James A. Roberts

Complex reasoning depends on flexible coordination among frontal, parietal, and thalamic systems, but the circuit mechanisms that support increasing relational demands remain unclear. We combined EEG with biologically grounded corticothalamic neural field modelling while participants solved relational problems of graded complexity. Successful reasoning was associated with dissociable frontoparietal dynamics. Frontal regions showed increased theta-band power, whereas parietal regions showed reduced alpha- and beta-band power. Theta-band phase synchronisation across frontoparietal-network nodes increased with problem complexity but was not associated with performance. By contrast, stronger beta-band synchronisation across the same network was associated with slower and less accurate responses as demands approached the highest complexity, suggesting that stronger coordination is not uniformly beneficial. Neural field modelling indicated that these regional spectral dynamics reflected specific complexity-dependent circuit adaptations. Parietal regions showed modulation of intracortical and corticothalamic gains, intrathalamic inhibition, prolonged loop delays, and faster synaptic filtering, whereas frontal regions primarily adjusted intracortical gains to maintain local excitatory-inhibitory balance and supported longer temporal integration windows. Together, these empirical and model-derived findings reveal complementary frontoparietal and corticothalamic mechanisms for relational reasoning.

The routing of information across the brain’s structural network is central to its wide range of functional capabilities. However, the mechanisms underlying information routing in complex brain networks, particularly between regions that do not share a direct anatomical connection, remain poorly understood. Neural mass models (NMMs), a computational modelling framework capable of capturing complex neural dynamics across scales, can potentially be used to study the dynamical and network bases of these vital polysynaptic routing processes. In this study, we investigate polysynaptic signalling in three widely used NMMs, obeying Ornstein-Ühlenbeck, Stuart-Landau, and Jansen-Rit dynamics, by tracking the propagation of a discrete, focal, high-amplitude perturbation across the underlying network. We find that polysynaptic propagation emerges in all tested NMMs when configured within dynamical regimes that effectively enhance the persistence of perturbations. We also find distinct parameter domains that maximise signal propagation to directly connected regions and to those separated from the source by at least two hops. Finally, we benchmark in silico stimulus propagation in the brain network against an empirical dataset of direct electrical stimulation trials, to explore the relative capabilities of the NMMs in capturing signal propagation to connected versus unconnected regions. This analysis highlights the significance of dynamical repertoire in capturing stimulus propagation outcomes. Overall, this study provides insights into how dynamical and network features shape signal propagation over complex brain networks.

The sleep patterns of infants and young children differ from adult sleep patterns, with longer duration and multiple bouts per 24 hours. There is also considerable heterogeneity in infant sleep, both between individuals and within individuals across development. While mathematical models have been used to understand the mechanisms that regulate adult sleep, the development of sleep from infancy through early childhood has remained largely unexplored. Here we used an established mathematical model for adult sleep regulation to investigate how the underlying mechanisms of sleep mature from ages 1 month to 5 years, and identify a basis for inter-individual differences at each age. Using a Bayesian approach to estimate joint distributions of model parameters at different ages, we found that: (i) decreases in the rate of accumulation of sleep homeostatic pressure captured the reduction in sleep duration across development; while (ii) increases in the time scale of the sleep homeostatic drive captured the consolidation of sleep into fewer sleep bouts per day across development. In terms of inter-individual differences, we found larger spread in the parameters within the earlier stages of infancy (<1 year). The center and boundaries of the joint distributions evolved with age through the parameter space, converging towards previously described adult parameter values. A bifurcation analysis of the homeostatic timescale parameter revealed that progressive consolidation of sleep occurs through abrupt loss of bouts one at a time, punctuated by narrow intervals of greater-than-24 h cycles. These results establish plausible, population-level trajectories in physiological parameters underpinning maturation of sleep regulation through infancy and early childhood.

Objective To assess the predictive accuracy of early neurophysiological and neuroimaging biomarkers, alone and in combination, for adverse neurodevelopmental disorders in term-born infants with neonatal encephalopathy (NE). Design Systematic review and meta-analysis conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy guidelines. Eligible studies included infants born at term with NE who underwent amplitude-integrated EEG (aEEG) or EEG and MRI of the brain within the first month of life. Adverse outcomes, assessed at 18–36 months of age, were defined as cerebral palsy, postneonatal epilepsy, severe hearing or visual impairment, moderate-to-severe developmental delay, or death attributable to NE. Searches were conducted in MEDLINE, CINAHL, Embase and Web of Science from database inception to 10 June 2025; risk of bias of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-Comparative (QUADAS-C) tool. Main outcome measures Sensitivity, specificity and diagnostic odds ratio (DOR) of abnormal aEEG background, EEG background, EEG seizures and MRI injury, individually and in combination, for predicting adverse outcomes, pooled using Bayesian bivariate random effects meta-analyses. Results 27 studies including 1843 infants were analysed. MRI injury was the individual predictor with higher DOR estimate (31.01, 95% CI 15.07 to 72.82), followed by abnormal EEG background (16.84, 95% CI 5.88 to 50.59), while abnormal aEEG background and EEG seizures performed less well (7.99, 95% CI 2.40 to 33.00; 4.46, 95% CI 1.86 to 11.42). Combining EEG background with MRI injury improved DOR (78.59, 95% CI 19.72 to 321.36) and specificity (93.8%, 95% CI 85.2% to 97.9%) compared with MRI alone. Conclusions MRI is a strong individual predictor of adverse outcomes in NE. Combining it with early EEG improves prognostic accuracy and may better support clinical decision-making. PROSPERO registration number CRD42024585816.

Background: Master protocols leverage a common trial infrastructure for launching multiple sub-studies. Translational research aims to progress scientific discoveries toward public health impact, which depends on establishing an intervention’s efficacy, effectiveness in real-world conditions, and successful strategies for implementation. While master protocols have been designed to improve the efficiency of clinical trials as sub-studies addressing a particular disease, their application with effectiveness-implementation hybrid studies is yet to be explored. The aim of this study was to develop recommendations for adapting mater protocol methods for effectiveness-implementation research. Methods: A method of consultation with translational research networks was undertaken between January and December 2024. Consideration was given to the requirements for service providers to engage in translational research, and how master protocols could support effectiveness-implementation hybrid sub-studies. The underlying rationale for potential adaptations is provided with reference to implementation frameworks, discussion of advantages and disadvantages, and summary recommendations. Results: Recommendations are proposed on establishing common trial infrastructure, aims and hypotheses, data collection, control groups, adaptive elements, and eligibility criteria. By leveraging cross-sectoral partnerships, co-producing research and dissemination, and incorporating adaptive elements, master protocols may offer a promising approach for accelerating progress along the translational research pipeline. Conclusions: The adaptation of master protocols for hybrid sub-studies could enable evidence-based interventions to be more effectively implemented in routine care settings. The feasibility of master protocols for effectiveness-implementation research is yet to be tested, and further development in this area is needed to trial the proposed methodology.

Changes in the frontostriatal system activity support individuals’ perseverance in distressful thoughts and rigid, repetitive behaviours that define obsessive-compulsive disorder (OCD). Converging evidence from preclinical and clinical work suggests that OCD maps onto a functional imbalance in the ventral and dorsal frontostriatal circuits. However, the neural mechanisms supporting these dysregulations remain elusive, their association with symptom severity is unclear, and therapeutic interventions are limited. To address these gaps, we combined neuroimaging and behavioural data from individuals with OCD and controls with computational modelling. We found that bidirectionally decreasing spontaneous neural coupling in the ventromedial circuit while concurrently increasing dorsolateral cortico-striatal coupling delivered the highest functional improvements in OCD. The analysis of longitudinal changes in obsessions and compulsions with respect to modelled neural interventions supported our predictions. By highlighting behaviourally meaningful neural mechanisms hidden from traditional neuroimaging analysis, this study advances knowledge on the neural basis of OCD and provides new therapeutic targets for obsessions and compulsions.

Sleep patterns in infancy and early childhood vary greatly and change rapidly during development. In adults, sleep patterns are regulated by interactions between neuronal populations in the brainstem and hypothalamus, driven by the circadian and sleep homeostatic processes. However, the neurophysiological mechanisms underlying the sleep patterns and their variations across infancy and early childhood are poorly understood. We investigated whether a well-established mathematical model for sleep regulation in adults can model infant sleep characteristics and explain the physiological basis for developmental changes. By fitting longitudinal sleep data spanning 2 to 540 days after birth, we inferred parameter trajectories across age. We found that the developmental changes in sleep patterns are consistent with a faster accumulation and faster clearance of sleep homeostatic pressure in infancy and a weaker circadian rhythm in early infancy. We also find greater sensitivity to phase-delaying effects of light in infancy and early childhood. These findings reveal fundamental mechanisms that regulate sleep in infancy and early childhood. Given the critical role of sleep in healthy neurodevelopment, this framework could be used to pinpoint pathophysiological mechanisms and identify ways to improve sleep quality in early life.

Cortical activity depends upon a continuous supply of oxygen and other metabolic resources. Perinatal disruption of oxygen availability is a common clinical scenario in neonatal intensive care units, and a leading cause of lifelong disability. Pathological patterns of brain activity including burst suppression and seizures are a hallmark of the recovery period, yet the mechanisms by which these patterns arise remain poorly understood. Here, we use computational modeling of coupled metabolic-neuronal activity to explore the mechanisms by which oxygen depletion generates pathological brain activity. We find that restricting oxygen supply drives transitions from normal activity to several pathological activity patterns (isoelectric, burst suppression, and seizures), depending on the potassium supply. Trajectories through parameter space track key features of clinical electrophysiology recordings and reveal how infants with good recovery outcomes track toward normal parameter values, whereas the parameter values for infants with poor outcomes dwell around the pathological values. These findings open avenues for studying and monitoring the metabolically challenged infant brain, and deepen our understanding of the link between neuronal and metabolic activity.

Long-term control of SARS-CoV-2 outbreaks depends on the widespread coverage of effective vaccines. In Australia, two-dose vaccination coverage of above 90% of the adult population was achieved. However, between August 2020 and August 2021, hesitancy fluctuated dramatically. This raised the question of whether settings with low naturally derived immunity, such as Queensland where less than 0.005 % of the population is known to have been infected in 2020, could have achieved herd immunity against 2021’s variants of concern. To address this question, we used the agent-based model C ovasim . We simulated outbreak scenarios (with the Alpha, Delta and Omicron variants) and assumed ongoing interventions (testing, tracing, isolation and quarantine). We modelled vaccination using two approaches with different levels of realism. Hesitancy was modelled using Australian survey data. We found that with a vaccine effectiveness against infection of 80%, it was possible to control outbreaks of Alpha, but not Delta or Omicron. With 90% effectiveness, Delta outbreaks may have been preventable, but not Omicron outbreaks. We also estimated that a decrease in hesitancy from 20% to 14% reduced the number of infections, hospitalizations and deaths by over 30%. Overall, we demonstrate that while herd immunity may not be attainable, modest reductions in hesitancy and increases in vaccine uptake may greatly improve health outcomes. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

The human brain is distinct from those of other species in terms of size, organization, and connectivity. How do these structural evolutionary differences drive patterns of neural activity enabling brain function? Here, we combine brain imaging and biophysical modeling to show that the anatomical wiring of the human brain distinctly shapes neural dynamics. This shaping is characterized by a narrower distribution of dynamic ranges across brain regions compared with that of chimpanzees, our closest living primate relatives. We find that such a sharp dynamic range distribution supports faster integration between regions, particularly in transmodal systems. Conversely, a broad dynamic range distribution as seen in chimpanzees facilitates brain processes relying more on neural interactions within specialized local brain systems. These findings suggest that human brain dynamics have evolved to foster rapid associative processes in service of complex cognitive functions and behavior.