Xue Chen, Zheng Darui, Ruan Yiming, Cao Xuan, Zhang Xulian, Qi Wenzhang, Yuan Qianqian, Liang Xuhong, Huang Qingling
Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, United States.
Front Aging Neurosci. 2024 Nov 18;16:1467054. doi: 10.3389/fnagi.2024.1467054. eCollection 2024.
Mild cognitive impairment (MCI) includes two distinct subtypes, namely progressive MCI (pMCI) and stable MCI (sMCI). The objective of this study was to identify the topological reorganization of brain functional networks in patients with pMCI and sMCI.
Resting-state functional magnetic resonance imaging (rs-fMRI) was applied to patients with pMCI, sMCI and healthy controls. Graph theory was applied to study the topological characteristics of the brain's functional networks, examining global and nodal metrics, modularity, and rich-club organization. Analysis of covariance and two sample t-tests were applied to assess differences in topological attributes between patient groups, alongside correlation analysis, which examined the value of changing topological attributes in predicting various clinical outcomes.
Significant differences between each group with regard to network metrics were observed. These included clustering coefficients and small-worldness. At the nodal level, several nodes with an abnormal degree centrality and nodal efficiency were detected. In rich club, pMCI and sMCI patients showed declined connectivity compared with HC. Significant differences were observed in the intra- and inter-module connections among the three groups. Particularly noteworthy was the irreplaceable role of the cerebellar module in network interactions.
Our study revealed significant differences in network topological properties among sMCI, pMCI and HC patients, which were significantly correlated with cognitive function. Most notably, the cerebellar module played a crucial role in the overall network interactions. In conclusion, these findings could aid in the development of imaging markers used to expedite diagnosis and intervention prior to Alzheimer's disease onset.
轻度认知障碍（MCI）包括两种不同的亚型，即进展性MCI（pMCI）和稳定性MCI（sMCI）。本研究的目的是确定pMCI和sMCI患者脑功能网络的拓扑重组。
对pMCI、sMCI患者和健康对照者应用静息态功能磁共振成像（rs-fMRI）。采用图论研究脑功能网络的拓扑特征，检查全局和节点指标、模块化和富俱乐部组织。应用协方差分析和两样本t检验评估患者组之间拓扑属性的差异，并进行相关分析，以检验拓扑属性变化在预测各种临床结果中的价值。
观察到各组在网络指标方面存在显著差异。这些差异包括聚类系数和小世界性质。在节点水平上，检测到几个度中心性和节点效率异常的节点。在富俱乐部中，与健康对照相比，pMCI和sMCI患者的连接性下降。三组之间在模块内和模块间连接方面观察到显著差异。特别值得注意的是小脑模块在网络相互作用中不可替代的作用。
我们的研究揭示了sMCI、pMCI和健康对照患者在网络拓扑属性方面存在显著差异，这些差异与认知功能显著相关。最显著的是，小脑模块在整体网络相互作用中起关键作用。总之，这些发现有助于开发成像标志物，用于在阿尔茨海默病发病前加快诊断和干预。
