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Implementation of the concepts expressed in the article (Comparison method for community detection on brain networks from neuroimaging data)

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Comparison-method-for-community-detection-on-brain-networks-from-neuroimaging-data

Implementation of the concepts expressed in the article (Comparison method for community detection on brain networks from neuroimaging data)

Abstract

we propose a method to compare different community detection schemes for neuroimaging data from multiple subjects. To the best of our knowledge, our method is the first attempt to evaluate community detection from multiple-subject data without “ground truth” community and any assumptions about the original network features. To show its feasibility, three community detection algorithms and three different brain atlases were examined using resting-state fMRI functional networks. As it is crucial to find a single group-based community structure as a representative for a group of subjects to allow discussion about brain areas and connections in different conditions on common ground, a number of community detection schemes based on different approaches have been proposed. A non-parametric permutation test on similarity between group-based community structures and individual community structures was used to determine which algorithm or atlas provided the best representative structure of the group. The Normalized Mutual Information (NMI) was computed to measure the similarity between the community structures

image The processing steps of the community detection analysis. For each subject, resting-state fMRI data were acquired (a) and parcellated into ROIs with a brain atlas (AAL, HOA or Dosenbach atlas) (b) to get time-series data of brain activity (c). A correlation matrix was obtained from the time-series data (d) and a network graph was represented by a connectivity matrix thresholded at a density level (e). Then, a group�based community structure was detected with a community detection algorithm (VTS, IS or GA approach) (f). To compare the group-based structure with individual structures, Normalized Mutual Information (NMI) was computed for every pair of the group-based and individual communities (g). The individual communities were detected with Louvian algorithm. The obtained NMI values were subjected to the permutation test to determine which algorithm or atlas provided the best representative of the group

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