量化神经效率和能力：多项式对比的微分方程解释
Quantifying Neural Efficiency and Capacity: A Differential Equation Interpretation of Polynomial Contrasts
摘要：
基于任务的神经成像工具，认知神经科学的研究提供了深入了解大脑如何应对不断增长的认知需求。神经-认知关系的理论模型是基于对大脑活动线性和非线性增加的观察而开发的。神经网络的效率和容量是目前理论模型的两个参数。这两个理论参数分别描述了大脑活动的增加速率和增加的上限。目前的工作表明，增加大脑活动的二次模型响应n-back任务是一个微分方程模型的解决方案。这种对分析常见认知任务的标准方法的重新解释提供了丰富的新见解。结果包括神经效率和能力的全脑测量。神经-认知关系的量化为支持当前认知神经科学理论提供了证据。此外，这些方法为理解工作记忆的神经机制提供了一个框架。这允许在概念研究框架内估计实验操作的影响。所提出的方法被应用于21名健康的年轻人，同时参与四个级别的n-back任务。所有的方法都可以很容易地应用于神经成像的标准当前软件包。
英文摘要：
Task based neuroimaging tools for the study of cognitive neuroscience provide insight into understanding how the brain responds to increasing cognitive demand. Theoretical models of neural-cognitive relationships have been developed based on observations of linear and non-linear increases in brain activity. Neural efficiency and capacity are two parameters of current theoretical models. These two theoretical parameters describe the rate of increase of brain activity and the upper limits of the increases, respectively. The current work demonstrates that a quadratic model of increasing brain activity in response to the n-back task is a solution to a differential equation model. This reinterpretation of a standard approach to analyzing a common cognitive task provides a wealth of new insight. The results include brain wide measures of neural efficiency and capacity. The quantification of neural-cognitive relationships provides evidence to support current cognitive neuroscience theories. In addition, the methods provide a framework for understanding the neural mechanisms of working memory. This allows estimation of the effects of experimental manipulations within a conceptual research framework. The proposed methods were applied to twenty-one healthy young adults while engaging in four levels of the n-back task. All methods are easily applicable using standard current software packages for neuroimaging.
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