Department of Psychiatry and Behavioral Sciences, University of Minnesota, USA.
Institute for Health Informatics, University of Minnesota, USA.
Neuroimage. 2022 Jul 15;255:119211. doi: 10.1016/j.neuroimage.2022.119211. Epub 2022 Apr 14.
We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.
我们展示了一种数据驱动的方法,用于从静息态 fMRI 数据中计算有向连通性的“因果连接组”,使用贪婪邻接搜索和成对非高斯边缘方向。我们使用这种方法构建了 n=442 个因果连接组。与典型的基于 Pearson 相关性的图相比,这些连接组非常稀疏(大约 2.25%的边密度),但在几乎所有情况下都是完全连通的。因果连接组中突出的高度连接枢纽位于注意力(背侧注意)和执行(额顶叶和扣带回-顶叶)网络中。这些枢纽网络具有明显不同的连接模式:注意力网络与感觉区域共享传入连接,与更高认知网络共享传出连接,而执行网络主要与其他更高认知网络连接,具有高度的双向连接。虚拟损伤分析强调了这些发现,表明注意力和执行枢纽网络是人类因果连接组中关键脆弱点。这些数据突出了注意力和执行控制网络在人类皮质连接组中的核心作用,并为精神病学中数据驱动因果连通性分析的未来应用奠定了基础。