Duncan Edward Astle

Across 25,000 generative-model networks scored on 29 properties, 635 human and 224 mammalian connectomes cluster in one narrow niche, trading wiring economy for computational capability and robustness.

This paper benchmarks 16 brain network similarity measures, finding DeltaCon is most accurate for parameter recovery but no measure excels across all criteria.

The paper identifies a mid-gestational molecular mechanism linking cortical size and ADHD through genetic and single-cell RNA sequencing analyses.

The paper presents a game-theoretic model showing human cortical networks prioritize computational reliability over communication efficiency and wiring cost.

The paper shows that curiosity and brain-like constraints can generate complex neural architectures and cognitive abilities in artificial networks.

The paper examines associations between ADHD and intelligence polygenic scores and developmental dimensions in children with attention, learning, and memory difficulties, finding significant links with cognitive and behavioral traits.

The paper investigates neural dynamics and cortical excitability in ADHD and typically developing individuals, finding a developmental continuum rather than distinct diagnostic differences.

The paper identifies four social profiles in children and adolescents using data-driven methods and links them to cognitive, clinical, and neural outcomes.

The paper identifies a latent variable linking resting-state functional connectivity and psychopathology in children and adolescents, revealing a neurotypical-neurodivergent continuum.

The paper introduces dGAMLSS, a distributed algorithm for fitting GAMLSS models to construct population reference charts without sharing patient data.

This study introduces a heterochronous generative network model to better capture the spatial and temporal aspects of brain network development, improving model fit and identification of individual differences.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

This study identifies homophilic wiring as a principle governing neuronal network topology in vitro, outperforming traditional models in explaining connectivity patterns.

This study uses MEG and a recurrent neural network model to show that reduced inhibition in ZDHHC9-deficient individuals explains altered auditory processing and cortical dynamics.

This study uses HMM to analyze EEG data from preschoolers, finding that brain state transitions correlate with parental reports of behavior and emotion regulation.

The paper investigates how genetic and environmental risks for mental illness overlap in brain connectivity using data from the ABCD cohort.

The paper maps developmental changes in structural and functional brain organisation across neurotypical and neurodivergent individuals, showing stable gradients with plastic coupling in higher-order networks.

The paper identifies molecular differences between the frontal and visual cortex during prenatal development, showing higher gene expression for neuron growth and connectivity in the frontal cortex.

This paper introduces seRNNs, a spatially-embedded recurrent neural network model that reveals structural and functional brain motifs linked to biological optimization.

The paper uses computational modeling and genomics to show that genetic variation influences brain network structure and cognitive ability through a trade-off between connection cost and topological value.

The paper introduces a longitudinal dataset of school-age children and adolescents in China, detailing its design, sampling, and neuroimaging measurements.

This study uses MEG and HMM to analyze resting-state brain dynamics in children, finding that entropy and state transitions correlate with cognitive ability.

The paper models stochasticity in brain development using generative networks and finds that stochastic variation increases under weaker constraints and correlates with socioeconomic status in neurodiverse children.

This paper identifies two neural subtypes in children with elevated inattention and hyperactivity, differentiated by brain network topology and cognitive function.

This paper introduces a weighted generative model of the human connectome that captures both topology and edge-weights, showing improved accuracy over previous models.

This paper uses computational modeling to show that early adversity increases stochasticity in the formation of structural brain networks in mice.

The paper uses generative network models to show that socio-economic disadvantage in children is linked to altered brain network development, specifically slower modularity emergence.

This study compares social and emotional characteristics between females with DDX3X variants and those with other monogenic ID causes, finding higher anxiety and self-injurious behavior in the DDX3X group.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

FarmApp is a touchscreen-based cognitive assessment tool for young people with intellectual disability, showing feasibility, validity, and sensitivity to cognitive changes and genetic influences.