Chun Hei Michael Chan

The paper evaluates how well population-level connectome harmonics represent individual brains across different dimensions and finds they can be useful despite not perfectly matching individual data.

This study evaluates microstructure-weighted connectomes using diffusion MRI data and finds that DePerp provides the most consistent structure-function coupling across spatial scales.

This study examines how individual differences in anxiety and openness relate to brain activity during emotional film scenes, using emotion-informed functional gradients to improve prediction of these traits.

This paper shows that individualized connectome harmonics reveal a hierarchical organization linking brain structure, function, and cognition across 875 participants.

This paper introduces directed connectomics using bicommunities to reveal asymmetric pathways in brain networks and validates them with electrophysiological data.

This paper develops a whole-brain model to predict conduction velocity from MRI-derived microstructural features, explaining 29% of variance in neurophysiological conduction velocity.

The paper introduces YeaZ, a CNN for accurate yeast cell segmentation without fluorescent markers, outperforming existing methods and revealing new biology in cell morphology.