Dick J. Veltman

IBMMA improves neuroimaging analysis by handling missing data and large datasets, revealing new brain region findings overlooked by traditional methods.

This study analyzes fMRI data from OCD patients and controls to identify neural activation patterns during executive control tasks, finding reduced activation in frontoparietal regions and increased default mode network activity in OCD.

The paper examines thalamic volume differences in trauma-exposed individuals with PTSD, MDD, and controls, finding specific volume reductions and associations with symptom severity and childhood trauma.

This study uses normative modeling to analyze cortical thickness deviations in MDD patients and healthy controls, finding large overlaps and significant heterogeneity in extreme deviations across individuals.

This study identifies specific amygdala and hippocampal subfield volume differences in social anxiety disorder compared to healthy controls, with associations to comorbid conditions.

The paper examines hippocampal and amygdala subfield volumes in OCD patients and healthy controls, finding differences based on medication status.

The paper benchmarks and optimizes a normative modeling framework for brain morphometry across the lifespan using MFPR, showing excellent accuracy and stability.

The paper replicates a brain age gap in depression using a large-scale multisite study, finding a similar effect size to a prior study.

This study shows that multimodal multilayer network centrality in the frontoparietal network is positively associated with executive functioning, outperforming unilayer centrality.

This study finds smaller total and subregional cerebellar volumes in PTSD compared to controls using a large dataset and advanced parcellation methods.

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.

This study examines structural covariance networks in PTSD using a large multisite dataset, finding differences in centrality measures between PTSD and control groups, and highlighting sex-based and comorbid differences.

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.

Regressing confounds from inputs leaves confound information machine learning models exploit; adjusting the predictions post hoc instead held false-positive rates near 5%, versus up to 100% with input adjustment.

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

Evaluating four families of machine learning performance metrics on simulated and public neuroimaging data shows classification accuracy performs worst for statistical power, model comparison, feature selection, and reliability.

This paper attempts to replicate a study on depression subtypes using brain connectivity and clinical data but fails to find statistically significant results or distinct subtypes.

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.