Department of Applied Mathematics, University of Washington, Seattle, WA, USA.
Allen Institute for Brain Science, Seattle, WA, USA.
PLoS Comput Biol. 2019 Apr 23;15(4):e1006978. doi: 10.1371/journal.pcbi.1006978. eCollection 2019 Apr.
Complex structural connectivity of the mammalian brain is believed to underlie the versatility of neural computations. Many previous studies have investigated properties of small subsystems or coarse connectivity among large brain regions that are often binarized and lack spatial information. Yet little is known about spatial embedding of the detailed whole-brain connectivity and its functional implications. We focus on closing this gap by analyzing how spatially-constrained neural connectivity shapes synchronization of the brain dynamics based on a system of coupled phase oscillators on a mammalian whole-brain network at the mesoscopic level. This was made possible by the recent development of the Allen Mouse Brain Connectivity Atlas constructed from viral tracing experiments together with a new mapping algorithm. We investigated whether the network can be compactly represented based on the spatial dependence of the network topology. We found that the connectivity has a significant spatial dependence, with spatially close brain regions strongly connected and distal regions weakly connected, following a power law. However, there are a number of residuals above the power-law fit, indicating connections between brain regions that are stronger than predicted by the power-law relationship. By measuring the sensitivity of the network order parameter, we show how these strong connections dispersed across multiple spatial scales of the network promote rapid transitions between partial synchronization and more global synchronization as the global coupling coefficient changes. We further demonstrate the significance of the locations of the residual connections, suggesting a possible link between the network complexity and the brain's exceptional ability to swiftly switch computational states depending on stimulus and behavioral context.
哺乳动物大脑的复杂结构连接被认为是神经计算多功能性的基础。许多先前的研究已经调查了小子系统的特性或大脑区域之间的粗连接，这些连接通常是二进制的，缺乏空间信息。然而，关于详细的全脑连接的空间嵌入及其功能意义知之甚少。我们专注于通过分析基于哺乳动物全脑网络上的耦合相振荡器系统，空间约束的神经连接如何塑造大脑动力学的同步，来缩小这一差距。这是通过最近从病毒追踪实验构建的艾伦老鼠大脑连通性图谱以及新的映射算法的发展来实现的。我们研究了网络是否可以基于网络拓扑的空间依赖性进行紧凑表示。我们发现连接具有显著的空间依赖性，空间上接近的大脑区域强烈连接，而距离较远的区域则较弱连接，符合幂律关系。然而，幂律拟合之上有许多残差，这表明大脑区域之间的连接比幂律关系所预测的要强。通过测量网络有序参数的敏感性，我们展示了这些强连接如何在网络的多个空间尺度上分散，从而在全局耦合系数变化时促进局部同步到更全局同步的快速转变。我们进一步证明了剩余连接位置的重要性，这表明网络复杂性和大脑根据刺激和行为背景迅速切换计算状态的非凡能力之间可能存在联系。
