The current and future contribution of neuroimaging to the understanding of disorders of consciousness

Consciousness trajectories and functional independence after acute brain injury in children with prolonged disorder of consciousness

AIM

To explore the trajectories of consciousness recovery and prognosis-associated predictors in children with prolonged disorder of consciousness (pDoC).

METHOD

This single-centre, retrospective, observational cohort involved 134 (87 males, 47 females) children diagnosed with pDoC and hospitalized at the Department of Rehabilitation at the Children's Hospital of Chongqing Medical University in China. The median onset age was 30 (interquartile range [IQR] 18-54) months, with onset ages ranging from 3 to 164 months. Least absolute shrinkage and selection operator (LASSO) regression and logistic regression analyses were performed to identify the independent predictors of consciousness recovery at 1 year after brain injury. Discrimination and calibration were assessed using 1000 bootstrap resamples. The potential predictors of resultant living independence were also explored.

RESULTS

The predictors for consciousness recovery at 1-year postinjury were: traumatic brain injury (odds ratio [OR]: 3.26, 95% confidence interval [95% CI]: 1.21-9.46), electroencephalogram (EEG) grade IV or below based on Young's classification (OR: 3.41, 95% CI: 1.38-8.70), and no bilateral impairments in the basal ganglia (OR: 3.75, 95% CI: 1.50-9.91) or posterior cingulate (OR: 5.61, 95% CI: 2.20-15.54). A nomogram was constructed with the area under the curve of 0.845 (95% CI: 0.780-0.911). Additionally, EEG grade IV or below, and the absence of bilateral impairments in the frontal lobes and occipital lobes were associated with favorable functional outcomes.

INTERPRETATION

These findings underscore the importance of comprehensive early-stage assessments in evaluating consciousness and function, assisting clinicians and families in making clinical decisions.

EEG-based responses of patients with disorders of consciousness and healthy controls to familiar and non-familiar emotional videos

OBJECTIVE

To investigate the differences in the brain responses of healthy controls (HC) and patients with disorders of consciousness (DOC) to familiar and non-familiar audiovisual stimuli and their consistency with the clinical progress.

METHODS

EEG responses of 19 HC and 19 patients with DOC were recorded while watching emotionally-valenced familiar and non-familiar videos. Differential entropy of the EEG recordings was used to train machine learning models aimed to distinguish brain responses to stimuli type. The consistency of brain responses with the clinical progress of the patients was also evaluated.

RESULTS

Models trained using data from HC outperformed those for patients. However, the performance of the models for patients was not influenced by their clinical condition. The models were successfully trained for over 75% of participants, regardless of their clinical condition. More than 75% of patients whose CRS-R scores increased post-study displayed distinguishable brain responses to both stimuli.

CONCLUSIONS

Responses to emotionally-valenced stimuli enabled modelling classifiers that were sensitive to the familiarity of the stimuli, regardless of the clinical condition of the participants and were consistent with their clinical progress in most cases.

SIGNIFICANCE

EEG responses are sensitive to familiarity of emotionally-valenced stimuli in HC and patients with DOC.

Neuroimaging biomarkers for the diagnosis and prognosis of patients with disorders of consciousness

The progress in neuroimaging and electrophysiological techniques has shown substantial promise in improving the clinical assessment of disorders of consciousness (DOC). Through the examination of both stimulus-induced and spontaneous brain activity, numerous comprehensive investigations have explored variations in brain activity patterns among patients with DOC, yielding valuable insights for clinical diagnosis and prognostic purposes. Nonetheless, reaching a consensus on precise neuroimaging biomarkers for patients with DOC remains a challenge. Therefore, in this review, we begin by summarizing the empirical evidence related to neuroimaging biomarkers for DOC using various paradigms, including active, passive, and resting-state approaches, by employing task-based fMRI, resting-state fMRI (rs-fMRI), electroencephalography (EEG), and positron emission tomography (PET) techniques. Subsequently, we conducted a review of studies examining the neural correlates of consciousness in patients with DOC, with the findings holding potential value for the clinical application of DOC. Notably, previous research indicates that neuroimaging techniques have the potential to unveil covert awareness that conventional behavioral assessments might overlook. Furthermore, when integrated with various task paradigms or analytical approaches, this combination has the potential to significantly enhance the accuracy of both diagnosis and prognosis in DOC patients. Nonetheless, the stability of these neural biomarkers still needs additional validation, and future directions may entail integrating diagnostic and prognostic methods with big data and deep learning approaches.

Cognitive Effects of Transcranial Direct Current Stimulation Plus Robotic Verticalization in Minimally Conscious State

BACKGROUND AND OBJECTIVES

Transcranial direct current stimulation (tDCS) is a non-invasive therapeutic method that modulates cortical excitability and shows promising results for treating disorders of consciousness (DoCs). Robotic verticalization training (RVT) has been shown to enhance motor and cognitive recovery. This study evaluates the effects of an innovative approach combining RVT with tDCS in individuals with DoCs.

METHODS

Twenty-four subjects with DoCs, particularly those with chronic minimally conscious state (MCS) due to vascular or traumatic brain injury, participated in a quasi-randomized study at the Neurorehabilitation Unit, IRCCS Neurolesi (Messina, Italy). Participants were divided into either a control group (CG) receiving RVT alone or an experimental group (EG) receiving combined tDCS and RVT. Both groups underwent treatments five times weekly for four weeks, with tDCS/sham sessions over the dorsolateral prefrontal cortex (DLPFC) lasting 20 min before Erigo training sessions, which lasted 45 min.

RESULTS

The findings indicate that combining tDCS with Erigo® Pro RTT could lead to greater improvements in cognitive functioning and P300 latency compared to the CG.

CONCLUSIONS

These results suggest that the integrated approach of tDCS with RVT could offer significant benefits for patients with MCS, highlighting its potential to enhance cognitive recovery, such as reducing P300 latency.

Psychedelics and disorders of consciousness: the current landscape and the path forward

Modern medicine has been shaken by the surge of psychedelic science that proposes a new approach to mitigate mental disorders, such as depression and post-traumatic stress disorder. Clinical trials to investigate whether psychedelic substances can treat psychiatric conditions are now underway, yet less discussion gravitates around their use in neurological disorders due to brain injury. One suggested implementation of brain-complexity enhancing psychedelics is to treat people with post-comatose disorders of consciousness (DoC). In this article, we discuss the rationale of this endeavour, examining possible outcomes of such experiments by postulating the existence of an optimal level of complexity. We consider the possible counterintuitive effects of both psychedelics and DoC on the functional connectivity of the default mode network and its possible impact on selfhood. We also elaborate on the role of computational modelling in providing complementary information to experimental studies, both contributing to our understanding of the treatment mechanisms and providing a path towards personalized medicine. Finally, we update the discourse surrounding the ethical considerations, encompassing clinical and scientific values.

An Update on Curing Coma Campaign

The Curing Coma Campaign (CCC) and its contributing collaborators identified multiple key areas of knowledge and research gaps in coma and disorders of consciousness (DoC). This step was a crucial effort and essential to prioritize future educational and research efforts. These key areas include defining categories of DoC, assessing DoC using multimodal approach (e.g., behavioral assessment tools, advanced neuroimaging studies), discussing optimal clinical trials' design and exploring computational models to conduct clinical trials in patients with DoC, and establishing common data elements to standardize data collection. Other key areas focused on creating coma care registry and educating clinicians and patients and promoting awareness of DoC to improve care in patients with DoC. The ongoing efforts in these key areas are discussed.

Low frequency oscillations reflect neurovascular coupling and disappear after cerebral death

Spectrum power analysis in the low frequency oscillations (LFO) region of functional near infrared spectroscopy (fNIRS) is a promising method to deliver information about brain activation and therefore might be used for prognostication in patients with disorders of consciousness in the neurocritical care unit alongside with established methods. In this study, we measure the cortical hemodynamic response measured by fNIRS in the LFO region following auditory and somatosensory stimulation in healthy subjects. The significant hemodynamic reaction in the contralateral hemisphere correlation with the physiologic electric response suggests neurovascular coupling. In addition, we investigate power spectrum changes in steady state measurements of cerebral death patients and healthy subjects in the LFO region, the frequency of the heartbeat and respiration. The spectral power within the LFO region was lower in the patients with cerebral death compared to the healthy subjects, whereas there were no differences in spectral power for physiological activities such as heartbeat and respiration rate. This finding indicates the cerebral origin of our low frequency measurements. Therefore, LFO measurements are a potential method to detect brain activation in patients with disorders of consciousness and cerebral death. However, further studies in patients are needed to investigate its potential clinical use.

Cell consciousness: a dissenting opinion : The cellular basis of consciousness theory lacks empirical evidence for its claims that all cells have consciousness

The proponents of CBC claim that all living organisms down to prokaryotes have consciousness. However, their arguments lack empirical evidence or are refuted by established facts.

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

Untangling a taxonomy of living from the science of the continuum of life

Medical innovation and technologic advances enrich daily living and occur within our normative worlds, that are socially constructed. These advances confront society with critical questions about the nature of human life, laying bare the inadequacies of extant norms and boundaries. Yet, society has been unable to develop consensus about when life ends. Scientific studies highlight that life is best characterized by continua without natural boundaries. Thus, scientific information alone cannot be employed to justify the socially constructed health categories required for setting norms and boundaries. An iterative process that integrates a broad range of non-scientific data with advancing scientific information is needed to facilitate consensus for updating social norms and boundaries. This can lead to a new taxonomy of living across the measurable continuum of life and align our normative worlds with the dizzying pace of medical innovation and advances in technologies transforming the world in which we live.