Objective: The need for distilling the hemodynamic complexity into clinically relevant quantities has imposed a reductionist approach to investigate aortic flows, resulting in a lossy compression of the information hidden in 4D aortic fluid structures. Aiming at reducing information loss, this study proposes a network-based approach to identify and characterize in vivo the large-scale coherent motion of blood in the healthy human aorta using 4D Flow MRI. Methods: Adopting the quantitative paradigm of the aortic flow as a “social network”, 4D flow MRI acquisitions were performed on forty-one healthy volunteers. Correlations between the aortic blood flow rate waveform at the proximal ascending aorta (AAo), assumed as a main determinant of the aortic flow, and the waveforms of the axial velocity (aligned with the aortic centerline) in the whole aorta were used to build “one-to-all” networks. The impact of the driving flow rate waveform and of the main aortic geometric attributes on the transport of large-scale coherent fluid structures was investigated. Results: The anatomical length of persistence of large-scale coherent motion was the 29.6% of the healthy thoracic aorta length (median value, IQR 23.1%-33.9%). Moreover, it was positively correlated with the average and peak-to-peak AAo blood flow rate values, suggesting a remarkable inertial effect of the AAo flow rate on aortic hemodynamic coherence. No association emerged between the anatomical length of persistence and aortic geometric attributes. Conclusion: The here proposed in vivo approach allowed to quantitatively characterize the large-scale aortic blood motion, strengthening the definition of coherent hemodynamic structures. Significance: The findings on healthy aortas may be used as reference values to investigate the impact of aortic disease or devices implantation in disrupting/restoring the physiological spatiotemporal coherence of large-scale aortic flow.

In 2015, the United Nations established the Agenda 2030 for sustainable development, addressing the major challenges the world faces and introducing the 17 Sustainable Development Goals (SDGs). How are countries performing in their challenge toward sustainable development? We address this question by treating countries and Goals as a bipartite complex network. While network science has been used to unveil the interconnections among the Goals, it has been poorly exploited to rank countries for their achievements. In this work, we show that the network representation of the countries-SDGs relations as a bipartite system allows one to recover aggregated scores of countries’ capacity to cope with SDGs as the solutions of a network’s centrality exercise, where more central countries are showing best performances in pursuing the SDGs. While the Goals are all equally important by definition, interesting differences self-emerge when non-standard centrality metrics, borrowed from economic complexity, are adopted. Innovation and Climate Action stand as contrasting Goals to be accomplished, with countries facing the well-known trade-offs between economic and environmental issues even in addressing the Agenda. In conclusion, the complexity of countries’ paths toward sustainable development cannot be fully understood by resorting to a single, multipurpose, ranking indicator, while multi-variable analyses shed new light on the present and future of sustainable development.