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Complexity Research in Nature Communications

This web collection showcases the potential of interdisciplinary complexity research by bringing together a selection of recent Nature Communications articles investigating complex systems. Complexity research aims to characterize and understand the behaviour and nature of systems made up of many interacting elements. Such efforts often require interdisciplinary collaboration and expertise from diverse schools of thought. Nature Communications publishes papers across a broad range of topics that span the physical and life sciences, making the journal an ideal home for interdisciplinary studies.

The Ecology and evolution section contains studies that explore the dynamics of networks of genes, individuals and communities using combinations of empirical data and mathematical tools. Other examples of computational modeling in biology include studies on precision medicine and molecular network dynamics, and these are highlighted in Network medicine tab. Neuroscience is another discipline that is effectively leveraging network analysis to better understand how the complex interactions between neuroanatomy and function give rise to equally complex human behaviors. Such behaviors, when combined, give rise to cultures and societies.  The latter are paradigmatic complex systems, and articles presented in the Social systems section examine these paradigmatic complex systems, describing the dynamics of social systems, financial systems and transport networks that affect much of our daily lives.  Finally, the collection under the Network structure and dynamics tab showcases methodological advances in complex system modeling and network analysis. The articles that we felt represented each section particularly well are also available in the Editors' picks section, below.  

Solving some of the most important problems in science may only be possible when scientists with different backgrounds collaborate to address shared questions using complementary techniques. Truly interdisciplinary research that can bridge the natural, physical and social sciences remains challenging, as it requires scientists to share and discuss their views across disciplines, and therefore such research must also be able to reach a diverse audience. Our collection, which has been chosen by editors across the broad spectrum of subjects covered by Nature Communications, has been put together with this specific goal in mind.  



Finding influential nodes for integration in brain networks using optimal percolation theory
Gino Del Ferraro et al., Nat. Commun. 9 2274 (2018)

Brain networks can be understood and studied as complex networks. Here the authors identify the essential nodes in a model of a brain network and then validate these predictions by means of in vivo pharmacogenetic interventions. They find that the nucleus accumbens is a central region for brain integration.


The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis
Jenna M. Reinen et al., Nat. Commun. 9 1157 (2018)

Temporal changes in brain dynamics are linked with cognitive abilities, but neither their stability nor relationship to psychosis is clear. Here, authors describe the dynamic neural architecture in healthy controls and patients with psychosis and find that they are stable over time and can predict psychotic symptoms.


Toward a universal decoder of linguistic meaning from brain activation
Francisco Pereira et al., Nat. Commun. 9 963 (2018)

Previous work decoding linguistic meaning from imaging data has generally been limited to a small number of semantic categories. Here, authors show that a decoder trained on neuroimaging data of single concepts sampling the semantic space can robustly decode meanings of semantically diverse new sentences with topics not encountered during training.


Towards a new approach to reveal dynamical organization of the brain using topological data analysis
Manish Saggar et al., Nat. Commun. 9 1399 (2018)

Approaches describing how the brain changes to accomplish cognitive tasks tend to rely on collapsed data. Here, authors present a new approach that maintains high dimensionality and use it to describe individual differences in how brain activity is represented and organized across different cognitive ´┐╝tasks.

Developmental increases in white matter network controllability support a growing diversity of brain dynamics
Evelyn Tang et al., Nat. Commun. 8 1252 (2017)

Human brain development is characterized by an increased control of neural activity, but how this happens is not well understood. Here, authors show that white matter connectivity in 882 youth, aged 8-22, becomes increasingly specialized locally and is optimized for network control.


Cognitive task information is transferred between brain regions via resting-state network topology
Takuya Ito et al., Nat. Commun. 8 1027 (2017)

Resting-state functional connections have been associated with cognitive abilities but it is unclear how these connections contribute to cognition. Here Ito et al present a new approach, information transfer mapping, showing that task-relevant information can be predicted by estimated activity flow through resting-state networks.

Brain network dynamics in high-functioning individuals with autism
Takamitsu Watanbe and Geraint Rees, Nat. Commun. 8 16048 (2017)

Though individuals with autism spectrum disorder (ASD) show a number of neural abnormalities, the relationship between global dynamic neural patterns and ASD symptoms remains unclear. Here, authors describe such global brain dynamics, relate these to cognitive abilities, ASD symptoms, and predict ASD diagnosis.

Hippocampal and prefrontal processing of network topology to simulate the future
Amir-Homayoun Javadi et al., Nat. Commun. 8 14652 (2017)

Cities and societies are often arranged according to topological networks, yet despite their importance, it is unclear how the brain processes such topological information to guide future decisions. Here, Javadi et al. use functional magnetic resonance imaging on participants who navigate a simulation of London and show that hippocampal activity is modulated by the characteristics and number of new streets, while prefrontal activity relates to planning the future route.

Dynamic reconfiguration of the default mode network during narrative comprehension
Erez Simony et al., Nat. Commun. 7 12141 (2016)

Default mode network (DMN) activity in the human brain is thought to reflect resting state activity in absence of external stimuli, but it was not known whether it encodes information about changes in the environment as well. Here, the authors devise a method called inter-subject functional correlation, and apply it to data generated by having people listen to real-life auditory narratives while undergoing functional magnetic resonance imaging (fMRI). They show that the strength of DMN coupling predicts memory of narrative segments, thereby linking resting state network dynamics to stimulus features.

Human seizures couple across spatial scales through travelling wave dynamics
Louis-Emmanuel Martinet et al., Nat. Commun. 8 14896 (2017)

Epileptic seizures span spatial and temporal scales making it difficult to understand the mechanism by which they manifest and spread. Here, the authors record both local and long-range neural activity during human epileptic seizures to study the underlying multi-scale dynamics. They find that the coupling of activity across spatial scales increases during seizures through propagating waves that are fit by a model combining neural activity and potassium concentration dynamics.

Constraints and spandrels of interareal connectomes
Mikail Rubinov, Nat. Commun. 7 13812 (2016)

Interareal connectomes are whole-brain wiring diagrams of white-matter pathways. Modules, hubs and their associated hierarchies are the structural hallmarks of such connectomes. This network structure has been postulated to be constrained by wiring-cost minimization. In this manuscript the author describes the interdependences between structural features for various established connectomes through network models. He shows that module hierarchy and rich club features in these networks are not in fact constrained by wiring cost, but rather arise as structural byproducts (spandrels) of simultaneous module and hub constraints.

Long-term coding of personal and universal associations underlying the memory web in the human brain
Emanuela De Falco et al., Nat. Commun. 7 13408 (2016)

Neurons in the medial temporal lobe change their firing patterns as people learn to pair items together, yet it is unclear if this pairing lasts, and if it does, how these long-term associations are encoded in the human brain. Here, authors use the participants' own ratings of how closely two items are associated as well as a web-based metric to show that single neurons in the medial temporal lobe tend to respond similarly to items that are closely conceptually related. Moreover, they show that the degree to which two concepts were related can be predicted by the neurons' response patterns.

Controllability of structural brain networks
Shi Gu et al.Nat. Commun. 6 8414 (2015)

Cognitive control is fundamental to human intelligence, yet the principles constraining the neural dynamics of cognitive control remain elusive. Here the authors use network control theory to explain the mechanism that lets the brain move between cognitive states. Their results demonstrate that the structure of brain networks dictates their distinct functional roles in controlling the trajectories of brain network function.





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