Individuals with mild cognitive impairment (MCI) of different subtypes show distinct
alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by …

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 …

Mild cognitive impairment (MCI) has received increasing attention not only because of its
potential as a precursor for Alzheimer's disease but also as a predictor of conversion to other …

The purpose of our study was to investigate whether the whole‐brain functional connectivity
pattern exhibits disease severity‐related alterations in patients with Alzheimer's disease …

Mild cognitive impairment (MCI) was recognized as the prodromal stage of Alzheimer's
disease (AD). Recent neuroimaging studies have shown that the cognitive and memory …

Regional volume atrophy and functional degeneration are key imaging hallmarks of
Alzheimer's disease (AD) in structural and functional magnetic resonance imaging (MRI) …

A structural covariance network (SCN) has been used successfully in structural magnetic
resonance imaging (sMRI) studies. However, most SCNs have been constructed by a …

Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network
properties are unclear. We analyzed data from two independent datasets, including 166 …

While machine learning approaches to analyzing Alzheimer disease connectome
neuroimaging data have been studied, many have limited ability to provide insight in …

Mild cognitive impairment (MCI) is often considered a critical time window for predicting
early conversion to Alzheimer's disease (AD). Brain functional connectome data (ie …