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.

利用 fMRI 技术，我们采用全新的节点显著性评分（NSS）来确定受轻度认知障碍（MCI）影响的大脑区域（位于默认模式网络（DMN）中的节点），通过对构成 DMN 的感兴趣区（ROIs）进行部分相关性分析建立个体特定的 DMN 图。对 DMN 图，ROIs 即节点，边根据部分相关性来确定。我们应用四种常用的社区检测算法（Clique Percolation Method（CPM），Louvain 算法，Greedy Modularity 和 Leading Eigenvectors）来确定最大子社区。通过考虑（I）在一个类中的最大子社区中全部个体的频率和（II）在全部四种方法中的最大子社区中出现的情况，为每个节点计算 NSS 评分。计算了健康和 MCI 个体中每个 ROIs 的 NSS 后，我们量化了评分差异以确定 MCI 影响最大的节点，结果揭示了超过 20％的差异对于 10 个 DMN 节点，其中海马后回和颞上回的差异最大，分别达到了 45.69％和 43.08％。这与现有的医学文献相一致，并提供了一个定量的度量方法，可以对受影响的 ROIs 进行排序。这些发现提供了有价值的洞见，并可能带来针对受影响节点的积极治疗策略。

通过亚社团检测在轻度认知障碍中定位和评估默认模式网络节点的重要性

Localizing and Assessing Node Significance in Default Mode Network using  Sub-Community Detection in Mild Cognitive Impairment

Simultaneous modeling of the spatio-temporal variation patterns of brain
functional network from 4D fMRI data has been an important yet challenging
problem for the field of cognitive neuroscience and medical image analysis.
Inspired by the recent success in applying deep learning for functional brain
decoding and encoding, in this work we propose a spatio-temporal convolutional
neural network (ST-CNN)to jointly learn the spatial and temporal patterns of
targeted network from the training data and perform automatic, pin-pointing
functional network identification. The proposed ST-CNN is evaluated by the task
of identifying the Default Mode Network (DMN) from fMRI data. Results show that
while the framework is only trained on one fMRI dataset,it has the sufficient
generalizability to identify the DMN from different populations of data as well
as different cognitive tasks. Further investigation into the results show that
the superior performance of ST-CNN is driven by the jointly-learning scheme,
which capture the intrinsic relationship between the spatial and temporal
characteristic of DMN and ensures the accurate identification.

使用 ST-CNN 算法对 4D fMRI 数据进行空间 - 时间建模，并自动准确定位功能网络，ST-CNN 通过联合学习空间和时间模式来确保准确识别 Default Mode Network（DMN）。