School of Computer Science and Technology, Beijing Institute of Technology, Beijing, PR China.
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, PR China.
Neuroimage. 2021 Oct 15;240:118382. doi: 10.1016/j.neuroimage.2021.118382. Epub 2021 Jul 9.
Self-construal (orientations of independence and interdependence) is a fundamental concept that guides human behaviour, and it is linked to a large number of brain regions. However, understanding the connectivity of these regions and the critical principles underlying these self-functions are lacking. Because brain activity linked to self-related processes are intrinsic, the resting-state method has received substantial attention. Here, we focused on resting-state functional connectivity matrices based on brain asymmetry as indexed by the differential partition of the connectivity located in mirrored positions of the two hemispheres, hemispheric specialization measured using the intra-hemispheric (left or right) connectivity, brain communication via inter-hemispheric interactions, and global connectivity as the sum of the two intra-hemispheric connectivity. Combining machine learning techniques with hypothesis-driven network mapping approaches, we demonstrated that orientations of independence and interdependence were best predicted by the asymmetric matrix compared to brain communication, hemispheric specialization, and global connectivity matrices. The network results revealed that there were distinct asymmetric connections between the default mode network, the salience network and the executive control network which characterise independence and interdependence. These analyses shed light on the importance of brain asymmetry in understanding how complex self-functions are optimally represented in the brain networks.
自我建构(独立和相互依存的取向)是指导人类行为的基本概念,它与大量的大脑区域有关。然而,人们对这些区域的连接性以及这些自我功能的关键原则缺乏了解。由于与自我相关的过程相关的大脑活动是内在的,静息状态方法受到了广泛关注。在这里,我们关注基于大脑不对称的静息态功能连接矩阵,该矩阵由位于两个半球镜像位置的连接的差异分区索引,使用半球内(左或右)连接测量的半球专门化,通过半球间相互作用的大脑通信以及作为两个半球内连接总和的全局连接。我们结合机器学习技术和假设驱动的网络映射方法,证明与大脑通信、半球专门化和全局连通性矩阵相比,独立和相互依存的取向可以通过不对称矩阵得到最佳预测。网络结果表明,默认模式网络、突显网络和执行控制网络之间存在着明显的不对称连接,这些连接特征分别代表着独立和相互依存。这些分析揭示了大脑不对称在理解复杂的自我功能如何在大脑网络中得到最佳表示的重要性。