the CALM Team

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.

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

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

The paper examines age-related changes in functional brain connectivity in neurodevelopmentally at-risk children compared to a control group, finding divergent developmental trends in network integration and segregation.

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 models fluid intelligence as a hierarchical system where white matter influences intermediate cognitive traits, which in turn affect fluid intelligence, showing age-related changes in the relationship.

This study uses a transdiagnostic approach to analyze cognitive, behavioral, and neural data from children with learning difficulties to identify underlying mechanisms.

This study uses machine learning to identify cognitive and neural profiles in a large heterogeneous sample of struggling learners, revealing distinct groups with similar learning profiles despite different cognitive characteristics.

The paper identifies two brain types based on white-matter microstructure in children and adolescents, showing distinct cognitive abilities and structural connectivity differences.

The paper examines cognitive and behavioral dimensions in children with learning difficulties and ADHD, finding distinct cognitive deficits linked to learning challenges and ADHD symptoms.

This study uses data-driven clustering to identify subtypes of executive function-related behavioral problems in children, finding three distinct groups linked to specific neurobiological patterns.