Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands
Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands
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DOI：
10.1038/s42003-020-0961-x
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发表时间：
2020-05-22
影响因子：
5.9
通讯作者：
Bassett, Danielle S.
中科院分区：
文献类型：
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作者：
Cornblath, Eli J.;Ashourvan, Arian;Bassett, Danielle S.
各种各样的白色物质连接支持认知状态之间的无缝转换。然而，目前尚不清楚这些连接如何引导不同认知状态下大规模大脑活动模式的时间进展。在这里，我们分析了大脑的轨迹在一组单一的时间点的活动模式，从功能磁共振成像数据采集在休息状态和n-回工作记忆任务。我们发现，特定的时间序列的大脑活动调制的认知负荷，与年龄，并与任务的性能。使用从相同的主题获得的扩散加权成像，我们应用网络控制理论的工具，显示活动的线性传播沿着白色物质连接约束这些序列在休息的概率，而刺激驱动的视觉输入解释在n-back任务期间观察到的序列。总体而言，这些结果阐明了认知和发育相关的时空脑动力学的结构基础。Eli J. Cornblath等人使用线性网络控制理论的工具表明，白色物质连接性限制了休息时大脑活动模式之间的转换，有利于能量需求较小的转换，而视觉输入在认知任务中克服了这些限制。这些发现强调了在大脑活动模型中考虑内部白色物质网络动力学和外部输入的重要性。
A diverse set of white matter connections supports seamless transitions between cognitive states. However, it remains unclear how these connections guide the temporal progression of large-scale brain activity patterns in different cognitive states. Here, we analyze the brain's trajectories across a set of single time point activity patterns from functional magnetic resonance imaging data acquired during the resting state and an n-back working memory task. We find that specific temporal sequences of brain activity are modulated by cognitive load, associated with age, and related to task performance. Using diffusion-weighted imaging acquired from the same subjects, we apply tools from network control theory to show that linear spread of activity along white matter connections constrains the probabilities of these sequences at rest, while stimulus-driven visual inputs explain the sequences observed during the n-back task. Overall, these results elucidate the structural underpinnings of cognitively and developmentally relevant spatiotemporal brain dynamics. Eli J. Cornblath et al use tools from linear network control theory to show that white matter connectivity constrains transitions between brain activity patterns at rest to favor transitions with small energy requirements, while visual inputs overcome these constraints during a cognitive task. These findings highlight the importance of accounting for both internal white matter network dynamics and external inputs in models of brain activity.