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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Autores principales:	Daoqiang Zhang, Dinggang Shen, Alzheimer's Disease Neuroimaging Initiative
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c186d02b49cf450c8c8f63cc1cc95a38
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spelling	oai:doaj.org-article:c186d02b49cf450c8c8f63cc1cc95a382021-11-18T07:24:29ZPredicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.1932-620310.1371/journal.pone.0033182https://doaj.org/article/c186d02b49cf450c8c8f63cc1cc95a382012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22457741/?tool=EBIhttps://doaj.org/toc/1932-6203Accurate 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.Daoqiang ZhangDinggang ShenAlzheimer's Disease Neuroimaging InitiativePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 3, p e33182 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Daoqiang Zhang Dinggang Shen Alzheimer's Disease Neuroimaging Initiative Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.
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.
format	article
author	Daoqiang Zhang Dinggang Shen Alzheimer's Disease Neuroimaging Initiative
author_facet	Daoqiang Zhang Dinggang Shen Alzheimer's Disease Neuroimaging Initiative
author_sort	Daoqiang Zhang
title	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_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_sort	predicting future clinical changes of mci patients using longitudinal and multimodal biomarkers.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/c186d02b49cf450c8c8f63cc1cc95a38
work_keys_str_mv	AT daoqiangzhang predictingfutureclinicalchangesofmcipatientsusinglongitudinalandmultimodalbiomarkers AT dinggangshen predictingfutureclinicalchangesofmcipatientsusinglongitudinalandmultimodalbiomarkers AT alzheimersdiseaseneuroimaginginitiative predictingfutureclinicalchangesofmcipatientsusinglongitudinalandmultimodalbiomarkers
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Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers.

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