Jianzhong Chen

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.

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

The paper develops homotopic local-global parcellations of the human cerebral cortex using resting-state and task-fMRI, showing improved homogeneity and lateralization compared to existing parcellations.

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.

The paper introduces a framework called meta-matching to improve prediction performance in small datasets by leveraging correlations with large-scale phenotypic data.