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-, 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 |
| Journal | Journal of Alzheimer's Disease |
| Volume | 68 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 23 Apr 2019 |
| MoE publication type | A1 Journal article-refereed |
Fingerprint
Keywords
- Alzheimer's disease
- Atrophy
- Decision support techniques
- Disease progression
- Hippocampus
- Magnetic resonance imaging
- Regression analysis
- Statistical models
Cite this
}
Multivariate prediction of Hippocampal atrophy in Alzheimer's disease. / Liedes, Hilkka; Lötjönen, Jyrki; Kortelainen, Juha M.; Novak, Gerald; van Gils, Mark; Gordon, Mark Forrest.
In: Journal of Alzheimer's Disease, Vol. 68, No. 4, 23.04.2019, p. 1453-1468.Research output: Contribution to journal › Article › Scientific › peer-review
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 - project 121842
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-, 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-, 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 -