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

Using fMRI, a Node Significance Score (NSS) was developed to identify brain regions within the Default Mode Network (DMN) affected by Mild Cognitive Impairment (MCI). Subject-specific DMN graphs were built from partial correlations among regions of interest (ROIs). Four community detection algorithms identified the largest sub-community, and NSS ratings captured each node's frequency and occurrence in that sub-community across healthy and MCI subjects. Score disparities exceeding 20% were found for 10 DMN nodes, with the posterior cingulate cortex (PCC) and fusiform gyrus showing the largest disparities (45.69% and 43.08%). These quantitative measures align with prior literature and may guide targeted treatment strategies.

Study at a glance

Abstract

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.