近日，美国耶鲁医学院Dustin Scheinost及其研究团队提出了连接组夸张化处理通过消除静息态功能磁共振成像中的大强度共激活模式，以强化个体差异特征。相关论文于2025年11月3日发表在《自然—神经科学》杂志上。

该课题组研究人员介绍了“夸张化处理”创新方法，一种将静息状态数据投影到与任务fMRI数据估计的协同激活模式的流形正交的子空间上的方法。这将从静息状态数据中删除这些共激活模式的线性组合，以创建夸张化连接体。课题组从两个大型神经成像数据集中对任务数据进行主题化，以构建任务协同激活模式的多种形式，并创建夸张化连接体。这些连接体表现出较低的个体间相似性和较高的可识别性，并且可以预测表型测量，代表个体行为差异，通常比标准连接体在更大程度上。他们的研究结果表明，在驱动静息状态功能连接的主导共激活之外，存在一个主题信号，这可能更好地表征了大脑的内在功能结构。

据介绍，在静息状态功能磁共振成像（fMRI）中，高振幅共激活模式是稀疏存在的，但它们驱动功能连接并类似于任务激活模式。然而，很少有研究表征了其余大部分静息状态信号。

附：英文原文

Title: Connectome caricatures remove large-amplitude coactivation patterns in resting-state fMRI to emphasize individual differences

Author: Rodriguez, Raimundo X., Noble, Stephanie, Camp, Chris C., Scheinost, Dustin

Issue&Volume: 2025-11-03

Abstract: High-amplitude coactivation patterns are sparsely present during resting-state functional magnetic resonance imaging (fMRI), yet they drive functional connectivity and resemble task activation patterns. However, little research has characterized the remaining majority of the resting-state signal. Here, we introduce caricaturing, a method for projecting resting-state data onto a subspace orthogonal to a manifold of coactivation patterns estimated from task fMRI data. This removes linear combinations of these coactivation patterns from resting-state data to create caricatured connectomes. We used task data from two large-scale neuroimaging datasets to construct a manifold of task coactivation patterns and created caricatured connectomes. These connectomes exhibit lower between-individual similarity and higher identifiability and could be used to predict phenotypic measures, representing individual differences in behavior, often to a greater degree than standard connectomes. Our results show that there is a useful signal beyond the dominant coactivations that drive resting-state functional connectivity, which may better characterize the brain’s intrinsic functional architecture.

DOI: 10.1038/s41593-025-02099-7

Source: https://www.nature.com/articles/s41593-025-02099-7