Liang Ma

The paper introduces CNTC to analyze nonlinear developmental trajectories across brain networks using fMRI data from infants, finding linear and selective nonlinear effects across systems.

The paper identifies altered default mode network connectivity dynamics in schizophrenia using a novel iterative feature-removal clustering approach on resting-state fMRI data.

PhaseICA extracts reoccurring spatiotemporal brain patterns by incorporating phase information, improving the analysis of nonstationary brain activity.

This study links short-term brain dynamics to long-term changes in the functional connectome during postnatal development, finding a strong association between specific brain states and non-linear developmental acceleration.

The paper creates a macaque brain atlas using diffusion MRI data to map connectivity, parcellation, and histology, revealing structural differences between macaque and human brains.

BAI-Net improves individualized brain mapping by integrating topological priors and connectivity data, showing better performance than traditional clustering methods.

This paper develops a statistical framework for connexel-wise analysis in brain-wide association studies, improving power and reducing false positives using spatial information from fMRI data.