Zhang Jijin, Wu Kejian, Dong Jiaqi, Feng Jianfeng, Yu Lianchun
School of Physical Science and Technology, Lanzhou Center for Theoretical Physics, Key Laboratory of Theoretical Physics of Gansu Province, and Key Laboratory of Quantum Theory and Applications of MoE, Lanzhou University, Lanzhou, Gansu 730000, China.
Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, 200433, China; Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK; School of Mathematical Sciences, School of Life Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, 200433, China.
Neural Netw. 2025 Apr;184:107100. doi: 10.1016/j.neunet.2024.107100. Epub 2024 Dec 25.
The human brain exhibits heterogeneity across regions and network connectivity patterns; However, how these heterogeneities contribute to whole-brain network functions and cognitive capacities remains unclear. In this study, we focus on the regional heterogeneity reflected in local dynamics and study how it contributes to the emergence of functional connectivity patterns, network ignition dynamics of the empirical brains. We find that the level of synchrony among voxelwise neural activities measured from the fMRI data is significantly correlated with the transcriptional variations in excitatory and inhibitory receptor gene expression. Consequently, we construct heterogeneous whole-brain network models with nodal excitability calibrated by the synchronization measure of regional dynamics. We demonstrate that as the extent of heterogeneity increases, the models operating around the critical point between order and disorder generate simulated functional connectivity networks increasingly similar to empirical resting-state or working memory task-evoked function connectivity networks. Furthermore, the heterogeneous models can predict individual differences in resting-state and task-evoked reconfiguration of the functional connectivity, as well as the comparative causal effect of empirical brain networks-that is, how the dynamics of one brain region affect whole-brain synchronization. Overall, this work demonstrates the viability of using regional heterogeneous functional signals to improve the performance of the whole-brain models, and illustrates how regional heterogeneity in human brains interplays with structural connections and critical dynamics to contribute to the emergence of functional connectivity networks.
人类大脑在区域和网络连接模式上表现出异质性；然而，这些异质性如何对全脑网络功能和认知能力产生影响仍不清楚。在本研究中，我们聚焦于局部动力学中反映出的区域异质性，并研究其如何促成功能连接模式的出现，即实证大脑的网络点火动力学。我们发现，从功能磁共振成像（fMRI）数据测量得到的体素级神经活动之间的同步水平，与兴奋性和抑制性受体基因表达的转录变化显著相关。因此，我们构建了异质性全脑网络模型，其节点兴奋性通过区域动力学的同步测量进行校准。我们证明，随着异质性程度的增加，在有序和无序之间的临界点附近运行的模型生成的模拟功能连接网络越来越类似于实证静息态或工作记忆任务诱发的功能连接网络。此外，异质性模型可以预测静息态和任务诱发的功能连接重新配置中的个体差异，以及实证脑网络的比较因果效应——即一个脑区的动力学如何影响全脑同步。总体而言，这项工作证明了利用区域异质性功能信号来提高全脑模型性能的可行性，并阐明了人类大脑中的区域异质性如何与结构连接和临界动力学相互作用，从而促成功能连接网络的出现。
