Aihuiping Xue

The paper uses fMRI data to estimate hierarchical networks in the human cerebral cortex, revealing a global organization with distinct functional properties across different network orders.

The paper evaluates how different censoring strategies affect cortical parcellations and TMS targets in rs-fMRI, finding that lenient censoring yields better results than strict censoring.

This paper develops a tree-based algorithm to identify personalized TMS targets for depression and anxiety by optimizing functional connectivity and scalp proximity.

The paper introduces the Network Correspondence Toolbox (NCT) to evaluate spatial correspondence between novel neuroimaging results and existing brain atlases, enabling standardized reporting and comparison across studies.

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.

The paper develops individual-specific areal-level parcellations using a Bayesian model and shows they improve functional connectivity prediction of behavior compared to group-level parcellations.

The paper uses functional connectivity MRI to map the detailed organization of the human cerebellum in two individuals, revealing multiple networks and hierarchical structures.