Ruiqi Chen

This paper develops a computational framework to model how task-induced changes in brain dynamics relate to cognitive individual differences using rsfMRI and tfMRI data.

The paper models neural dynamics on an attractor landscape to show how attentional states and contexts alter the geometry of brain activity trajectories.

This paper shows that combining hierarchical Bayesian modeling with multivariate decoding improves the reliability and precision of estimating individual differences in cognitive control from fMRI data.

The paper constructs dynamical models of resting-state brain networks from fMRI data, revealing anatomically reliable attractor landscapes that go beyond conventional functional connectivity analyses.

This paper replicates and improves mental health trait predictions using fMRI data through data manipulations like feature expansion and phenotype balancing.