Patterns of low frequency brain-wide activity have drawn attention across multiple disciplines in neuroscience. Brain-wide activity patterns are often described through correlations, which capture concurrent increases and decreases in neural activity. More recently, several groups have described reproducible temporal sequences across the brain, illustrating precise long-distance control over the timing of low frequency activity. Features of correlation and temporal organization both point to a systems-level structure of brain activity consisting of large-scale networks and their mutual interactions. Yet a unified view for understanding large networks and their interactions remains elusive. Here, we propose a framework for computing probabilistic flow in brain-wide activity. We demonstrate how flow probabilities are modulated across rest and task states and show that the probabilistic perspective captures both intra- and inter-network dynamics. Finally, we suggest that a probabilistic framework may prove fruitful in characterizing low frequency brain-wide activity in health and disease.

中文翻译：

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全脑活动的概率流

低频全脑活动模式引起了神经科学多个学科的关注。全脑活动模式通常通过相关性来描述，这些相关性捕获神经活动的同时增加和减少。最近，几个小组描述了大脑中可重复的时间序列，说明了对低频活动时间的精确长距离控制。相关性和时间组织的特征都指向大脑活动的系统级结构，由大规模网络及其相互作用组成。然而，理解大型网络及其相互作用的统一观点仍然难以捉摸。在这里，我们提出了一个计算全脑活动概率流的框架。我们展示了如何在休息和任务状态之间调节流动概率，并表明概率视角捕获网络内和网络间动态。最后，我们建议概率框架在表征健康和疾病中的低频全脑活动方面可能卓有成效。