Community detection in the human connectome: Method types, differences and their impact on inference
Community detection in the human connectome: Method types, differences and their impact on inference
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DOI：
10.1002/hbm.26669
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发表时间：
2024-04-01
影响因子：
4.8
通讯作者：
Stamoulis，Catherine
中科院分区：
文献类型：
--
作者：
Brooks，Skylar J.;Jones，Victoria O.;Stamoulis，Catherine
社团结构是优化组织的脑网络的一个基本拓扑特征。目前，没有明确的标准或系统的方法来选择最合适的社区检测方法。此外，方法选择对估计社区(和网络模块化)的准确性和稳健性的影响，以及网络社区与认知和其他个体测量之间的方法依赖关系，还没有被很好地理解。本研究分析了真实大脑网络的大数据集(从静息状态功能磁共振估计，来自青少年大脑认知发展研究中的 5251前/早期青少年)，以及具有异质的、受数据启发的拓扑的 5338合成网络，目的是调查和比较三类社区检测方法：(I)基于模块化最大化的(Newman和Louvain)，(Ii)概率(在随机区块建模框架内的贝叶斯推理)，以及(Iii)几何(基于图Ricci flow)。在方法和它们的个体准确性(相对于合成网络中的地面事实)和可靠性(当应用于来自同一大脑的多个fMRI运行时)之间的广泛比较表明，潜在的大脑网络拓扑在社区检测方法的准确性、可靠性和一致性方面起着关键作用。一致性方法(DIS)相似性及其与拓扑特性的相关性在各次fMRI检查中被评估。基于合成图，大多数方法的性能相似，只有在某些拓扑结构下才有相当高的精度，特别是那些对应于至少具有准最优群落组织的发达连通的拓扑结构。相比之下，在难以发现社区的密集和/或弱连接网络中，这些方法产生了非常不同的结果，其中SBM中的贝叶斯推理比所有其他方法具有更高的准确率。方法特定模块与人口学、人体测量学、生理学和认知参数之间的关联主要表现为方法不变性，但也有一定的方法依赖性。尽管方法对不同水平的社区结构的敏感性可能部分解释了模块化估计和感兴趣的参数之间的方法依赖关系，但方法依赖也突出了可靠性和可重复性的潜在问题。这些发现表明，概率方法，如SBM框架中的贝叶斯推理，可以提供跨网络拓扑的社区结构的一致可靠的估计。此外，为了最大限度地提高生物推理的稳健性，应使用多种可靠的检测方法来确认已识别的网络社区及其认知、行为和其他相关因素。
Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method‐dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting‐state fMRI from n$$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and n$$ n $$ = 5338 synthetic networks with heterogeneous, data‐inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization‐based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi‐optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method‐specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method‐dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.