Faculty Collaboration Database

Classification of Alzheimer disease, mild cognitive impairment, and normal cognitive status with large-scale network analysis based on resting-state functional MR imaging. Radiology 2011 Apr;259(1):213-21

Date

01/21/2011

Pubmed ID

21248238

Pubmed Central ID

PMC3064820

DOI

10.1148/radiol.10100734

Scopus ID

2-s2.0-79953010204 (requires institutional sign-in at Scopus site)   241 Citations

Abstract

PURPOSE: 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.

MATERIALS AND METHODS: 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.

RESULTS: 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.

CONCLUSION: 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.

Author List

Chen G, Ward BD, Xie C, Li W, Wu Z, Jones JL, Franczak M, Antuono P, Li SJ

Authors

Piero G. Antuono MD Adjunct Professor in the Neurology department at Medical College of Wisconsin
Malgorzata Franczak MD Professor in the Neurology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Aged
Algorithms
Alzheimer Disease
Brain Mapping
Cognition Disorders
Female
Humans
Image Enhancement
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging
Male
Reproducibility of Results
Rest
Sensitivity and Specificity