Comparison method for community detection on brain networks from neuroimaging data

Fumihiko Taya; Joshua de Souza; Nitish V Thakor; Anastasios Bezerianos

doi:10.1007/s41109-016-0007-y

Comparison method for community detection on brain networks from neuroimaging data

Appl Netw Sci. 2016;1(1):8. doi: 10.1007/s41109-016-0007-y. Epub 2016 Aug 16.

Authors

Fumihiko Taya^{1

2}, Joshua de Souza¹, Nitish V Thakor^{1

3

4

5}, Anastasios Bezerianos^{1

6}

Affiliations

¹ 1Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, 28 Medical Drive, #05-Cor, 117456 Singapore, Singapore.
² 2Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (ASTAR), Singapore, Singapore.
³ 3Department of Electrical & Computer Engineering, National University of Singapore, Singapore, Singapore.
⁴ 4Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
⁵ 5Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA.
⁶ 6School of Medicine, University of Patras, Patras, Greece.

Abstract

The brain is a complex system consisting of regions dedicated to different brain functions, and higher cognitive functions are realized via information flow between distant brain areas communicating with each other. As such, it is natural to shift towards brain network analysis from mapping of brain functions, for deeper understanding of the brain system. The graph theoretical network metrics measure global or local properties of network topology, but they do not provide any information about the intermediate scale of the network. Community structure analysis is a useful approach to investigate the mesoscale organization of brain network. However, the community detection schemes are yet to be established. In this paper, 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. We also discuss further issues on community detection using the proposed method.

Keywords: Brain atlases; Brain network; Community detection; Functional Magnetic Resonance Imaging (fMRI); Multiple-subject data; Normalized Mutual Information (NMI); Permutation test; “Ground truth”.