通过新型正则化盲源分离法揭示动态功能连接组的隐藏来源

Jialu Ran, Yikai Wang, Ying Guo
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引用次数: 0

摘要

摘要 对大脑功能连接组及其动态变化的研究可以为了解大脑组织及其重构提供宝贵的信息。然而,利用功能磁共振成像(fMRI)分析动态功能连接(dFC)面临着重大挑战,包括大脑网络的高维性、观察到的 dFC 潜在来源的未知性以及增加了虚假发现风险的大量大脑连接。在本文中,我们提出了一种名为 dyna-LOCUS 的正则化盲源分离(BSS)新方法来应对这些挑战。dyna-LOCUS 对观察到的 dFC 测量进行分解,以揭示潜在的源连接特征及其动态的时间表达轮廓。通过利用低阶因式分解和新颖的正则化,dyna-LOCUS 实现了动态脑功能连接组基础连接特质的高效可靠映射,描述了有助于观察到的 dFC 重构的连接特质的时间变化,并在识别全脑 dFC 状态方面生成了简洁且可解释的结果。我们引入了一种高效的迭代节点旋转算法(Node-Rotation algorithm),它能解决学习 dyna-LOCUS 的非凸优化问题。模拟研究证明了我们提出的方法的优势。将 dyna-LOCUS 应用于费城神经发育队列(PNC)研究,揭示了潜在的连通性特征以及驱动这些神经回路的关键大脑连接和区域,揭示了这些连通性特征的时间表达水平和相互作用,并就与执行功能相关的连通性特征的神经发育过程中的性别差异得出了新的发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling hidden sources of dynamic functional connectome through a novel regularized blind source separation approach
Abstract The investigation of the brain’s functional connectome and its dynamic changes can provide valuable insights into brain organization and its reconfiguration. However, the analysis of dynamic functional connectivity (dFC) using functional magnetic resonance imaging (fMRI) faces major challenges, including the high dimensionality of brain networks, unknown latent sources underlying observed dFC, and the large number of brain connections that increase the risk of spurious findings. In this paper, we propose a new regularized blind source separation (BSS) method called dyna-LOCUS to address these challenges. dyna-LOCUS decomposes observed dFC measures to reveal latent source connectivity traits and their dynamic temporal expression profiles. By utilizing low-rank factorization and novel regularizations, dyna-LOCUS achieves efficient and reliable mapping of connectivity traits underlying the dynamic brain functional connectome, characterizes temporal changes of the connectivity traits that contribute to the reconfiguration in the observed dFC, and generates parsimonious and interpretable results in identifying whole-brain dFC states. We introduce a highly efficient iterative Node-Rotation algorithm that solves the nonconvex optimization problem for learning dyna-LOCUS. Simulation studies demonstrate the advantages of our proposed method. Application of dyna-LOCUS to the Philadelphia Neurodevelopmental Cohort (PNC) study unveils latent connectivity traits and key brain connections and regions driving each of these neural circuits, reveals temporal expression levels and interactions of these connectivity traits, and generates new findings regarding gender differences in the neurodevelopment of an executive function-related connectivity trait.
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