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What does resting-state network (RSN) activity actually reflect? Deco and colleagues review computational models showing that local dynamics, signal transmission delays and noise contribute to emerging RSNs. They propose that multiple functional connectivity patterns can be expressed around the same anatomical framework, and that the resting brain explores these possible configurations.

Brain age gaps (BAGs) highlight deviations from healthy brain aging, yet their biophysical underpinnings in aging and dementia are not well understood. Here, the authors use EEG connectivity and generative modeling across diverse populations to reveal that BAGs are influenced by geography, income, sex and education, with implications for understanding accelerated aging and dementia.

Better understanding of a trade-off between the speed and accuracy of decision-making is relevant for mapping biological intelligence to machines. The authors introduce a brain-inspired learning algorithm to uncover dependencies in individual fMRI networks with features of neural activity and predict inter-individual differences in decision-making.

An unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics of the brain. Here, the authors introduce a new framework to study the different timescales through binning the output of a generative model of neural activity.

Psilocybin and escitalopram create significantly different reconfigurations in the global functional hierarchy of brain dynamics with opposite statistical effect responses in people with major depressive disorder.

This magnetic resonance imaging study identified distinct brain connectivity profiles for different clinical remission trajectories after a first episode of psychosis.

Deco et al. use multimodal neuroimaging data to quantify the global workspace as the common ‘functional rich club’ of regions intersecting across seven tasks as well as rest.

A unifying turbulent dynamics framework using both model-free and modelbased measures of whole-brain information provides insights into brain states.

The brain balances the segregation and integration of incoming information to facilitate flexible cognition and behaviour. In this Opinion article, Deco and colleagues argue that whole-brain computational modelling based on neuroimaging data can provide insights into these segregation and integration processes.

Castells-Nobau et al. provide insight into how bacteriophages in the gut microbiome contribute to regulating food addiction by modulating tryptophan and tyrosine metabolism in the host.

Whole-brain modelling is an essential tool that provides relevant insights for neuroscientists as they work to discover the fundamental principles of healthy brain function.

Autism spectrum disorder (ASD) is characterised by synaptic surplus and atypical functional connectivity. Here, the authors show that synaptic pathology in Tsc2 haploinsufficient mice is associated with autism-like behavior and cortico-striatal hyperconnectivity, and that analogous functional hyperconnectivity signatures can be linked to mTOR-pathway dysfunction in subgroups of children with idiopathic ASD.

A ventromedial cortical ‘Where’ visual pathway in humans for spatial scenes is revealed with MEG. It is fundamental to episodic memory and navigation.

Mapping brain connections is critical for decoding brain functions. Here, the authors present an integrated resource of awake resting-state fMRI and neuronal tracing data of marmosets to understand structural-functional relationships of brain connections.

Neonatal brain dynamics are not well understood. Here, the authors characterise brain transient states in neonates, and show that preterm infants display altered whole brain dynamics and an atypical repertoire of regional transient states, which are associated with behavioural outcomes at 18 months of age.

There are several models of how serotonergic psychedelic drugs affect brain activity. Here the authors use network control theory and functional MRI data to provide evidence that serotonin receptor agonists LSD and psilocybin flatten the brain’s dynamic landscape, allowing for facile state transitions and more temporally diverse brain activity.

Spontaneous fluctuations in brain activity exhibit complex spatiotemporal patterns across animal species. Here the authors show that sensory-motor regions and spatial heterogeneity in excitation-inhibition balance might shape multi-stability in brain dynamics.

Computational neuroscience contributes to our understanding of complex diseases. Here, Rolls and colleagues review several models of schizophrenia, focusing on those that include attractor networks. They show how reduced stability of attractor networks in the prefrontal cortex might produce symptoms of schizophrenia.

Favaretto et al. show that the brain rapidly alternates between transient connectivity patterns, with cortical regions flexibly synchronizing with two groups of subcortical regions, and that this dynamic is abnormal in stroke patients.

How genes sculpt the complex architecture of the human connectome remains unclear. Here, the authors show that genes preferentially influence the strength of connectivity between functionally valuable, metabolically costly connections between brain network hubs.

Metastability is a concept from dynamical systems. In the brain, it is thought to reflect the balance between the cooperative and independent functioning of brain areas or neuronal populations. In this Review, Hancock, Rosas and colleagues provide an overview of metastability in neuroscience.

Time-dependent parameters in whole-brain models capture DMT’s transient effects, revealing heightened reactivity in fronto-parietal and visual regions induced by perturbations.

The brain is relatively slow and needs efficient information transfer to ensure survival. Here, the authors demonstrated a likely solution, namely the presence of turbulence in fast neuronal whole-brain dynamics, providing a fundamental principle to facilitate the necessary information transfer across spatiotemporal scales

A thermodynamic-inspired framework enables researchers to quantify the balance between intrinsic and extrinsic dynamics in brain signals, providing further insight into how the brain behaves during sleep and when under anesthesia.

López-González et al study the fMRI brain dynamics and their underlying mechanism from patients that suffered brain injuries leading to a disorder of consciousness as well as from healthy subjects undergoing propofol-induced sedation. They show that pathological and pharmacological low-level states of consciousness display disrupted synchronization patterns, higher constraint to the anatomy and a loss of heterogeneity and stability in the structural hubs compared to conscious states.

In this Perspective, the authors propose that brain injury can result in sleep-like slowing of cortical EEG waves during wakefulness. The generation of these dynamics and their effects on brain networks and behavior are discussed, as well as future directions for neuromodulation.

In this Opinion article, Hahn, Kumar and colleagues propose that synchrony- and oscillation-based communication between brain networks can be described by a common theoretical framework. They also suggest a mechanism for control of the flow of information in the brain through nesting of slow and fast oscillations.

Human fMRI and dMRI data are used alongside modelling to test the phenomenological renormalization group (PRG) method, consequentially suggesting that scaling of rsfMRI activity emerges from criticality and exponentially decaying connectivity.

The mechanisms underlying transient brain rhythms and weekly stable synchronization of distant brain areas and their link with neural activity is still a matter of debate. Here, the authors use a brain network model to study spatio-temporal synchronization dynamics of brain regions and find that there is an optimal regime where spatially and spectrally resolved metastable oscillatory modes, similar to human magnetoencephalography data, emerge.

Rué-Queralt et al. present a method for calculating low dimensional manifolds in functional magnetic resonance imaging data and use it across human sleep-wake cycles. Their results indicate that non-REM sleep states occupy distinct areas of this intrinsic manifold and can be used to differentiate stages of sleep and waking.