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任务态功能连接的功能相关性。

The Functional Relevance of Task-State Functional Connectivity.

机构信息

Center for Molecular & Behavioral Neuroscience, Rutgers University, Newark, New Jersey 07102

Center for Molecular & Behavioral Neuroscience, Rutgers University, Newark, New Jersey 07102.

出版信息

J Neurosci. 2021 Mar 24;41(12):2684-2702. doi: 10.1523/JNEUROSCI.1713-20.2021. Epub 2021 Feb 4.

Abstract

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the ability of the human brain to adaptively alter its functionality via rapid changes in inter-regional relationships. We used activity flow mapping-an approach for building empirically derived network models-to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance. Human cognition is highly dynamic, yet the functional network organization of the human brain is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the intrinsic (resting-state) network organization of the brain strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

摘要

静息态功能连接为理解内在脑网络组织提供了大量的见解,但与内在网络组织相关的任务相关变化的功能重要性仍不清楚。事实上,这种与任务相关的变化很小,表明它们可能只有最小的功能相关性。或者,尽管这些与任务相关的变化幅度很小,但它们对于人类大脑通过区域间关系的快速变化自适应地改变其功能的能力可能是至关重要的。我们使用活动流映射——一种构建经验衍生网络模型的方法——来量化任务状态功能连接(超出静息状态功能连接)在塑造人类大脑认知任务激活方面的功能重要性。我们发现,任务状态功能连接可以更好地预测所有 24 个任务条件和所有 360 个皮质区域的独立 fMRI 激活。此外,我们发现预测准确性主要受个体特定的功能连接模式驱动,而来自其他任务的功能连接模式(任务普遍的功能连接)在超过静息状态功能连接的基础上仍然提高了预测。此外,由于活动流模型模拟了任务诱发激活(是行为的基础)是如何产生的,这些结果可能为为什么先前的研究发现任务状态功能连接与行为个体差异之间存在相关性提供了机制上的见解。这些发现表明,功能连接的相关变化在动态重塑大脑网络组织方面起着重要作用,在任务执行过程中改变了神经活动的流动。人类认知是高度动态的,但人类大脑在静息和任务状态下的功能网络组织非常相似。我们假设,尽管存在这种整体网络稳定性,但大脑内在(静息态)网络组织的与任务相关的变化强烈影响认知任务表现期间的大脑激活。鉴于认知任务激活是通过网络相互作用产生的,我们利用基于连接的模型,使用静息态与任务态功能连接来预测独立的认知任务激活。这表明,功能网络组织的与任务相关的变化大大提高了认知任务激活的预测准确性,尽管这些与任务相关的网络变化很小,但证明了它们对动态认知过程的可能功能相关性。

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