Medaglia John Dominic, Pasqualetti Fabio, Hamilton Roy H, Thompson-Schill Sharon L, Bassett Danielle S
Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, United States.
Department of Mechanical Engineering, University of California-Riverside, Riverside, CA 92521, United States.
Neurosci Biobehav Rev. 2017 Apr;75:53-64. doi: 10.1016/j.neubiorev.2017.01.016. Epub 2017 Jan 16.
Traditional approaches to understanding the brain's resilience to neuropathology have identified neurophysiological variables, often described as brain or cognitive "reserve," associated with better outcomes. However, mechanisms of function and resilience in large-scale brain networks remain poorly understood. Dynamic network theory may provide a basis for substantive advances in understanding functional resilience in the human brain. In this perspective, we describe recent theoretical approaches from network control theory as a framework for investigating network level mechanisms underlying cognitive function and the dynamics of neuroplasticity in the human brain. We describe the theoretical opportunities offered by the application of network control theory at the level of the human connectome to understand cognitive resilience and inform translational intervention.
传统上理解大脑对神经病理学的恢复力的方法已经确定了神经生理变量，这些变量通常被描述为大脑或认知“储备”，与更好的结果相关。然而，大规模脑网络中的功能和恢复力机制仍知之甚少。动态网络理论可能为在理解人类大脑功能恢复力方面取得实质性进展提供基础。从这个角度来看，我们将网络控制理论的最新理论方法描述为一个框架，用于研究人类大脑中认知功能和神经可塑性动态背后的网络层面机制。我们描述了在人类连接组水平应用网络控制理论所提供的理论机会，以理解认知恢复力并为转化干预提供信息。
