Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China.
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Sci Rep. 2018 Feb 6;8(1):2507. doi: 10.1038/s41598-018-20123-8.
A critical mystery in neuroscience lies in determining how anatomical structure impacts the complex functional dynamics of the brain. How does large-scale brain circuitry constrain states of neuronal activity and transitions between those states? We address these questions using a maximum entropy model of brain dynamics informed by white matter tractography. We demonstrate that the most probable brain states - characterized by minimal energy - display common activation profiles across brain areas: local spatially-contiguous sets of brain regions reminiscent of cognitive systems are co-activated frequently. The predicted activation rate of these systems is highly correlated with the observed activation rate measured in a separate resting state fMRI data set, validating the utility of the maximum entropy model in describing neurophysiological dynamics. This approach also offers a formal notion of the energy of activity within a system, and the energy of activity shared between systems. We observe that within- and between-system energies cleanly separate cognitive systems into distinct categories, optimized for differential contributions to integrated versus segregated function. These results support the notion that energetic and structural constraints circumscribe brain dynamics, offering insights into the roles that cognitive systems play in driving whole-brain activation patterns.
神经科学中的一个关键难题在于确定解剖结构如何影响大脑的复杂功能动态。大规模的大脑回路如何限制神经元活动的状态和这些状态之间的转换？我们使用基于白质束追踪的大脑动力学最大熵模型来解决这些问题。我们证明，最可能的大脑状态 - 以最小能量为特征 - 在大脑区域之间显示出共同的激活模式：局部空间连续的一组大脑区域类似于认知系统经常被共同激活。这些系统的预测激活率与在单独的静息状态 fMRI 数据集测量的观察到的激活率高度相关，验证了最大熵模型在描述神经生理动力学方面的实用性。这种方法还提供了系统内活动能量和系统间活动能量的形式化概念。我们观察到，系统内和系统间的能量可以将认知系统清晰地分为不同的类别，为整合功能和分离功能的不同贡献进行了优化。这些结果支持这样一种观点，即能量和结构约束限制了大脑的动力学，为认知系统在驱动整个大脑激活模式中所起的作用提供了见解。
