Localizing and Assessing Node Significance in Default Mode Network using
  Sub-Community Detection in Mild Cognitive Impairment
Ameiy Acharya Chakka Sai Pradeep Neelam Sinha
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.