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Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring...

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Detalles Bibliográficos
Autores principales: Zhang, Daoqiang, Shen, Dinggang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3310854/
https://www.ncbi.nlm.nih.gov/pubmed/22457741
http://dx.doi.org/10.1371/journal.pone.0033182
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author Zhang, Daoqiang
Shen, Dinggang
author_facet Zhang, Daoqiang
Shen, Dinggang
author_sort Zhang, Daoqiang
collection PubMed
description Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra ‘group regularization’ to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.
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spelling pubmed-33108542012-03-28 Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers Zhang, Daoqiang Shen, Dinggang PLoS One Research Article Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra ‘group regularization’ to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods. Public Library of Science 2012-03-22 /pmc/articles/PMC3310854/ /pubmed/22457741 http://dx.doi.org/10.1371/journal.pone.0033182 Text en Zhang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Zhang, Daoqiang
Shen, Dinggang
Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
title Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
title_full Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
title_fullStr Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
title_full_unstemmed Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
title_short Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers
title_sort predicting future clinical changes of mci patients using longitudinal and multimodal biomarkers
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3310854/
https://www.ncbi.nlm.nih.gov/pubmed/22457741
http://dx.doi.org/10.1371/journal.pone.0033182
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