Christopher R. K. Ching

This study evaluates how sample size affects machine learning performance in classifying dementia stages using subcortical brain features from 2,511 ADNI samples.

The paper introduces a 3D brain geometry toolkit for multisite neuroimaging analysis, showing subcortical shape measures improve diagnostic and treatment prediction compared to traditional volume features.

This study analyzes structural brain network changes in bipolar disorder, finding widespread connectivity disruptions linked to illness severity and medication use.

This study identifies genetic maps of human sulcal complexity using rare and common variants, revealing new mechanistic pathways for cortical morphogenesis.

This study investigates brain age differences in youth with conduct disorder compared to controls, finding accelerated brain maturation in those with adolescence-onset CD.

This study compares deterministic and probabilistic tractography methods for the left arcuate fasciculus across four datasets, finding probabilistic methods yield higher inter-subject shape similarity but may obscure subtle pathological variations.

The paper introduces a scalable toolkit for 3D surface-based brain analysis, demonstrating its utility in mapping subcortical shape variations across 21 international samples.

This paper applies a spherical harmonics-based deep learning model to MRI-derived brain shape metrics to predict age, sex, and Alzheimer's disease diagnosis, showing strong performance in classification and regression tasks.

The paper conducts GWAS on brain volumes, identifies genetic loci, and develops polygenic scores with cross-ancestry predictive ability and causal links to neuropsychiatric disorders.

This study examines how 22q11.2 copy number variations affect subcortical brain volumes and their development across different age groups.

This study maps microstructural abnormalities along white matter tracts in bipolar disorder using multisite diffusion MRI tractometry and identifies significant differences in fractional anisotropy between BD patients and controls.

This study examines subcortical brain alterations in CNV carriers across multiple neuropsychiatric disorders, finding shared and distinct patterns of structural changes.

The paper identifies a shared cortical morphology dimension across six psychiatric conditions using MRI data from 28,546 participants.

This paper compares four methods for harmonizing cortical thickness data across multiple sites and finds that ComBat-GAM is most effective in detecting case-control differences and age-related cortical thinning.

The paper investigates how molecular and connectomic vulnerabilities contribute to cortical abnormalities across multiple brain disorders using MRI data from 47,000 individuals.

The paper analyzes cortical thickness trajectories in 17,075 individuals to show peak thickness in childhood, steep decline in early adulthood, and slower decline later, with notable exceptions and significant inter-individual variability.

The paper examines genetic correlations between neuropsychiatric disorders using structural MRI data from the ENIGMA consortium, finding significant overlaps in brain structure and genetic architecture.

The paper creates normative percentile charts for subcortical brain volumes across adulthood and examines age-by-sex interactions and associations with APOE genotype in a large UK Biobank sample.

The paper analyzes sex differences in brain structure variability across the lifespan using a large dataset, finding greater male variability in subcortical volumes, cortical surface area, and thickness in most regions.

This paper examines brain aging in major depressive disorder (MDD) using a large dataset, finding that MDD patients show increased brain age differences compared to controls.

This study analyzes subcortical volume changes across the lifespan using MRI data from 18,605 individuals, finding age-related declines in some structures and increases in others.

This study identifies subcortical shape differences in MDD patients using ENIGMA data, highlighting region-specific changes in surface area and thickness.

The paper identifies genetic loci associated with human cortical structure and finds genetic correlations with neuropsychiatric disorders.

This study identifies hippocampal subfield volume differences in PTSD and depression across 31 cohorts, finding depression has a stronger effect than PTSD, especially in CA1, with comorbid cases showing more pronounced changes.

This study finds smaller limbic brain volumes are associated with greater immunosuppression in HIV+ adults across five continents.

The paper identifies genetic associations with cortical surface area using PCA and GWAS, linking DAAM1 to visual cortex development through mitochondrial pathways and thalamocortical input.

This study finds that perinatal exposure to maternal smoking is associated with reduced cortical surface area in specific brain regions and increased disease risk in middle-aged and older adults.

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.