Our study aims to utilize fMRI to identify the affected brain regions within the Default Mode Network (DMN) in subjects with Mild Cognitive Impairment (MCI), using a novel Node Significance Score (NSS). We construct subject-specific DMN graphs by employing partial correlation of Regions of Interest (ROIs) that make-up the DMN. For the DMN graph, ROIs are the nodes and edges are determined based on partial correlation. Four popular community detection algorithms (Clique Percolation Method (CPM), Louvain algorithm, Greedy Modularity and Leading Eigenvectors) are applied to determine the largest sub-community. NSS ratings are derived for each node, considering (I) frequency in the largest sub-community within a class across all subjects and (II) occurrence in the largest sub-community according to all four methods. After computing the NSS of each ROI in both healthy and MCI subjects, we quantify the score disparity to identify nodes most impacted by MCI. The results reveal a disparity exceeding 20% for 10 DMN nodes, maximally for PCC and Fusiform, showing 45.69% and 43.08% disparity. This aligns with existing medical literature, additionally providing a quantitative measure that enables the ordering of the affected ROIs. These findings offer valuable insights and could lead to treatment strategies aggressively targeting the affected nodes.

翻译：本研究旨在利用功能磁共振成像（fMRI），通过一种新颖的节点显著性评分（Node Significance Score, NSS），识别轻度认知障碍（Mild Cognitive Impairment, MCI）受试者默认模式网络（Default Mode Network, DMN）中受影响的脑区。我们采用构成DMN的兴趣区域（Regions of Interest, ROIs）之间的偏相关方法，构建受试者特异性DMN图。在该DMN图中，ROIs作为节点，边则基于偏相关确定。应用四种流行的社区检测算法（团渗透法（CPM）、Louvain算法、贪婪模块度算法和主导特征向量法）来确定最大子社区。针对每个节点，考虑（I）该节点在某一类别所有受试者中出现在最大子社区内的频率，以及（II）该节点在所有四种方法下均出现在最大子社区中的情况，推导出NSS评分。在计算健康受试者和MCI受试者每个ROI的NSS后，我们量化评分差异，以识别受MCI影响最显著的节点。结果显示，有10个DMN节点的差异超过20%，其中后扣带回（PCC）和梭状回（Fusiform）的差异最大，分别达到45.69%和43.08%。这一发现与现有医学文献相符，并提供了量化指标，从而能够对受影响ROI进行排序。这些结果提供了有价值的见解，可能有助于制定针对受影响节点的激进治疗策略。