Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical “template” ICA model where source signals—including known population brain networks and subject-specific signals—are represented as latent variables. For estimation, we derive an expectation–maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75–250% higher intra-subject reliability. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.大型脑成像数据库蕴含其所覆盖人群的脑组织结构与个体差异的丰富信息。尽管此类数据库已被直接用于研究人群的群体层面特征，但目前尚未被充分利用为指导单受试者分析的资源。 在此，我们提出利用大型功能磁共振成像（functional magnetic resonance imaging, fMRI）数据库中的信息，通过构建群体先验并将其应用于经验贝叶斯框架中。我们聚焦于独立成分分析（independent component analysis, ICA）中作为源信号的脑网络估计任务。 我们构建了层级化的“模板”ICA模型，其中源信号——包括已知的群体脑网络与受试者特异性信号——被表示为隐变量。针对模型估计，我们推导了具备显式解的期望最大化（expectation–maximization, EM）算法。 然而，由于该解在计算上难以处理，我们同时考虑了近似子空间算法与更快的两阶段方法。 通过大量模拟研究，我们评估了两种所提算法的性能，并与双回归（dual regression）这一流行但特设的方法进行对比。 所提的两种算法性能相近，且均显著优于双回归法。 我们还借助人类连接组计划（Human Connectome Project, HCP）开展了可靠性研究，结果表明模板ICA的性能远优于双回归法，受试者内部信度提升75%至250%。 本文的补充材料（包含可复现本研究所需材料的标准化说明）可作为在线补充材料获取。