Gu Shi, Betzel Richard F, Mattar Marcelo G, Cieslak Matthew, Delio Philip R, Grafton Scott T, Pasqualetti Fabio, Bassett Danielle S
Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Neuroimage. 2017 Mar 1;148:305-317. doi: 10.1016/j.neuroimage.2017.01.003. Epub 2017 Jan 11.
The complexity of neural dynamics stems in part from the complexity of the underlying anatomy. Yet how white matter structure constrains how the brain transitions from one cognitive state to another remains unknown. Here we address this question by drawing on recent advances in network control theory to model the underlying mechanisms of brain state transitions as elicited by the collective control of region sets. We find that previously identified attention and executive control systems are poised to affect a broad array of state transitions that cannot easily be classified by traditional engineering-based notions of control. This theoretical versatility comes with a vulnerability to injury. In patients with mild traumatic brain injury, we observe a loss of specificity in putative control processes, suggesting greater susceptibility to neurophysiological noise. These results offer fundamental insights into the mechanisms driving brain state transitions in healthy cognition and their alteration following injury.
神经动力学的复杂性部分源于其潜在解剖结构的复杂性。然而，白质结构如何限制大脑从一种认知状态转变为另一种认知状态仍然未知。在这里，我们借助网络控制理论的最新进展来解决这个问题，将大脑状态转变的潜在机制建模为由区域集的集体控制引发的机制。我们发现，先前确定的注意力和执行控制系统准备影响一系列广泛的状态转变，而这些转变无法轻易用基于传统工程学的控制概念进行分类。这种理论上的通用性伴随着对损伤的易感性。在轻度创伤性脑损伤患者中，我们观察到假定控制过程中特异性的丧失，这表明对神经生理噪声更敏感。这些结果为驱动健康认知中大脑状态转变的机制及其损伤后的改变提供了基本见解。
