Institute of Cognitive Neuroscience, University College London, London, United Kingdom.
FMRIB Centre, University of Oxford, Oxford, United Kingdom.
PLoS One. 2019 Apr 10;14(4):e0213861. doi: 10.1371/journal.pone.0213861. eCollection 2019.
Measuring whole-brain functional connectivity patterns based on task-free ('resting-state') spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisition is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns (commonly termed 'resting state networks'). We employed a 'steady-states' paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing us to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis) using standard resting-state functional connectivity (FC) analysis, we show that the whole-brain network architecture characteristic of the resting-state is comparable across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Changes in functional connectivity were detected locally, within the active networks. But to identify these local changes, the contributions of different FC networks to the global intrinsic connectivity pattern had to be isolated. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.
基于功能磁共振成像 (fMRI) 信号的无任务（“静息状态”）自发波动来测量全脑功能连接模式是探测习惯性大脑状态的标准方法，与特定任务的背景无关。这种观点得到了任务和静息状态衍生连接网络之间空间对应关系的支持。然而，静息状态下观察到的内在连接在任务期间是否持续仍然不清楚。在这里，我们试图确定任务执行引起的持续大脑激活变化如何影响全脑功能连接模式的完整性（通常称为“静息状态网络”）。我们采用了“稳态”范式，其中参与者连续执行特定任务（没有基线期）。参与者接受了单独的基于任务（视觉、运动和视觉运动）或无任务（静息）稳态扫描，每个扫描持续 5 分钟。这种独特的设计允许我们将一组传统的静息态分析应用于各种任务状态。此外，采用经典的 fMRI 块设计来为每个任务识别个体化的大脑激活模式，使我们能够描述稳态之间不同的激活模式如何影响全脑内在连接模式。通过使用标准的静息态功能连接 (FC) 分析检查分离的脑区 (节点) 和整个大脑之间的相关性（使用独立成分分析），我们表明，尽管在大脑激活（信号幅度）方面存在显著的任务间差异，但静息态的全脑网络架构特征在不同的稳态任务状态下是相似的。功能连接的变化在局部被检测到，在活跃的网络内。但是，要识别这些局部变化，必须隔离不同 FC 网络对全局内在连接模式的贡献。总的来说，我们表明，即使参与者参与不同的任务，静息态网络的内在连接仍然相对稳定，并且不限于静息状态。
