Cognitive and MRI trajectories for prediction of Alzheimer’s disease

Abstract The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI database to investigate how well normal functio...

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Autores principales:	Samaneh A. Mofrad, Astri J. Lundervold, Alexandra Vik, Alexander S. Lundervold
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7734fb18c5aa4cc29e29e085711bc4c0
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spelling	oai:doaj.org-article:7734fb18c5aa4cc29e29e085711bc4c02021-12-02T13:48:53ZCognitive and MRI trajectories for prediction of Alzheimer’s disease10.1038/s41598-020-78095-72045-2322https://doaj.org/article/7734fb18c5aa4cc29e29e085711bc4c02021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78095-7https://doaj.org/toc/2045-2322Abstract The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI database to investigate how well normal function (HC, n= 134) vs. conversion to MCI (cMCI, n= 134) and stable MCI (sMCI, n=333) vs. conversion to AD (cAD, n= 333) could be predicted from cognitive tests, and whether the predictions improve by adding information from magnetic resonance imaging (MRI) examinations. Features representing trajectories of change in the selected cognitive and MRI measures were derived from mixed effects models and used to train ensemble machine learning models to classify the pairs of subgroups based on a subset of the data set. Evaluation in an independent test set showed that the predictions for HC vs. cMCI improved substantially when MRI features were added, with an increase in $$F_1$$ F 1 -score from 60 to 77%. The $$F_1$$ F 1 -scores for sMCI vs. cAD were 77% without and 78% with inclusion of MRI features. The results are in-line with findings showing that cognitive changes tend to manifest themselves several years after the Alzheimer’s disease is well-established in the brain.Samaneh A. MofradAstri J. LundervoldAlexandra VikAlexander S. LundervoldNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Samaneh A. Mofrad Astri J. Lundervold Alexandra Vik Alexander S. Lundervold Cognitive and MRI trajectories for prediction of Alzheimer’s disease
description	Abstract The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI database to investigate how well normal function (HC, n= 134) vs. conversion to MCI (cMCI, n= 134) and stable MCI (sMCI, n=333) vs. conversion to AD (cAD, n= 333) could be predicted from cognitive tests, and whether the predictions improve by adding information from magnetic resonance imaging (MRI) examinations. Features representing trajectories of change in the selected cognitive and MRI measures were derived from mixed effects models and used to train ensemble machine learning models to classify the pairs of subgroups based on a subset of the data set. Evaluation in an independent test set showed that the predictions for HC vs. cMCI improved substantially when MRI features were added, with an increase in $$F_1$$ F 1 -score from 60 to 77%. The $$F_1$$ F 1 -scores for sMCI vs. cAD were 77% without and 78% with inclusion of MRI features. The results are in-line with findings showing that cognitive changes tend to manifest themselves several years after the Alzheimer’s disease is well-established in the brain.
format	article
author	Samaneh A. Mofrad Astri J. Lundervold Alexandra Vik Alexander S. Lundervold
author_facet	Samaneh A. Mofrad Astri J. Lundervold Alexandra Vik Alexander S. Lundervold
author_sort	Samaneh A. Mofrad
title	Cognitive and MRI trajectories for prediction of Alzheimer’s disease
title_short	Cognitive and MRI trajectories for prediction of Alzheimer’s disease
title_full	Cognitive and MRI trajectories for prediction of Alzheimer’s disease
title_fullStr	Cognitive and MRI trajectories for prediction of Alzheimer’s disease
title_full_unstemmed	Cognitive and MRI trajectories for prediction of Alzheimer’s disease
title_sort	cognitive and mri trajectories for prediction of alzheimer’s disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7734fb18c5aa4cc29e29e085711bc4c0
work_keys_str_mv	AT samanehamofrad cognitiveandmritrajectoriesforpredictionofalzheimersdisease AT astrijlundervold cognitiveandmritrajectoriesforpredictionofalzheimersdisease AT alexandravik cognitiveandmritrajectoriesforpredictionofalzheimersdisease AT alexanderslundervold cognitiveandmritrajectoriesforpredictionofalzheimersdisease
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Cognitive and MRI trajectories for prediction of Alzheimer’s disease

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