Caparelli Elisabeth C, Gu Hong, Yang Yihong
Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA.
Brain Connect. 2025 Feb;15(1):19-29. doi: 10.1089/brain.2023.0090. Epub 2024 Dec 10.
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. 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. 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. 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.
基于模块性的社区结构概念被广泛用于解决许多系统级别的问题。然而，其基于模块性指数Q最大化的算法存在退化问题，会产生一组不同的可能解决方案。在这项工作中，我们探讨了模块性原理在静息态功能磁共振成像（rsfMRI）数据中的退化效应，该原理被用于利用人类连接组计划的数据对扣带回进行分区。我们提出了一种新的迭代方法来解决这一局限性。我们的结果表明，当重复这些算法时，当前的模块性方法会提供各种不同的解决方案，导致扣带回的细分数量不同。然而，我们新提出的方法克服了这一局限性，并为最终分区生成了更稳定的解决方案。通过这种新方法，我们能够减轻退化问题，并提供一种工具，以便在将模块性应用于rsfMRI数据时以更可靠的方式使用它。
