Converging evidence indicates that the heterogeneity of cognitive profiles may arise through detectable alternations in brain functions. Particularly, brain functional connectivity, measured under resting and cognitive states, characterizes the unique neuronal interconnections across large-scale brain networks. Despite an unprecedented opportunity to uncover neurobiological subtypes through clustering or subtyping analyses on multi-state functional connectivity, few existing approaches are applicable here to accommodate the network topology and unique biological architecture of functional connectivity. To address this issue, we propose an innovative Bayesian nonparametric network-variate clustering analysis to uncover subgroups with homogeneous brain functional network patterns integrating different cognitive states. In light of the existing neuroscience literature, we assume there are unknown state-specific modular structures within functional connectivity and simultaneously impose selection to identify informative network features for defining subtypes within unsupervised learning. To further facilitate practical use, we develop a computationally efficient variational inference algorithm to perform posterior inference with satisfactory estimation accuracy. Extensive simulations show the superior clustering accuracy and plausible result of our method. Applying the method to the landmark Adolescent Brain Cognitive Development (ABCD) study, we successfully establish neurodevelopmental subtypes linked with impulsivity related behavior trait, and identify brain sub-network phenotypes under each state to signal neurobiological heterogeneity.

翻译：积累的证据表明，认知特征的异质性可能源于脑功能的可检测变化。特别地，在静息态和认知态下测量的脑功能连接，刻画了大规模脑网络中独特的神经互联模式。尽管多状态功能连接为通过聚类或亚型分析揭示神经生物学亚型提供了前所未有的机遇，但现有方法鲜少能适应功能连接的网络拓扑结构和独特生物学架构。为解决该问题，我们提出一种创新的贝叶斯非参数网络变量聚类分析方法，在整合不同认知状态的前提下，揭示具有同质性脑功能网络模式的亚组。基于现有神经科学文献，我们假设功能连接中隐藏着状态特异性的未知模块化结构，同时通过变量选择在无监督学习中识别定义亚型的信息性网络特征。为促进实际应用，我们开发了计算高效的变分推断算法，在保持满意估计精度的同时执行后验推断。大量模拟实验证明了该方法优异的聚类准确性与结果可解释性。将该方法应用于里程碑式的青少年脑认知发展（ABCD）研究，我们成功建立了与冲动行为特质相关的神经发育亚型，并识别出各状态下的脑亚网络表型，揭示了神经生物学异质性。