Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI...

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Main Authors: Wolz, Robin, Julkunen, Valtteri, Koikkalainen, Juha, Niskanen, Eini, Zhang, Dong Ping, Rueckert, Daniel, Soininen, Hilkka, Lötjönen, Jyrki
Format: Online
Language:English
Published: Public Library of Science 2011
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3192759/
id pubmed-3192759
recordtype oai_dc
spelling pubmed-31927592011-10-21 Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease Wolz, Robin Julkunen, Valtteri Koikkalainen, Juha Niskanen, Eini Zhang, Dong Ping Rueckert, Daniel Soininen, Hilkka Lötjönen, Jyrki Research Article The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features. Public Library of Science 2011-10-13 /pmc/articles/PMC3192759/ /pubmed/22022397 http://dx.doi.org/10.1371/journal.pone.0025446 Text en Wolz 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.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Wolz, Robin
Julkunen, Valtteri
Koikkalainen, Juha
Niskanen, Eini
Zhang, Dong Ping
Rueckert, Daniel
Soininen, Hilkka
Lötjönen, Jyrki
spellingShingle Wolz, Robin
Julkunen, Valtteri
Koikkalainen, Juha
Niskanen, Eini
Zhang, Dong Ping
Rueckert, Daniel
Soininen, Hilkka
Lötjönen, Jyrki
Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
author_facet Wolz, Robin
Julkunen, Valtteri
Koikkalainen, Juha
Niskanen, Eini
Zhang, Dong Ping
Rueckert, Daniel
Soininen, Hilkka
Lötjönen, Jyrki
author_sort Wolz, Robin
title Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
title_short Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
title_full Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
title_fullStr Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
title_full_unstemmed Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease
title_sort multi-method analysis of mri images in early diagnostics of alzheimer's disease
description The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.
publisher Public Library of Science
publishDate 2011
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3192759/
_version_ 1611480608663404544

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