Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy; Dipartimento di Neuroscienze umane, Sapienza Università di Roma, Roma, Italy.
Centro Fermi - Museo storico della fisica e Centro di studi e ricerche Enrico Fermi, Roma, Italy.
Neuroimage. 2018 Oct 1;179:570-581. doi: 10.1016/j.neuroimage.2018.06.006. Epub 2018 Jul 5.
Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation.
静息状态下的大脑活动以广泛分布且具有空间特异性的同步低频血氧水平依赖(BOLD)波动模式为特征,这些波动模式与生理相关的大脑网络相对应。已知这种网络行为在执行任务期间也会持续存在,但与任务相关的网络内和网络间连通性调节的细节在很大程度上尚不清楚。在这项研究中,我们利用多参数和多尺度方法来研究低频波动如何适应持续的 n 回工作记忆任务。我们发现,从静息状态到任务状态的转变涉及到任务正性和默认模式网络内同步模式的行为相关且具有尺度不变性的调节。具体来说,网络内的连通性降低伴随着网络间的连通性增加。尽管连通性强度发生了广泛的变化,但大脑网络的整体拓扑结构仍然得以保持。我们表明,这些发现受到静息状态下连通性的强烈影响,这表明连通性的绝对变化(即不考虑基线)可能不是研究功能连通性动态调节的最合适指标。我们的研究结果表明,任务可以引起 BOLD 波动的具有尺度不变性的分布式变化,进一步证实低频 BOLD 振荡具有专门的反应,并且与任务引发的激活紧密相关。