Latent functional connectivity underlying multiple brain states.
Latent functional connectivity underlying multiple brain states.
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DOI：
10.1162/netn_a_00234
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发表时间：
2022-06
影响因子：
4.7
通讯作者：
Cole, Michael W
中科院分区：
文献类型：
--
作者：
McCormick, Ethan M;Arnemann, Katelyn L;Ito, Takuya;Hanson, Stephen Jose;Cole, Michael W
功能连接性(FC)的研究主要集中在静息状态，在这种状态下，正在进行的动态被认为反映了大脑的内在网络结构，这被认为是广泛相关的，因为它持续存在于大脑状态(即，是状态通用的)。然而，静息状态是否是测量本征FC的最佳状态尚不清楚。我们认为，潜在FC反映了许多大脑状态的共享连接模式，相对于仅来自静息状态的测量，更好地捕获了状态一般的内在FC。我们使用来自人类连接组项目的fMRI数据，在七个高度不同的任务状态(24个条件)和休息状态下，使用留下一个任务排除因素分析，独立地估计了每个连接的潜在FC。与静止态连接相比，潜伏期FC提高了对保持状态的概括性，更好地解释了连接和任务诱发激活的模式。我们还发现，潜在的连通性改善了对扫描仪外行为的预测，以一般智力因素(G)为指标。我们的结果表明，在许多大脑状态下共享的FC模式，而不仅仅是休息状态，更好地反映了状态-总体连接。这肯定了“内在的”脑网络结构的概念，即一组跨大脑状态持续存在的连通性属性，提供了一个更新的概念和数学框架，将内在连通性作为潜在因素。静息状态功能磁共振成像最初的承诺是，它将反映大脑中不受任何特定任务背景影响的“内在”功能关系，然而直到最近，这一假设仍未得到检验。在这里，我们提出了一种潜在变量方法来估计内在功能连接性(FC)，作为REST功能连接性的替代。我们发现，潜伏期FC在预测大脑中的持续期FC和区域激活状态方面优于REST FC。此外，潜伏期Fc能更好地预测扫描仪外部测量的一般智力标记。我们证明，潜在变量方法包含了其他组合来自多个状态的数据的方法(例如，平均)，并且它在泛化能力和预测有效性方面优于单独的REST FC。
Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to reflect the brain’s intrinsic network architecture, which is thought to be broadly relevant because it persists across brain states (i.e., is state-general). However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting shared connectivity patterns across many brain states, better captures state-general intrinsic FC relative to measures derived from resting state alone. We estimated latent FC independently for each connection using leave-one-task-out factor analysis in seven highly distinct task states (24 conditions) and resting state using fMRI data from the Human Connectome Project. Compared with resting-state connectivity, latent FC improves generalization to held-out brain states, better explaining patterns of connectivity and task-evoked activation. We also found that latent connectivity improved prediction of behavior outside the scanner, indexed by the general intelligence factor (g). Our results suggest that FC patterns shared across many brain states, rather than just resting state, better reflect state-general connectivity. This affirms the notion of “intrinsic” brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor. The initial promise of resting-state fMRI was that it would reflect “intrinsic” functional relationships in the brain free from any specific task context, yet this assumption has remained untested until recently. Here we propose a latent variable method for estimating intrinsic functional connectivity (FC) as an alternative to rest FC. We show that latent FC outperforms rest FC in predicting held-out FC and regional activation states in the brain. Additionally, latent FC better predicts a marker of general intelligence measured outside of the scanner. We demonstrate that the latent variable approach subsumes other approaches to combining data from multiple states (e.g., averaging) and that it outperforms rest FC alone in terms of generalizability and predictive validity.