Classification of Alzheimer Disease, Mild Cognitive Impairment, and Normal Cognitive Status with Large-Scale Network Analysis Based on Resting-State Functional MR Imaging

• Published in: Radiology Vol. 259; no. 1; pp. 213 - 221
• Main Authors: Chen, Gang, Ward, B. Douglas, Xie, Chunming, Li, Wenjun, Wu, Zhilin, Jones, Jennifer L., Franczak, Malgorzata, Antuono, Piero, Li, Shi-Jiang
• Format: Journal Article
• Language: English
• Published: Oak Brook, IL Radiological Society of North America 01.04.2011; Radiological Society of North America, Inc
• Subjects: Adult and adolescent clinical studies; Aged; Algorithms; Alzheimer Disease - complications; Alzheimer Disease - diagnosis; Alzheimer's disease; Biological and medical sciences; Brain; Brain Mapping - methods; Classification; Cognition Disorders - diagnosis; Cognition Disorders - etiology; Cognitive ability; Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases; Differentiation; Female; Functional magnetic resonance imaging; Geriatrics; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Medical sciences; Neural networks; Neural Networks (Computer); Neurodegenerative diseases; Neurology; Organic mental disorders. Neuropsychology; Original Research; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Regression analysis; Reproducibility of Results; Rest; Sensitivity and Specificity; Human; Nervous system diseases; Cognitive disorder; Alzheimer disease; Nuclear magnetic resonance imaging; Cerebral disorder; Central nervous system disease; Classification; Medical imagery; Degenerative disease; mild cognitive impairment; Diagnosis; Functional imaging
• ISSN: 0033-8419; 1527-1315; 1527-1315
• DOI: 10.1148/radiol.10100734

To use large-scale network (LSN) analysis to classify subjects with Alzheimer disease (AD), those with amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) subjects. The study was conducted with institutional review board approval and was in compliance with HIPAA regulations. Written informed consent was obtained from each participant. Resting-state functional magnetic resonance (MR) imaging was used to acquire the voxelwise time series in 55 subjects with clinically diagnosed AD (n = 20), aMCI (n =15), and normal cognitive function (n = 20). The brains were divided into 116 regions of interest (ROIs). The Pearson product moment correlation coefficients of pairwise ROIs were used to classify these subjects. Error estimation of the classifications was performed with the leave-one-out cross-validation method. Linear regression analysis was performed to analyze the relationship between changes in network connectivity strengths and behavioral scores. The area under the receiver operating characteristic curve (AUC) yielded 87% classification power, 85% sensitivity, and 80% specificity between the AD group and the non-AD group (subjects with aMCI and CN subjects) in the first-step classification. For differentiation between subjects with aMCI and CN subjects, AUC was 95%; sensitivity, 93%; and specificity, 90%. The decreased network indexes were significantly correlated with the Mini-Mental State Examination score in all tested subjects. Similarly, changes in network indexes significantly correlated with Rey Auditory Verbal Leaning Test delayed recall scores in subjects with aMCI and CN subjects. LSN analysis revealed that interconnectivity patterns of brain regions can be used to classify subjects with AD, those with aMCI, and CN subjects. In addition, the altered connectivity networks were significantly correlated with the results of cognitive tests.