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

• Published in: PloS one Vol. 6; no. 10; p. e25446
• Main Authors: Wolz, Robin, Julkunen, Valtteri, Koikkalainen, Juha, Niskanen, Eini, Zhang, Dong Ping, Rueckert, Daniel, Soininen, Hilkka, Lötjönen, Jyrki
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.10.2011; Public Library of Science (PLoS)
• Subjects: Accuracy; Activities of daily living; Advertising executives; Aged; Alzheimer Disease - diagnosis; Alzheimer's disease; Alzheimers disease; Biomarkers; Brain; Brain research; Case-Control Studies; Cerebral Cortex - pathology; Classification; Cognitive ability; Cognitive Dysfunction - diagnosis; Cortex; Development and progression; Discriminant analysis; Engineering; Female; Finite element analysis; Hippocampus; Hospitals; Humans; Image classification; Machine learning; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Manifolds (mathematics); Medical diagnosis; Medical imaging; Medical research; Medicine; Methods; Morphometry; Multivariate analysis; Neurodegenerative diseases; Neuroimaging; Neurology; NMR; Nuclear magnetic resonance; Parameter estimation; Regression analysis; Sensitivity; Support vector machines; Finland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0025446

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.