Leon Qi Rong Ooi

Spurious model comparisons are widespread in biomedical artificial intelligence

97% of 184 high-impact biomedical AI studies compare cross-validated models with tests assuming fold independence; in 69% of re-analyzable studies a significant comparison does not survive correction.

Network-based Near-Scalp Personalized Brain Stimulation Targets

This paper develops a tree-based algorithm to identify personalized TMS targets for depression and anxiety by optimizing functional connectivity and scalp proximity.

Associations between epilepsy-related polygenic risk and brain morphology in childhood

The paper examines associations between epilepsy-related polygenic risk scores and brain morphology in children, finding PRS-related cortical thinning linked to structural patterns in TLE-HS patients.

Longer scans boost prediction and cut costs in brain-wide association studies

The paper shows longer scans improve prediction accuracy in brain-wide association studies while reducing costs, with 30-minute scans being the most cost-effective.

Convergent and Divergent Brain-Cognition Development

The paper examines how stable and changing brain network features predict cognitive development in adolescents using rs-fMRI and cognitive data from the ABCD Study.

Protective role of parenthood on age-related brain function in mid- to late-life

This study examines the association between parenthood and brain function in mid- to late-life, finding that higher parity is linked to increased functional connectivity and counteracts age-related declines.

Multimodal neural correlates of childhood psychopathology

The paper identifies multimodal brain network patterns associated with dimensions of psychopathology using the Adolescent Brain Cognitive Development dataset.

Ventral attention network connectivity is linked to cortical maturation and cognitive ability in childhood

The paper links ventral attention network connectivity to cortical maturation and cognitive ability in children using independent datasets.

Sex differences in the functional network underpinnings of psychotic-like experiences in children

The paper examines sex differences in functional brain network connectivity associated with psychotic-like experiences in children using the ABCD Study dataset.

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

The paper introduces multilayer meta-matching to improve phenotypic prediction in small datasets by translating models from multiple large datasets, outperforming classical methods and transfer learning.

Distinct brain network features predict internalizing and externalizing traits in children and adults

The paper shows that brain network features predicting internalizing and externalizing traits differ between children and adults, with more similarity within categories in children and distinct features in adults.

Relationship Between Prediction Accuracy and Feature Importance Reliability: an Empirical and Theoretical Study

This study investigates the relationship between prediction accuracy and feature importance reliability in neuroimaging data, showing that feature importance reliability can be high with sufficient sample size and is positively correlated with prediction accuracy.

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

The paper shows that functional brain connectivity predicts psychiatric illness-linked behaviors differently in males and females, with shared and unique patterns across sexes.

Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

This study compares parcellation and gradient methods for predicting behavior from resting-state functional connectivity in two datasets, finding that individual-specific hard parcellations perform best in HCP and principal gradients perform similarly in ABCD.

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI

This study compares the ability of anatomical, diffusion, and functional MRI to predict individual behavioral measures, finding that functional connectivity provides the best predictions, especially for cognition.

Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features and populations

This study shows that proportional intracranial volume correction affects the predictive accuracy and generalizability of brain-behavior models across anatomical features and populations.

Shared and unique brain network features predict cognition, personality and mental health in childhood

The paper identifies shared and unique brain network features that predict cognition, personality, and mental health in children using resting and task-state functional connectivity.