Biomarkers Derived from Alterations in Overlapping Community Structure of Resting-state Brain Functional Networks for Detecting Alzheimer's Disease. (21st February 2022)

• Record Type: Journal Article
• Authors: Han, Hongfang; Li, Xuan; Gan, John Q.; Yu, Hua; Wang, Haixian
• Is Part Of: Neuroscience. Volume 484(2022)
• Page Start: 38
• Page End: 52
• Publication Date: 2022-02-21
• Subjects: Alzheimer's disease -- overlapping community structure -- brain functional network -- resting-state fMRI -- machine learing -- agglomerative hierarchical clustering; Neurochemistry -- Periodicals; Neurophysiology -- Periodicals; Neurology -- Periodicals; Neurochimie -- Périodiques; Neurophysiologie -- Périodiques; Neurochemistry; Neurophysiology; Electronic journals; Periodicals; Electronic journals; 612.8
• DOI: 10.1016/j.neuroscience.2021.12.031
• Languages: English
• ISSNs: 0306-4522

Abstract

Highlights: The overlapping community structure is detected using the cssNMF method with NASR. Alterations in the overlapping community structure of the two groups are studied. The hierarchy of communities detected at different scales is investigated. A machine learning framework proposed for AD detection can achieve high accuracy. Abstract: Recent studies show that overlapping community structure is an important feature of the brain functional network. However, alterations in such overlapping community structure in Alzheimer's disease (AD) patients have not been examined yet. In this study, we investigate the overlapping community structure in AD by using resting-state functional magnetic resonance imaging (rs-fMRI) data. The collective sparse symmetric non-negative matrix factorization (cssNMF) is adopted to detect the overlapping community structure. Experimental results on 28 AD patients and 32 normal controls (NCs) from the ADNI2 dataset show that the two groups have remarkable differences in terms of the optimal number of communities, the hierarchy of communities detected at different scales, network functional segregation, and nodal functional diversity. In particular, the frontal-parietal and basal ganglia networks exhibit significant differences between the two groups. A machine learning framework proposed in this paper for AD detection achieved an accuracy of 76.7% when using the detected community strengths of the frontal-parietal and basal ganglia networks only as input features. These findings provide novel insights into the understanding of pathological changes in the brain functional network organization of AD and show the potential of the community structure-related features for AD detection.