The coordinated interactions between individuals are fundamental for the success of the activities in some professional categories. We reported on brain-to-brain cooperative interactions between civil pilots during a simulated flight. We demonstrated for the first time how the combination of neuroelectrical hyperscanning and intersubject connectivity could provide indicators sensitive to the humans’ degree of synchronization under a highly demanding task performed in an ecological environment. Our results showed how intersubject connectivity was able to i) characterize the degree of cooperation between pilots in different phases of the flight, and ii) to highlight the role of specific brain macro areas in cooperative behavior. During the most cooperative flight phases pilots showed, in fact, dense patterns of interbrain connectivity, mainly linking frontal and parietal brain areas. On the contrary, the amount of interbrain connections went close to zero in the non-cooperative phase. The reliability of the interbrain connectivity patterns was verified by means of a baseline condition represented by formal couples, i.e. pilots paired offline for the connectivity analysis but not simultaneously recorded during the flight. Interbrain density was, in fact, significantly higher in real couples with respect to formal couples in the cooperative flight phases. All the achieved results demonstrated how the description of brain networks at the basis of cooperation could effectively benefit from a hyperscanning approach. Interbrain connectivity was, in fact, more informative in the investigation of cooperative behavior with respect to established EEG signal processing methodologies applied at a single subject level.

The network architecture of the human brain contributes in shaping neural activity, influencing cognitive and behavioral processes. The availability of neuroimaging data across the lifespan allows us to monitor how this architecture reorganizes, influenced by processes like learning, adaptation, maturation, and senescence. Changing patterns in brain connectivity can be analyzed with the tools of network science, which can be used to reveal organizational principles such as modular network topology. The identification of network modules is fundamental, as they parse the brain into coherent sub-systems and allow for both functional integration and segregation among different brain areas. In this work we examined the brain's modular organization by developing an ensemble-based multilayer network approach, allowing us to link changes of structural connectivity patterns to development and aging. We show that modular structure exhibits both linear and nonlinear age-related trends. In the early and late lifespan, communities are more modular, and we track the origins of this high modularity to two different substrates in brain connectivity, linked to the number and the weights of the intra-clusters edges. We also demonstrate that aging leads to a progressive and increasing reconfiguration of modules and a redistribution across hemispheres. Finally, we identify those brain regions that most contribute to network reconfiguration and those that remain more stable across the lifespan.

Comprehensive understanding of the human protein-protein interaction (PPI) network, aka the human interactome, can provide important insights into the molecular mechanisms of complex biological processes and diseases. Despite the remarkable experimental efforts undertaken to date to determine the structure of the human interactome, many PPIs remain unmapped. Computational approaches, especially network-based methods, can facilitate the identification of previously uncharacterized PPIs. Many such methods have been proposed. Yet, a systematic evaluation of existing network-based methods in predicting PPIs is still lacking. Here, we report community efforts initiated by the International Network Medicine Consortium to benchmark the ability of 26 representative network-based methods to predict PPIs across six different interactomes of four different organisms: A. thaliana, C. elegans, S. cerevisiae, and H. sapiens. Through extensive computational and experimental validations, we found that advanced similarity-based methods, which leverage the underlying network characteristics of PPIs, show superior performance over other general link prediction methods in the interactomes we considered.