Background and objective Electroencephalography (EEG) and neuroimaging measurements have been highly encouraged to be applied in clinics of disorders of consciousness (DOC) to improve consciousness detection. We tested the relationships between neural complexity measured on EEG and residual consciousness levels in DOC patients. Methods Resting-state EEG was recorded from twenty-five patients with DOC. Lempel–Ziv complexity (LZC) and permutation Lempel–Ziv complexity (PLZC) were measured on the EEG, and their relationships were analyzed with the consciousness levels of the patients. Results PLZC and LZC values significantly distinguished patients with a minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS), and healthy controls. PLZC was significantly correlated with the Coma Recovery Scale-Revised (CRS-R) scores of DOC patients in the global brain, particularly in electrodes locating in the anterior and posterior brain regions. Patients with higher CRS-R scores showed higher PLZC values. The significant difference in PLZC values between MCS and VS/UWS was mainly located in the bilateral frontal and right hemisphere regions. Conclusion Neural complexity measured on EEG correlates with residual consciousness levels of DOC patients. PLZC showed higher sensitivity than LZC in the classification of consciousness levels.

The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer, graph theoretical analysis (GTA)-based approaches cannot always reliably differentiate patients with Disorders of Consciousness (DoC). Using multiplex and multilayer network analyses of frequency-specific and area-specific networks, we investigated functional connectivity during resting-state EEG in 17 patients with Unresponsive Wakefulness Syndrome (UWS) and 15 with Minimally Conscious State (MCS). Multiplex and multilayer network metrics indicated the deterioration and heterogeneity of functional networks and, particularly, the frontal-parietal (FP), as the discriminant between patients with MCS and UWS. These data were not appreciable when considering each individual frequency-specific network. The distinctive properties of multiplex/multilayer network metrics and individual frequency-specific network metrics further suggest the value of integrating the networks as opposed to analyzing frequency-specific network metrics one at a time. The hub vulnerability of these regions was positively correlated with the behavioral responsiveness, thus strengthening the clinically-based differential diagnosis. Therefore, it may be beneficial to adopt both multiplex and multilayer network analyses when expanding the conventional GTA-based analyses in the differential diagnosis of patients with DoC. Multiplex analysis differentiated patients at a group level, whereas the multilayer analysis offered complementary information to differentiate patients with DoC individually. Although further studies are necessary to confirm our preliminary findings, these results contribute to the issue of DoC differential diagnosis and may help in guiding patient-tailored management.

Humans have conscious experiences of the events in their environment. Previous research from electroencephalography (EEG) has shown visual awareness negativity (VAN) at about 200 ms to be a neural correlate of consciousness (NCC). However, when considering VAN as an NCC, it is important to explore which particular experiences are associated with VAN. Recent research proposes that VAN is an NCC of lower-level experiences (detection) rather than higher-level experiences (identification). However, previous results are mixed and have several limitations. In the present study, the stimulus was a ring with a Gabor patch tilting either left or right. On each trial, subjects rated their awareness on a three-level perceptual awareness scale that captured both detection (something vs. nothing) and identification (identification vs. something). Separate staircases were used to adjust stimulus opacity to the detection threshold and the identification threshold. Bayesian linear mixed models provided extreme evidence (BF10 = 131) that VAN was stronger at the detection threshold than at the identification threshold. Mean VAN decreased from -\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}2.12 microV [-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}2.86, -\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}1.42] at detection to -\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}0.46 microV [-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}0.79, -\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}0.11] at identification. These results strongly support the claim that VAN is an NCC of lower-level experiences of seeing something rather than of higher-level experiences of specific properties of the stimuli. Thus, results are consistent with recurrent processing theory in that phenomenal visual consciousness is reflected by VAN. Further, results emphasize that it is important to consider the level of experience when searching for NCC.

The diagnosis of consciousness has always been a major challenge in clinical diagnosis. Resent researches prove that machine learning has a powerful ability to distinguish between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS). What’s more, convolutional neural network has made great progress in electroencephalography (EEG) analysis of other disorders. As a result, an improved 1D-convolutional neural network structure has been proposed for outcome prediction, using resting-state EEG signals from patients with disorders of consciousness. The model is established by training 690 EEG segments from 34 of MCS and 35 of UWS diagnosed by Coma Recovery Scale – Revised. The experimental results show that the accuracy, positive predictive value, specificity and sensitivity of the improved model in our research are 88.84%, 85.59%, 86.79% and 91.22%, respectively. It shows that our improved model has better performance than the model without Batch Normalization layer, as well as the model with deep graph convolutional neural network. The improved 1D-convolutional neural network model in this study can be used as an auxiliary medical method for clinical diagnosis and detection of consciousness disorders. More profoundly, it could drive the development of robust expert systems in other neurological diseases.

Neuroscience has generated a number of recent advances in the search for the neural correlates of consciousness, but these have yet to find valuable real-world applications. Electroencephalography under anesthesia provides a powerful experimental setup to identify electrophysiological signatures of altered states of consciousness, as well as a testbed for developing systems for automatic diagnosis and prognosis of awareness in clinical settings. In this work, we use deep convolutional neural networks to automatically differentiate sub-anesthetic states and depths of anesthesia, solely from one second of raw EEG signal. Our results with leave-one-participant-out-cross-validation show that behavioral measures, such as the Ramsay score, can be used to learn generalizable neural networks that reliably predict levels of unconsciousness in unseen transitional anesthetic states, as well as in unseen experimental setups and behaviors. Our findings highlight the potential of deep learning to detect progressive changes in anesthetic-induced unconsciousness with higher granularity than behavioral or pharmacological markers. This work has broader significance for identifying generalized patterns of brain activity that index states of consciousness.Clinical Relevance- In the United States alone, over 100,000 people receive general anesthesia every day, from which up to 1% is affected by unintended intraoperative awareness [1]. Despite this, brain-based monitoring of consciousness is not c

l, to establish states of awareness in a large population of patients with unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS). 1 Using the DoC-Forest approach, the authors demonstrated that combining multiple EEG data (including alpha-band power, theta-band connectivity, and time series complexity) in the analysis provides complementary information to clinical assessments of states of consciousness, significantly reducing the influence of different EEG configurations and experimental protocols on the distribution and performance of the EEG markers. The gold-standard approach for diagnosing a consciousness disorder is repeated clinical assessment using the Coma Recovery Scale (CRS-R). 2 , 3 However, even the best standardized behavioral assessments can miss signs of residual conscious processing in some patients. Using advanced para-clinical approaches, these signs are more easily detected, 4 , 5 and the patients may be labeled as “with covert awareness” or “with cognitive-motor dissociation." 6 , 7 We have shown that different experimental approaches based on EEG data are useful in refining the clinical diagnosis in cases of consciousness, 8 , 9 as recently as that proposed by Engemann et al. 1 As the authors confirmed, the DoC-Forest complex analyses tool consistently demonstrated its usefulness in differentiating states of consciousness. EEG analysis offers rich temporal information on cognitive operations, capturing even small fluctuations in awareness, which are not only biasing factors when attempting to differentiate disorders of consciousness, but are important predictors of awareness recovery. 10 Additionally, EEG analysis using DoC-Forest could potentially be used at bedside or during home assessment. The authors highlight the importance of approaching EEG markers with DoC-Forest. Quantitative metrics of specific neural networks have been shown to correlate with the continuum of behavioral recovery in patients with disorders of consc

Functional near-infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging tool for assessing patients with disorders of consciousness (DoC). While functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) have advanced the detection of covert brain function, their use is often constrained by accessibility, medical and physical contraindications, and practical limitations. fNIRS offers a portable, safe, and cost-effective alternative capable of measuring hemodynamic responses at the bedside. In this perspective, we discuss the clinical motivation for integrating fNIRS into DoC patient assessments, summarize recent advancements in the application of fNIRS for examining brain function, and outline the clinical and technical advantages. We highlight key future directions of fNIRS research, including large-scale validation, multimodal integration, and the development of fNIRS-based brain-computer interfaces. Finally, we address the ethical imperative to ensure equitable access to neurotechnologies capable of detecting covert brain function. With continued methodological refinement and standardization, fNIRS may significantly transform the diagnostic, prognostic, and communicative landscape of DoC care.