Ju‐Chi Yu

This study examines BOLD signal variability in autism and schizophrenia, finding reduced variability in these groups compared to controls, linked to poorer cognitive and functional outcomes.

IndivSTATIS integrates individualized brain parcellations into a shared component space to compare network configurations and predict individual differences.

The paper uses fALFF to compare brain activity patterns in ASD, SSD, and TDC groups, finding shared neurobiological features and variability differences.

The paper links higher general intelligence to stable, efficient, and typical dynamic functional brain connectivity patterns using resting-state fMRI data.

This study identifies an insula-driven neural network that tracks decision uncertainty and predicts abstinence in chronic cocaine users.