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Working memory training reshapes the brain functional network reorganization. Here, the authors demonstrate an increase of the whole-brain network segregation during the n-back task, accompanied by alterations in dynamic communication between the default mode system and task-positive systems.

To mark the 20th anniversary of Nature Reviews Neuroscience, in this Viewpoint article we asked some of the researchers who have authored pieces published in the journal in recent years for their views on how the field, and their areas within it, have developed over the past two decades.

Working memory requires the brain to switch between cognitive states and activity patterns. Here, the authors show that the steering of these neural network dynamics is influenced by dopamine D1- and D2-receptor function and altered in schizophrenia.

The arrangement of a sequence of stimuli affects how humans perceive information. Here, the authors show experimentally that humans perceive information in a way that depends on the network structure of stimuli.

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.

Sera from vaccinated individuals and some monoclonal antibodies show a modest reduction in neutralizing activity against the B.1.1.7 variant of SARS-CoV-2; but the E484K substitution leads to a considerable loss of neutralizing activity.

Chronic infection with SARS-CoV-2 leads to the emergence of viral variants that show reduced susceptibility to neutralizing antibodies in an immunosuppressed individual treated with convalescent plasma.

Recurrent neural networks (RNNs) can learn to process temporal information, such as speech or movement. New work makes such approaches more powerful and flexible by describing theory and experiments demonstrating that RNNs can learn from a few examples to generalize and predict complex dynamics including chaotic behaviour.

Using data from top neuroscience journals, this study finds that women-led work tends to be undercited relative to expectations. This imbalance is driven largely by the citation practices of men and is increasing over time as the field diversifies.

Humans can easily uncover abstract associations. Here, the authors propose that higher-order associations arise from natural errors in learning and memory. They suggest that mental errors influence the humans’ representation of the world in significant and predictable ways.

Why do certain individuals learn faster than others, and how does the acquired information take shape within their brains? Tang and colleagues show that fast learners encode information in a particularly compact, efficient and space-saving manner.

Analyses of interregional neural networks from electrocorticography data from a large database of individuals with medication-resistant epilepsy highlight the structural, geometric and genetic factors that shape network organization.

Post-mortem histopathological data can be used to classify neuropathologies into six transdiagnostic clusters, and patient membership to these clusters can be predicted from cognitive scores, genotype and protein levels in cerebrospinal fluid.

In this study, Aggarwal and colleagues perform prospective sequencing of SARS-CoV-2 isolates derived from asymptomatic student screening and symptomatic testing of students and staff at the University of Cambridge. They identify important factors that contributed to within university transmission and onward spread into the wider community.

Lydon-Staley and colleagues examine intrinsic information seeking and find that individuals who tend to seek information that eliminates knowledge gaps move between similar concepts and tend to return to previously visited concepts.

Working memory is a critical component of executive function that allows people to complete complex tasks in the moment. Here, the authors show that this ability is underpinned by two newly defined brain networks.

Recent studies separately address the neural representation of stimuli and its dynamics in networks that model neural interactions. Ju and Bassett review such recent advances and discuss the integration of neural representations and network models.

Henderson et al. use quantitative pathology mapping and network modeling to show that α-synuclein pathology spreads through a neuroanatomically connected network, guided by selective vulnerability and genetic risk factors.

Associations between of omega-3 fatty acids and mortality are not clear. Here the authors report that, based on a pooled analysis of 17 prospective cohort studies, higher blood omega-3 fatty acid levels correlate with lower risk of all-cause mortality.

Co-morbidity and symptom overlap make it difficult to associate psychiatric disorders with unique neural signatures. Here, the authors use a data-driven approach to show that the symptom dimensions of mood, psychosis, fear and externalizing behavior exhibit unique patterns of functional dysconnectivity.

Studies of brain network development typically focus on a single scale. Here, the authors derived personalized functional networks across scales, and find that network development systematically adheres to and strengthens hierarchical cortical organization.

Medaglia et al. explore how network control theory — a subdiscipline of engineering — could guide interventions that modulate mental states in order to treat cognitive deficits or enhance mental abilities.

Genetic and testing data from England show that the SARS-CoV-2 variant of concern B.1.1.7 has a transmission advantage over other lineages.

Controlling the behavior of a complex network usually requires a knowledge of the network dynamics. Baggio et al. propose a data-driven framework to control a complex dynamical network, effective for non-complete or random datasets, which is of relevance for power grids and neural networks.

Adaptive adjustments in learning dynamics are accompanied by dynamic changes in a pattern of whole-brain functional connectivity characterized by integration between fronto-parietal and other networks. These dynamic functional connectivity changes also track individual differences in learning.

Post-international travel quarantine has been widely implemented to mitigate SARS-CoV-2 transmission, but the impacts of such policies are unclear. Here, the authors used linked genomic and contact tracing data to assess the impacts of a 14-day quarantine on return to England in summer 2020.

A bottom-up mathematical approach provides a framework for the design of mechanical networks of two- or three-dimensional frames composed of freely rotating rods and springs that achieve any desired coordinate motion.

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 demonstrate that the structure of brain networks dictates their functional role in controlling dynamics.

The energy needed to control a network is related to the links between driver and non-driver nodes, a linear control theory suggests. Applying the theory to connectome data reveals that diverse dynamics in brain networks incur small energetic cost.

A study of the evolution of the SARS-CoV-2 virus in England between September 2020 and June 2021 finds that interventions capable of containing previous variants were insufficient to stop the more transmissible Alpha and Delta variants.

Evidence suggests that socio-economic status can affect not only the outcome of structural and functional development of the brain but also its rate. Tooley, Bassett and Mackey review this evidence and suggest that the valence and frequency of early experiences interact to influence brain development.

Meso-scale architecture of connectomes is usually modeled as segregated clusters and communities. Here the authors report that non-assortative communities are better able to capture the functional connectivity for some networks and offer measures of community diversity that predict cognitive performance.

As children grow, so does their knowledge of language. Sizemore et al. describe knowledge gaps, manifesting as topological cavities, in toddlers’ growing semantic network. These gaps progress similarly, independent of the order in which children learn words.

Modern network neuroscience involves the use of various types of models to understand the brain. In this Review, Bassett, Zurn and Gold discuss the aims of this approach before examining how network models may be categorized and validated.

Resting-state functional connectivity has helped reveal the brain's network organization, yet its relevance to cognitive task activations has been unclear. The authors found that estimating activity flow over resting-state networks allows prediction of held-out activations, suggesting activity flow as a linking mechanism between resting-state networks and cognitive task activations.

The authors used graph signal processing to examine whether fMRI signals correspond to underlying anatomical networks. They found that alignment between functional signals and anatomical structure was associated with greater cognitive flexibility.

The authors used new network-analysis algorithms to examine how distributed networks of brain areas are reorganized as humans learn a new motor skill. Using fMRI, the authors found that learning induced autonomy of sensorimotor systems and that a release of cognitive control hubs predicted individual differences in learning.

How the 22q11.2 deletion predisposes to psychiatric disease is unclear. Here, the authors examine living human neuronal cells and show that 22q11.2 regulates the expression of genes linked to autism during early development, and genes linked to schizophrenia and synaptic biology in neurons.

Network neuroscience tackles the challenge of discovering the principles underlying complex brain function and cognition from an explicitly integrative perspective. Here, the authors discuss emerging trends in network neuroscience, charting a path towards a better understanding of the brain that bridges computation, theory and experiment across spatial scales and species.

Kahn et al. show that learners capitalize on higher-order topological properties when they learn a probabilistic motor sequence based on a network traversal.

QSIPrep is a software platform for processing of most diffusion MRI datasets and ensures that adequate workflows are used.

Brain networks are characterized by nodes and hubs that determine information flow within and between areas. Bertolero et al. show that task-driven changes to hub and node connectivity increase modularity and improve cognitive performance.