Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease

Robin Wolz, Valtteri Julkunen, Juha Koikkalainen, Eini Niskanen, Dong Ping Zhang, Daniel Rueckert, Hilkka Soininen, Jyrki Lötjönen (Corresponding Author), The Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticleScientificpeer-review

170 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article numbere25446
JournalPLoS ONE
Volume6
Issue number10
DOIs
Publication statusPublished - 2011
MoE publication typeA1 Journal article-refereed

Fingerprint

Magnetic resonance
Alzheimer disease
magnetic resonance imaging
Alzheimer Disease
Magnetic Resonance Imaging
Imaging techniques
Neuroimaging
Disease control
Discriminant analysis
Discriminant Analysis
methodology
discriminant analysis
Databases
Sensitivity and Specificity
Magnetic resonance imaging
Tensors
Support vector machines
Brain
Classifiers
morphometry

Cite this

Wolz, R., Julkunen, V., Koikkalainen, J., Niskanen, E., Zhang, D. P., Rueckert, D., ... The Alzheimer’s Disease Neuroimaging Initiative (2011). Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. PLoS ONE, 6(10), [e25446]. https://doi.org/10.1371/journal.pone.0025446
Wolz, Robin ; Julkunen, Valtteri ; Koikkalainen, Juha ; Niskanen, Eini ; Zhang, Dong Ping ; Rueckert, Daniel ; Soininen, Hilkka ; Lötjönen, Jyrki ; The Alzheimer’s Disease Neuroimaging Initiative. / Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. In: PLoS ONE. 2011 ; Vol. 6, No. 10.
@article{9bbc48ff5a0648b197bcc61accf9af01,
title = "Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease",
abstract = "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.",
author = "Robin Wolz and Valtteri Julkunen and Juha Koikkalainen and Eini Niskanen and Zhang, {Dong Ping} and Daniel Rueckert and Hilkka Soininen and Jyrki L{\"o}tj{\"o}nen and {The Alzheimer’s Disease Neuroimaging Initiative}",
year = "2011",
doi = "10.1371/journal.pone.0025446",
language = "English",
volume = "6",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "10",

}

Wolz, R, Julkunen, V, Koikkalainen, J, Niskanen, E, Zhang, DP, Rueckert, D, Soininen, H, Lötjönen, J & The Alzheimer’s Disease Neuroimaging Initiative 2011, 'Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease', PLoS ONE, vol. 6, no. 10, e25446. https://doi.org/10.1371/journal.pone.0025446

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 (Corresponding Author); The Alzheimer’s Disease Neuroimaging Initiative.

In: PLoS ONE, Vol. 6, No. 10, e25446, 2011.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease

AU - Wolz, Robin

AU - Julkunen, Valtteri

AU - Koikkalainen, Juha

AU - Niskanen, Eini

AU - Zhang, Dong Ping

AU - Rueckert, Daniel

AU - Soininen, Hilkka

AU - Lötjönen, Jyrki

AU - The Alzheimer’s Disease Neuroimaging Initiative

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

U2 - 10.1371/journal.pone.0025446

DO - 10.1371/journal.pone.0025446

M3 - Article

VL - 6

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 10

M1 - e25446

ER -

Wolz R, Julkunen V, Koikkalainen J, Niskanen E, Zhang DP, Rueckert D et al. Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease. PLoS ONE. 2011;6(10). e25446. https://doi.org/10.1371/journal.pone.0025446