Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China.
Department of Psychology and Centre for Psychosocial Health, The Education University of Hong Kong, Hong Kong, China.
Neuroimage. 2018 Nov 1;181:430-445. doi: 10.1016/j.neuroimage.2018.07.019. Epub 2018 Jul 11.
A wealth of research on resting-state functional MRI (R-fMRI) data has revealed modularity as a fundamental characteristic of the human brain functional network. The modular structure has recently been suggested to be overlapping, meaning that a brain region may engage in multiple modules. However, not only the overlapping modular structure remains inconclusive, the topological features and functional roles of overlapping regions are also poorly understood. To address these issues, the present work utilized the maximal-clique based multiobjective evolutionary algorithm to explore the overlapping modular structure of the R-fMRI data obtained from 57 young healthy adults. Without prior knowledge, brain regions were optimally grouped into eight modules with wide overlap. Based on the topological features captured by graph theory analyses, overlapping regions were classified into an integrated club and a dominant minority club through clustering. Functional flexibility analysis found that overlapping regions in both clubs were significantly more flexible than non-overlapping ones. Lesion simulations revealed that targeted attack at overlapping regions were more damaging than random failure or even targeted attack at hub regions. In particular, overlapping regions in the dominant minority club were more flexible and more crucial for information communication than the others were. Together, our findings demonstrated the highly organized overlapping modular architecture and revealed the importance as well as complexity of overlapping regions from both topological and functional aspects, which provides important implications for their roles in executing multiple tasks and maintaining information communication.
大量关于静息态功能磁共振成像 (R-fMRI) 数据的研究揭示了模块性是人类大脑功能网络的基本特征。最近有人提出，模块结构具有重叠性，也就是说一个大脑区域可能参与多个模块。然而，不仅重叠模块结构仍不确定，重叠区域的拓扑特征和功能作用也知之甚少。为了解决这些问题，本研究利用基于最大团的多目标进化算法，探索了 57 名年轻健康成年人的 R-fMRI 数据的重叠模块结构。在没有先验知识的情况下，大脑区域被最优地划分为八个具有广泛重叠的模块。基于图论分析捕捉到的拓扑特征，通过聚类将重叠区域分为一个集成俱乐部和一个主导少数派俱乐部。功能灵活性分析发现，两个俱乐部中的重叠区域比非重叠区域更灵活。损伤模拟表明，针对重叠区域的靶向攻击比随机故障甚至针对枢纽区域的靶向攻击更具破坏性。特别是，主导少数派俱乐部中的重叠区域比其他区域更灵活，对于信息交流也更为关键。总之，我们的研究结果展示了高度组织化的重叠模块结构，并从拓扑和功能两个方面揭示了重叠区域的重要性和复杂性，这为它们在执行多项任务和维持信息交流方面的作用提供了重要启示。
