Lianne Schmaal

This study identifies a shared genetic factor underlying brain age gaps across 30 cohorts, finding 19 SNPs and associations with health traits.

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

This study identifies lifestyle, early-life, and genetic factors associated with brain age gaps in individuals with major depressive disorder compared to controls.

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 altered effective connectivity in the default mode network during reflected self-appraisal in social anxiety disorder using fMRI and dynamic causal modeling.

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

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 paper develops a hierarchical Bayesian model to account for site effects in neuroimaging data, showing improved predictive performance compared to harmonization techniques.

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

This study shows that global brain health, measured by brain age, influences post-stroke sensorimotor outcomes and mediates the effect of lesion damage.

HALFpipe provides a reproducible, interactive, and efficient analysis pipeline for fMRI data, supporting preprocessing, post-processing, and quality assessment.

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 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 maps individual deviations from normative brain structure in schizophrenia patients and finds high heterogeneity, with most patients showing infra-normal deviations in at least one region.

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.

The paper identifies neurovegetative symptom subtypes in young people with MDD and finds structural brain correlates in the anterior insula.

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 study compares structural brain differences across ADHD, ASD, and OCD using MRI data from multiple cohorts, finding disorder-specific alterations in brain volume and cortical thickness.

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%.

The paper identifies 25 genetic loci associated with subcortical brain structure volumes in over 40,000 individuals, revealing novel biology and potential drug targets.

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.