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Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment

The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whet...

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Autores principales: Martínez-Torteya, Antonio, Treviño, Víctor, Tamez-Peña, José G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464003/
https://www.ncbi.nlm.nih.gov/pubmed/26106620
http://dx.doi.org/10.1155/2015/961314
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author Martínez-Torteya, Antonio
Treviño, Víctor
Tamez-Peña, José G.
author_facet Martínez-Torteya, Antonio
Treviño, Víctor
Tamez-Peña, José G.
author_sort Martínez-Torteya, Antonio
collection PubMed
description The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers. For this, Alzheimer's Disease Neuroimaging Initiative database was mined for case-control candidates. At least 652 baseline features extracted from MRI and PET analyses, biological samples, and clinical data up to February 2014 were used. A feature selection methodology that includes a genetic algorithm search coupled to a logistic regression classifier and forward and backward selection strategies was used to explore combinations of features. This generated diagnostic models with sizes ranging from 3 to 8, including well documented AD biomarkers, as well as unexplored image, biochemical, and clinical features. Accuracies of 0.85, 0.79, and 0.80 were achieved for HC-AD, HC-MCI, and MCI-AD classifications, respectively, when evaluated using a blind test set. In conclusion, a set of features provided additional and independent information to well-established AD biomarkers, aiding in the classification of MCI and AD.
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spelling pubmed-44640032015-06-23 Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment Martínez-Torteya, Antonio Treviño, Víctor Tamez-Peña, José G. Biomed Res Int Research Article The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) is very important for treatment research and patient care purposes. Few biomarkers are currently considered in clinical settings, and their use is still optional. The objective of this work was to determine whether multimodal and nonpreviously AD associated features could improve the classification accuracy between AD, MCI, and healthy controls, which may impact future AD biomarkers. For this, Alzheimer's Disease Neuroimaging Initiative database was mined for case-control candidates. At least 652 baseline features extracted from MRI and PET analyses, biological samples, and clinical data up to February 2014 were used. A feature selection methodology that includes a genetic algorithm search coupled to a logistic regression classifier and forward and backward selection strategies was used to explore combinations of features. This generated diagnostic models with sizes ranging from 3 to 8, including well documented AD biomarkers, as well as unexplored image, biochemical, and clinical features. Accuracies of 0.85, 0.79, and 0.80 were achieved for HC-AD, HC-MCI, and MCI-AD classifications, respectively, when evaluated using a blind test set. In conclusion, a set of features provided additional and independent information to well-established AD biomarkers, aiding in the classification of MCI and AD. Hindawi Publishing Corporation 2015 2015-05-28 /pmc/articles/PMC4464003/ /pubmed/26106620 http://dx.doi.org/10.1155/2015/961314 Text en Copyright © 2015 Antonio Martínez-Torteya et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Martínez-Torteya, Antonio
Treviño, Víctor
Tamez-Peña, José G.
Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment
title Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment
title_full Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment
title_fullStr Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment
title_full_unstemmed Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment
title_short Improved Diagnostic Multimodal Biomarkers for Alzheimer's Disease and Mild Cognitive Impairment
title_sort improved diagnostic multimodal biomarkers for alzheimer's disease and mild cognitive impairment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4464003/
https://www.ncbi.nlm.nih.gov/pubmed/26106620
http://dx.doi.org/10.1155/2015/961314
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