Kaichao Wu

The paper maps intrinsic neural timescales across rest and tasks, showing task-induced changes while preserving temporal hierarchy and structural constraints.

This paper introduces AMAge-Net, a multimodal brain age prediction model combining fMRI and sMRI, achieving high accuracy and interpretability in estimating brain aging.

This study shows that stroke alters intrinsic neural timescales and long-range temporal correlations, indicating a shift toward criticality and potential as biomarkers for recovery.

The paper maps age-related changes in intrinsic neural timescales and links them to gray matter volume and cognitive performance in young and elderly adults.