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

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Abstract
We have shown that large-scale network connectivity changes can be used to classify subjects with Alzheimer disease (AD), those with amnestic mild cognitive impairment, and those with normal cognitive function; this method has the potential to assist clinicians in disease assessment, to serve as a biomarker in the prediction of the risk of AD progression and to be used to monitor the efficacy of disease-modifying therapies. PurposeTo 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 MethodsThe 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.ResultsThe area...