Localizing and Assessing Node Significance in Default Mode Network using Sub-Community Detection in Mild Cognitive Impairment
This provides a quantitative method to localize and assess node significance in MCI for medical researchers, offering insights for potential treatment strategies, though it is incremental as it builds on existing community detection techniques.
The study tackled the problem of identifying brain regions affected by Mild Cognitive Impairment (MCI) within the Default Mode Network using fMRI and a novel Node Significance Score, revealing a disparity exceeding 20% for 10 nodes, with maximum disparities of 45.69% and 43.08% for PCC and Fusiform.
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.