Chris L. Adamson

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

The paper shows that brain abnormalities after preterm birth are spatially heterogeneous but temporally consistent and plastic in response to social environments.

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.

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

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.

This paper benchmarks geometric and traditional deep learning models for cortical segmentation and neurodevelopmental phenotype prediction using neonatal cortical surface data.

This paper introduces surface-based versions of neonatal brain atlases and evaluates their parcellation accuracy compared to manual labels and an adult-derived atlas.

This study identifies brain morphometry features that predict individual differences in ASD symptom severity using a subject-level machine learning approach.

The paper updates the M-CRIB neonatal brain atlas to align with the Desikan-Killiany-Tourville adult atlas and FreeSurfer subcortical segmentations, resulting in a more compatible and accurate whole-brain parcellation.

This study models age and sex-related neuroanatomical variation during childhood and adolescence using multimodal imaging data and machine learning, achieving accurate age prediction and sex classification.