The concept of community structure, based on modularity, is widely used to address many systems-level queries. However, its algorithm, based on the maximization of the modularity index Q, suffers from degeneracy problem, which yields a set of different possible solutions.
Methods:
In this work, we explored the degeneracy effect of modularity principle on resting-state functional magnetic resonance imaging (rsfMRI) data, when it is used to parcellate the cingulate cortex using data from the Human Connectome Project. We proposed a new iterative approach to address this limitation.
Results:
Our results show that current modularity approaches furnish a variety of different solutions, when these algorithms are repeated, leading to different number of subdivisions for the cingulate cortex. Our new proposed method, however, overcomes this limitation and generates more stable solution for the final partition.
Conclusion:
With this new method, we were able to mitigate the degeneracy problem and offer a tool to use modularity in a more reliable manner, when applying it to rsfMRI data.
Impact Statement
This work highlights the limitations introduced by the modular degeneracy when using the current modularity approaches for brain parcellation on fMRI data. We present a new methodology that produces more coherent modularity results when it is applied to neuroimaging data.
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