Xuehai Wu

DeepPrep accelerates neuroimaging preprocessing by 11-fold using deep learning, outperforming current state-of-the-art methods in scalability and robustness.

This study investigates whether statistical learning occurs in patients with impaired consciousness by analyzing their ability to extract regularities from an artificial language using EEG frequency tagging.

The paper identifies that co-activation of the ventral posterolateral nucleus and global brain activity correlates with physiological arousal but not perceptual awareness.

This paper uses resting-state fMRI to identify cognitive motor dissociation by analyzing temporal stability in brain networks, achieving high accuracy in distinguishing CMD from non-CMD.

The paper identifies cortical hubs associated with consciousness recovery in UWS patients and finds the anterior precuneus is a key region.

The paper identifies higher-order sensorimotor brain hubs that correlate with consciousness levels across different unconscious states using resting-state fMRI data.

The paper investigates intrinsic neural timescales in different behavioural states and finds that sensory deficits are associated with prolonged neural timescales, while motor deficits do not show such changes.