Xihe Xie

This paper introduces a time-varying dynamic network model to analyze dynamic resting state functional connectivity in fMRI and MEG data, showing improved performance in capturing brain dynamics and detecting state transitions.

The paper examines associations between neuronal subpopulation parameters and tau/A{beta} in AD patients using MEG and PET data.

This paper shows how functional brain networks can emerge from the structural connectome by using a complex-valued graph Laplacian that outperforms real-valued models.

This paper introduces a spectral graph model to predict brain oscillations from structural connectomes, showing that certain oscillations emerge from network topology.

This study examines parameter identifiability and non-uniqueness in connectome-based neural mass models, finding that regional parameter models produce non-unique results and face challenges in diagnostic applications.