OBJECTIVE:To investigate the functional connectivity (FC) characteristics of Ascending Reticular Activating System (ARAS) in patients with disorders of consciousness (DOC) following severe traumatic brain injury (sTBI), while introducing the Linear support vector machine (LSVM) to predict the recovery of consciousness. METHODS:Resting-state MRI was used to measure FC changes between the brainstem ARAS nuclei and whole-brain voxels. We compared the differences in FC between sTBI patients and healthy controls, as well as between the wake and DOC groups. Furthermore, the LSVM model for consciousness recovery was developed based on the Z-values of regions of interest (ROIs) and/or scale to distinguish the prognosis of sTBI patients. RESULTS:A total of 28 sTBI patients with DOC and 30 healthy controls were included, with no significant baseline differences (p > 0.05). Using the brainstem ARAS nuclei as the ROI, we observed increased FC in the subcortical regions compared to healthy controls. The strength of FC was significantly different between patients who recovered consciousness and those who did not at 6 months post-sTBI (AlphaSim corrected, p < 0.05, Cluster > 154). Furthermore, the LSVM model demonstrated strong predictive performance, with an area under the receiver operating characteristic curve of 0.81-0.98. CONCLUSIONS:Our study suggest that the disruption FC of ARAS from the subcortex to the cortex may be associated with DOC and prognosis in sTBI patients. Furthermore, the LSVM model shows potential value in distinguishing the recovery of consciousness.

Objective:To investigate the spatial distribution characteristics of alterations in spontaneous brain activity in severe traumatic brain injury (sTBI) patients with disorders of consciousness (DOC), based on the mesocircuit theoretical framework, and to establish models for predicting recovery of consciousness. Methods:Resting-state functional magnetic resonance imaging was employed to measure the mean fractional amplitude of low-frequency fluctuations (mfALFF) in sTBI patients with DOC and healthy controls, identifying differential brain regions for conducting gene and functional decoding analyses. Patients were classified into wake and DOC groups according to Extended Glasgow Outcome Score at 6 months. Furthermore, predictive models for consciousness recovery were developed using Nomogram and Linear Support Vector Machine (LSVM) based on mfALFF. Results:In total, 28 sTBI patients with DOC and 30 healthy controls were included, with no significant baseline differences between groups ( > 0.05). The results revealed increased mfALFF of subcortical Ascending Reticular Activating System and decreased cortical mfALFF (default mode network) in DOC patients within the framework of the mesocircuit model (FDR_ < 0.001, Clusters > 100). The study identified 2080 differentially expressed genes associated with reduced brain activity regions, indicating mechanisms involving synaptic function, the oxytocin signaling pathway, and GABAergic processes in DOC formation. In addition, significantly higher mfALFF values were observed in the left angular gyrus, supramarginal gyrus, and inferior parietal lobule of DOC group compared to the wake group (AlphaSim_ < 0.01, Cluster > 19). The Nomogram prediction model highlighted the pivotal role of these regions' activity levels in prognosis (AUC = 0.90). Validation using LSVM demonstrated robust predictive performance with an AUC of 0.90 and positive predictive values of 80% for wake and 83% for DOC. Conclusions:This study offered crucial insights underlying DOC in sTBI patients, demonstrating the dissociation between cortical and subcortical brain activities. The findings supported the use of mfALFF as a robust and non-invasive biomarker for evaluating brain function and predicting recovery outcomes.

BACKGROUND:Accurate evaluation of level of disorder of consciousness (DOC) is clinically challenging. OBJECTIVE:This study aimed to establish a distinctive DOC-related pattern (DOCRP) for assessing disease severity and distinguishing unresponsive wakefulness syndrome (UWS) from minimally conscious state (MCS). METHODS:Fifteen patients with DOC and eighteen health subjects with F-18-fluorodeoxyglucose (F-18-FDG) positron emission tomography (PET) were enrolled in this study. All patients were assessed by Coma Recovery Scale-Revised (CRS-R) and all individuals were randomly divided into two cohorts (Cohort A and B). DOCRP was identified in Cohort A and subsequently validated in Cohort B and A+B. We also assessed the discriminatory power of DOCRP between MCS and UWS. RESULTS:The DOCRP was characterized bilaterally by relatively decreased metabolism in the medial and lateral frontal lobes, parieto-temporal lobes, cingulate gyrus and caudate, associated with relatively increased metabolism in the cerebellum and brainstem. DOCRP expression exhibited high accuracy in differentiating DOC patients from controls (P<0.0001, AUC=1.000), and furthermore could effectively distinguish MCS from UWS (P=0.037, AUC=0.821, sensitivity: 85.7 %, specificity: 75.0 %). Particularly in the subgroup of DOC patients survived global hypoxic-ischemic brain injury, DOCRP expression exhibited even better discriminatory power between MCS and UWS (P=0.046, AUC=1.000). CONCLUSIONS:DOCRP might serve as an objective biomarker in distinguishing between UWS and MCS, especially in patients survived global hypoxic-ischemic brain injury. TRIAL REGISTRATION NUMBER:ChiCTR2300073717 (Chinese clinical trial registry site, http://www.chictr.org).

Assessing communication abilities in patients with disorders of consciousness (DOCs) is challenging due to limitations in the behavioral scale. Electroencephalogram-based brain-computer interfaces (BCIs) and eye-tracking for detecting ocular changes can capture mental activities without requiring physical behaviors and thus may be a solution. This study proposes a hybrid BCI that integrates EEG and eye tracking to facilitate communication in patients with DOC. Specifically, the BCI presented a question and two randomly flashing answers (yes/no). The subjects were instructed to focus on an answer. A multimodal target recognition network (MTRN) is proposed to detect P300 potentials and eye-tracking responses (i.e., pupil constriction and gaze) and identify the target in real time. In the MTRN, the dual-stream feature extraction module with two independent multiscale convolutional neural networks extracts multiscale features from multimodal data. Then, the multimodal attention strategy adaptively extracts the most relevant information about the target from multimodal data. Finally, a prototype network is designed as a classifier to facilitate small-sample data classification. Ten healthy individuals, nine DOC patients and one LIS patient were included in this study. All healthy subjects achieved 100% accuracy. Five patients could communicate with our BCI, with 76.1±7.9% accuracy. Among them, two patients who were noncommunicative on the behavioral scale exhibited communication ability via our BCI. Additionally, we assessed the performance of unimodal BCIs and compared MTRNs with other methods. All the results suggested that our BCI can yield more sensitive outcomes than the CRS-R and can serve as a valuable communication tool.

Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest. The networks associated with these patterns have been extensively analyzed. However, the overall dynamic organization and how it relates to consciousness remains unclear. We hypothesized that deep learning networks would help to model this relationship. Recent studies have used low-dimensional variational autoencoders (VAE) to learn meaningful representations that can help explaining consciousness. Here, we investigated the complexity of selecting such a generative model to study brain dynamics, and extended the available methods for latent space characterization and modeling. Therefore, our contributions are threefold. First, compared with probabilistic principal component analysis and sparse VAE, we showed that the selected low-dimensional VAE exhibits balanced performance in reconstructing dFCs and classifying brain patterns. We then explored the organization of the obtained low-dimensional dFC latent representations. We showed how these representations stratify the dynamic organization of the brain patterns as well as the experimental conditions. Finally, we proposed to delve into the proposed brain computational model. We first applied a receptive field analysis to identify preferred directions in the latent space to move from one brain pattern to another. Then, an ablation study was achieved where we virtually inactivated specific brain areas. We demonstrated the model's efficiency in summarizing consciousness-specific information encoded in key inter-areal connections, as described in the global neuronal workspace theory of consciousness. The proposed framework advocates the possibility of developing an interpretable computational brain model of interest for disorders of consciousness, paving the way for a dynamic diagnostic support tool.

Disorders of consciousness (DoC) are generally diagnosed by clinical assessment, which is a predominantly motor-driven process and accounts for up to 40 % of non-communication being misdiagnosed as unresponsive wakefulness syndrome (UWS) (previously known as prolonged/persistent vegetative state). Given the consequences of misdiagnosis, a more reliable and objective multimodal protocol to diagnosing DoC is needed, but has not been produced due to concerns regarding their interpretation and reliability. Of the techniques commonly used to detect consciousness in DoC, task-based paradigms (active paradigms) produce the most unequivocal result when findings are positive. It is well-established that command following (CF) reliably reflects preserved consciousness. Task-based electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can detect motor-independent CF and reveal preserved covert consciousness in up to 14 % of UWS patients. Accordingly, to improve the diagnostic accuracy of DoC, we propose a practical multimodal clinical decision framework centered on task-based EEG and fMRI, and complemented by measures like transcranial magnetic stimulation (TMS-EEG).

BACKGROUND:Disorders of consciousness (DoC) refers to a group of clinical conditions of altered consciousness. To improve their diagnosis and prognosis, multimodal assessment can be of great importance. Informal caregivers of people with DoC who are confronted with new technologies as such can benefit from interventions to expand their health literacy, i.e., the ability to use information to make health decisions for oneself and others. METHODS:We developed an information brochure on multimodal assessment for DoC in a participatory process, with decisions made by a steering group. The process was based on a methodological framework for the development of patient decision aids that built on the International Patient Decision Aid Standards (IPDAS). RESULTS:On the background of a broad variety of needs, the priority was to focus on the explanation of multimodal testing and provide information about its uncertainty. Its development aimed at enhancing informal caregivers' understanding of implications of results from multimodal assessment and its relevance for prognosis. It should avoid the portrayal of information that could lead to the impression of false hope or suboptimal rehabilitation care. Informal caregivers rated its usability and acceptability highly, though they preferred less technical language. CONCLUSION:The participatory process was crucial to the project. Future studies should investigate the effectiveness of the brochure in fostering informal caregivers' health literacy. PATIENT OR PUBLIC CONTRIBUTION:Informal caregivers of people with DoC were deliberately included in the steering group and they participated in a field test of the prototype brochure.