There is a growing need for flexible methods for the analysis of large-scale functional magnetic resonance imaging (fMRI) data for the estimation of global signatures that summarize the population while preserving individual-specific traits. Independent vector analysis (IVA) is a data-driven method that jointly estimates global spatio-temporal patterns from multi-subject fMRI data, and effectively preserves subject variability. However, as we show, IVA performance is negatively affected when the number of datasets and components increases especially when there is low component correlation across the datasets. In this article, we study the problem and its relationship with respect to correlation across the datasets, and propose an effective method for addressing the issue by incorporating reference information of the estimation patterns into the formulation, as a guidance in high dimensional scenarios. Constrained IVA (cIVA) provides an efficient framework for incorporating references, however its performance depends on a user-defined constraint parameter, which enforces the association between the reference signals and estimation patterns to a fixed level. We propose adaptive cIVA (acIVA) that tunes the constraint parameter to allow flexible associations between the references and estimation patterns, and enables incorporating multiple reference signals, without enforcing inaccurate conditions. Our results indicate that acIVA can reliably estimate high-dimensional multivariate sources from large-scale simulated datasets, when compared with standard IVA. It also successfully extracts meaningful functional networks from a large-scale fMRI dataset for which standard IVA did not converge. The method also efficiently captures subject-specific information, which is demonstrated through observed gender differences in spectral power, higher spectral power in males at low frequencies and in females at high frequencies, within the motor, attention, visual and default mode networks.

中文翻译：

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自适应约束独立向量分析：大规模医学影像数据分析的有效解决方案

越来越需要灵活的方法来分析大规模功能磁共振成像 (fMRI) 数据，以估计在保留个体特定特征的同时汇总种群的全局特征。独立向量分析 (IVA) 是一种数据驱动的方法，可以从多主体 fMRI 数据中联合估计全局时空模式，并有效地保留主体可变性。然而，正如我们所展示的，当数据集和组件的数量增加时，IVA 性能会受到负面影响，尤其是当数据集之间的组件相关性较低时。在本文中，我们研究了问题及其与数据集相关性的关系，并通过将估计模式的参考信息纳入公式来提出解决该问题的有效方法，作为高维场景中的指导。受约束的 IVA (cIVA) 提供了一个用于合并参考的有效框架，但其性能取决于用户定义的约束参数，该参数将参考信号和估计模式之间的关联强制为固定水平。我们提出了自适应 cIVA (acIVA)，它调整约束参数以允许参考和估计模式之间的灵活关联，并能够合并多个参考信号，而不会强制执行不准确的条件。我们的结果表明，与标准 IVA 相比，acIVA 可以可靠地估计来自大规模模拟数据集的高维多变量源。它还成功地从标准 IVA 不收敛的大规模 fMRI 数据集中提取了有意义的功能网络。该方法还有效地捕获了特定于主题的信息，这通过观察到的光谱功率的性别差异、在低频的男性和高频的女性中较高的光谱功率、在运动、注意力、视觉和默认模式网络中得到证明。