Auditory evoked potentials (AEPs), specifically the N100 component and the auditory steady-state response (ASSR), are used to assess patients with disorders of consciousness (DOC), but their relative value is debated. In 30 DOC patients, the ASSR response was a more reliable marker of auditory function than N100. However, neither N100 nor ASSR, at response or microstate levels, could effectively distinguish between patients with unresponsive wakefulness syndrome (UWS) and those in a minimally conscious state (MCS). The role of ASSR was validated using a portable EEG device in an independent cohort of 30 patients. ASSR shows promise for assessing auditory function in DOC patients, but its utility in differentiating levels of consciousness may require further consideration.
In patients with prolonged disorders of consciousness, a one-hour intravenous infusion of esketamine (0.3 mg/kg/h) altered brain activity as measured by electroencephalography. The drug suppressed delta wave power while increasing beta and gamma wave power across the whole brain, and increased alpha wave power specifically in patients diagnosed as vegetative state/unresponsive wakefulness syndrome. Signal complexity, measured by Lempel-Ziv complexity, increased in the parietal and occipital brain regions. In minimally conscious patients, this complexity increase persisted for 30 minutes after the infusion stopped, while changes were transient in vegetative state patients. However, these neurophysiological changes were not accompanied by any improvements in behavioral responsiveness as assessed by the Coma Recovery Scale-Revised.
A framework using electroencephalography (EEG) and nonlinear analysis methods can differentiate four states of consciousness: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. Permutation entropy (PE) distinguished all four states. Altered contents of consciousness were best differentiated by sample entropy (SampEn) and permutation Lempel-Ziv complexity (PLZC), while levels of consciousness were best differentiated by relative power of Gamma and PE. A multi-dimensional index combining PE, PLZC, SampEn, and detrended fluctuation analysis (DFA) achieved 92.3% classification accuracy using a genetic algorithm-based support vector machine (GA-SVM), outperforming random forest and neural networks. A multivariable linear regression model constructed coordinate values for level and content dimensions.