Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism

• Published in: PloS one Vol. 10; no. 6; p. e0129250
• Main Authors: Yun, Hyuk Jin, Kwak, Kichang, Lee, Jong-Min
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.06.2015; Public Library of Science (PLoS)
• Subjects: Accuracy; Aged; Aged, 80 and over; Aging; Alzheimer Disease - diagnosis; Alzheimer Disease - metabolism; Alzheimer Disease - pathology; Alzheimer Disease - physiopathology; Alzheimer's disease; Atrophy; Biomedical engineering; Brain; Brain - metabolism; Brain - pathology; Classification; Cognitive ability; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - metabolism; Cortex; Dementia; Diagnosis; Discriminant analysis; Engineering; Female; Fluorodeoxyglucose F18 - metabolism; Glucose; Glucose - metabolism; Humans; Hypometabolism; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical imaging; Metabolism; Middle Aged; Multimodal Imaging - methods; Neurology; Neurosciences; NMR; Nuclear magnetic resonance; Pathology; Physiological aspects; Positron emission; Positron emission tomography; Positron-Emission Tomography - methods; Radiopharmaceuticals - metabolism; Sensitivity; Sensitivity and Specificity; Tomography; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0129250

Structural MR image (MRI) and 18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer's disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET.