Rukuang Huang

What drives apparent dynamic functional connectivity in resting-state fMRI?

This study investigates the drivers of dynamic functional connectivity in resting-state fMRI using HMMs and finds that mean activity and variance dynamics explain most of the apparent connectivity changes.

Bi-cross-validation: a data-driven method to evaluate dynamic functional connectivity models in fMRI

This paper introduces bi-cross-validation to evaluate and compare dynamic functional connectivity models in fMRI, showing it balances model fit and complexity effectively.

Canonical Hidden Markov Model Networks for Studying M/EEG

The paper introduces canonical Hidden Markov Models (HMMs) for M/EEG data, enabling consistent network analysis across studies by using pre-trained models instead of training new ones for each dataset.

Modelling Discrete States and Long-Term Dynamics in Functional Brain Networks

This paper introduces DyNeStE, a model that combines recurrent neural networks with Bayesian inference to capture long-term dynamics in brain networks while maintaining interpretability, outperforming HMMs in simulations and resting-state MEG data.

Modelling variability in functional brain networks using embeddings

This paper introduces HIVE, a method using embeddings and HMMs to model individual brain network variability across groups, showing improved performance on simulated and real MEG data.

Evidence for transient, uncoupled power and functional connectivity dynamics

This paper introduces M-DyNeMo, a method that reveals uncoupled dynamics between power and functional connectivity in MEG data, showing task-related modulation of these dynamics.

Dynamic Network Analysis of Electrophysiological Task Data

The paper compares HMM and DyNeMo for analyzing task-related oscillatory brain activity, showing DyNeMo detects more task activations than traditional methods.