO - Journal of Alzheimer's Disease

JF - Journal of Alzheimer's Disease

IS - 4

ER -

Multivariate prediction of Hippocampal atrophy in Alzheimer's disease

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