Di Zang

TL;DR: EEG-based language assessment provides a new and reliable approach to objectively characterize and predict states of consciousness and to longitudinally track individual patients’ language processing abilities at the bedside.

TL;DR: The results establish that the spontaneous activity's intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain.

TL;DR: It is demonstrated that regions forming a higher-order sensorimotor integration circuit are involved in supporting consciousness within the brain's global functional network, which offers novel and more mechanism-guided treatment targets for disorders of consciousness.

TL;DR: A novel machine-learning method was applied to find the most discriminative features from the multi-modal neuroimages to regress the JFK Coma Recovery Scale-Revised (CRS-R) scores to quantify the level of consciousness and showed that the regression model has high potential for the evaluation of consciousness in clinical practice.

TL;DR: It is established that the spontaneous activity’s intrinsic neural timescale is related to specifically sensory rather than motor information processing in the healthy brain.