Danyal Akarca

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

The paper examines functional connectivity patterns in youth at neurodevelopmental risk, linking mental health dimensions to brain network organization.

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.

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 a generative model of the connectome that simulates dynamic axon growth, producing brain-like network structures and revealing how axonal guidance principles shape macroscopic connectomes.

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.

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.

The paper uses network analysis to model brain structural covariance and cognitive abilities in struggling learners, finding positive correlations and intermediary roles of specific nodes.

This paper introduces a computational framework to model brain network development, showing how genetic and topological factors shape neurodiverse outcomes through iterative wiring rules.

The paper uses MEG data and Hidden Markov Modelling to analyze brain network dynamics in individuals with ZDHHC9-associated intellectual disability compared to controls, finding differences in resting state activation linked to gene expression.