Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei 230094, Anhui, PR China.
Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany.
Neuroscience. 2022 Feb 21;484:38-52. doi: 10.1016/j.neuroscience.2021.12.031. Epub 2021 Dec 29.
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.
最近的研究表明，重叠社区结构是大脑功能网络的一个重要特征。然而，尚未研究阿尔茨海默病（AD）患者中这种重叠社区结构的改变。在这项研究中，我们使用静息态功能磁共振成像（rs-fMRI）数据来研究 AD 中的重叠社区结构。采用集体稀疏对称非负矩阵分解（cssNMF）来检测重叠社区结构。来自 ADNI2 数据集的 28 名 AD 患者和 32 名正常对照（NC）的实验结果表明，两组在最佳社区数量、不同尺度下检测到的社区层次结构、网络功能分离和节点功能多样性方面存在显著差异。特别是，额顶叶和基底节网络在两组之间存在显著差异。本文提出的一种用于 AD 检测的机器学习框架，仅使用额顶叶和基底节网络的检测到的社区强度作为输入特征，其准确率达到 76.7%。这些发现为理解 AD 大脑功能网络组织的病理变化提供了新的见解，并显示了社区结构相关特征在 AD 检测中的潜力。
