Department of Psychology, Princeton University, Princeton, NJ 08540, USA.
Neuroimage. 2010 Sep;52(3):766-76. doi: 10.1016/j.neuroimage.2010.01.071. Epub 2010 Jan 29.
Over the past decade, scientific interest in the properties of large-scale spontaneous neural dynamics has intensified. Concurrently, novel technologies have been developed for characterizing the connective anatomy of intra-regional circuits and inter-regional fiber pathways. It will soon be possible to build computational models that incorporate these newly detailed structural network measurements to make predictions of neural dynamics at multiple scales. Here, we review the practicality and the value of these efforts, while at the same time considering in which cases and to what extent structure does determine neural function. Studies of the healthy brain, of neural development, and of pathology all yield examples of direct correspondences between structural linkage and dynamical correlation. Theoretical arguments further support the notion that brain network topology and spatial embedding should strongly influence network dynamics. Although future models will need to be tested more quantitatively and against a wider range of empirical neurodynamic features, our present large-scale models can already predict the macroscopic pattern of dynamic correlation across the brain. We conclude that as neuroscience grapples with datasets of increasing completeness and complexity, and attempts to relate the structural and functional architectures discovered at different neural scales, the value of computational modeling will continue to grow.
在过去的十年中，科学界对大规模自发神经动力学特性的兴趣日益浓厚。与此同时，用于描述区域内电路和区域间纤维通路连接解剖结构的新技术也得到了发展。很快，就有可能建立包含这些新的详细结构网络测量的计算模型，从而可以对多个尺度的神经动力学进行预测。在这里，我们回顾了这些努力的实用性和价值，同时考虑了在哪些情况下以及在何种程度上结构决定了神经功能。对健康大脑、神经发育和病理学的研究都提供了结构连接与动力相关性之间直接对应关系的例子。理论观点进一步支持了这样的观点，即大脑网络拓扑结构和空间嵌入应该强烈影响网络动力学。尽管未来的模型需要更定量地测试，并针对更广泛的经验神经动力学特征进行测试，但我们目前的大规模模型已经可以预测大脑中动态相关性的宏观模式。我们的结论是，随着神经科学处理越来越完整和复杂的数据集，并尝试将在不同神经尺度上发现的结构和功能架构联系起来，计算建模的价值将继续增长。
