Joe Bathelt

This study finds that autism polygenic scores are associated with increased variability in brain network modularity in males but not females among non-autistic adults.

The paper introduces a neuroimaging dataset of autistic and non-autistic adults with extended functional scans to study individual differences in brain function.

The paper uses multilayer network analysis to explore associations between environmental exposures and brain development in children, finding dense connections within environmental and brain layers but sparse connections between them.

This paper combines network psychometrics and network neuroscience to analyze behavioral and neural correlates in autism, revealing unique and shared brain mechanisms.

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.

The paper examines the relationship between white matter connectome efficiency, intelligence, and educational attainment in children, finding that distributed network properties are more predictive than voxel-wise analyses.

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.

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.

This study examines how structural brain correlates of working memory components change with age in children, finding shifts from distributed to specialized neural circuits.