Early detection of consciousness in patients with acute severe traumatic brain injury

Developing an Electroencephalogram-based Model to Predict Awakening after Cardiac Arrest Using Partial Processing with the BIS Engine

BACKGROUND

Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. The authors sought to determine whether internal electroencephalogram (EEG) subparameters extracted by the BIS monitor (Medtronic, USA), a device commonly used to estimate depth of anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest.

METHODS

In this retrospective cohort study, a three-layer neural network was trained to predict recovery of consciousness to the point of command following versus not based on 48 h of continuous EEG recordings in 315 comatose patients admitted to a single U.S. academic medical center after cardiac arrest (derivation cohort, n = 181; validation cohort, n = 134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine ( i.e. , the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. The model was trained on hourly averaged measurements of these internal subparameters. This model's performance was compared to the modified Westhall qualitative EEG scoring framework.

RESULTS

Maximum prognostic accuracy in the derivation cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, the model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristics curve, 0.86; accuracy, 0.87; sensitivity, 0.83; specificity, 0.88; positive predictive value, 0.71; negative predictive value, 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest.

CONCLUSIONS

In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative accepted standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.

Caring for Coma after Severe Brain Injury: Clinical Practices and Challenges to Improve Outcomes: An Initiative by the Curing Coma Campaign

Severe brain injury can result in disorders of consciousness (DoC), including coma, vegetative state/unresponsive wakefulness syndrome, and minimally conscious state. Improved emergency and trauma medicine response, in addition to expanding efforts to prevent premature withdrawal of life-sustaining treatment, has led to an increased number of patients with prolonged DoC. High-quality bedside care of patients with DoC is key to improving long-term functional outcomes. However, there is a paucity of DoC-specific evidence guiding clinicians on efficacious bedside care that can promote medical stability and recovery of consciousness. This Viewpoint describes the state of current DoC bedside care and identifies knowledge and practice gaps related to patient care with DoC collated by the Care of the Patient in Coma scientific workgroup as part of the Neurocritical Care Society's Curing Coma Campaign. The gap analysis identified and organized domains of bedside care that could affect patient outcomes: clinical expertise, assessment and monitoring, timing of intervention, technology, family engagement, cultural considerations, systems of care, and transition to the post-acute continuum. Finally, this Viewpoint recommends future research and education initiatives to address and improve the care of patients with DoC.

The Pupillary Light-Off Reflex in Acute Disorders of Consciousness

BACKGROUND

In intensive care patients with disorders of consciousness, the pupillary light reflex is a measure of pupillary parasympathetic function. By contrast, the pupillary light-off reflex leads to pupil dilation in response to an abrupt change from light to darkness ("light-off") and reflects combined parasympathetic and sympathetic pupillary function. To our knowledge, this reflex has not been systematically investigated in patients with disorders of consciousness. We hypothesized that the pupillary light-off reflex correlates with consciousness levels after acute brain injury.

METHODS

From November 2022 to March 2023, we enrolled 100 study participants: 25 clinically unresponsive (coma or unresponsive wakefulness syndrome) and 25 clinically low-responsive (minimally conscious state or better) patients from the intensive care units of a tertiary referral center, and 50 age-matched and sex-matched healthy controls. Exclusion criteria were active or chronic eye disease. We used automated pupillometry to assess the pupillary light-off reflex and the pupillary light reflex of both eyes under scotopic conditions in all study participants.

RESULTS

The pupillary light-off reflex was strongly correlated with consciousness levels (r = 0.62, p < 0.001), the increase in pupillary diameters being smallest in unresponsive patients (mean ± standard deviation 20% ± 21%), followed by low-responsive patients (mean ± standard deviation 47% ± 26%) and healthy controls (mean ± standard deviation 67% ± 17%; p < 0.001). Similar yet less pronounced patterns were observed for the pupillary light reflex. Twenty-one of 25 (84%) unresponsive patients had preserved pupillary light reflexes, but only seven (28%) had fully preserved pupillary light-off reflexes (p < 0.0001). Of these 7 patients, five (71%) regained awareness.

CONCLUSIONS

The pupillary light-off reflex may be more sensitive to consciousness levels than the pupillary light reflex. The clinical implications of this finding seem worthy of further investigation, particularly regarding possible benefits for neuromonitoring and prognostication after brain injury.

Detecting cognitive motor dissociation by functional near-infrared spectroscopy

Background

Behavioral assessment based on external manifestations of consciousness fails for patients with cognitive motor dissociation (CMD). Functional near-infrared spectroscopy (fNIRS) is an emerging neuroimaging technique that can detect internal brain functional activities. However, the extent to which fNIRS can help identify CMD patients among those with disorders of consciousness (DOC) remains unclear.

Objective

To identify CMD patients among DOC patients using fNIRS with a command-driven hand-open-close motor imagery task.

Methods

fNIRS was used to measure the hemodynamic responses of 70 prolonged DOC patients, including 30 with vegetative state/unresponsive wakefulness syndrome (VS/UWS), 20 with minimally conscious state minus (MCS-), and 20 with minimally conscious state plus (MCS+), during a command-driven hand-open-close motor imagery task. Seven features of hemodynamic responses were extracted during the task and the rest conditions. The support vector machine combined with genetic algorithm was employed to classify and predict the brain's response to spoken commands and to identify CMD patients among prolonged DOC individuals.

Results

We identified seven CMD patients using fNIRS, of whom four were in VS/UWS and three were in MCS-. Six months after fNIRS examination, the seven identified CMD patients were more likely to have a favorable outcome (3/4 vs. 1/31, P = 0.014, Fisher's exact test) compared with non-CMD patients.

Conclusions

CMD patients can be identified through fNIRS combined with a command-driven motor imagery task, which will aid in the accurate diagnosis of DOC patients.

Impact of Aphasia on Brain Activation to Motor Commands in Patients with Acute Intracerebral Hemorrhage

BACKGROUND

Brain activation to motor commands is seen in 15% of clinically unresponsive patients with acute brain injury. This state called cognitive motor dissociation (CMD) is detectable by electroencephalogram (EEG) or functional magnetic resonance imaging, predicts long-term recovery, and is recommended by recent guidelines to support prognostication. However, false negative CMD results are a particular concern, and occult aphasia in clinically unresponsive patients may be a major factor. This study aimed to quantify the impact of aphasia on CMD testing.

METHODS

We prospectively studied 61 intensive care unit patients admitted with acute primary intracerebral hemorrhage (ICH) who had behavioral evidence of command following or were able to mimic motor commands. All patients underwent an EEG-based motor command paradigm used to detect CMD and comprehensive aphasia assessments. Logistic regression was used to identify predictors of brain activation, including aphasia types and associations with recovery of independence (Glasgow Outcome Scale-Extended score ≥ 4).

RESULTS

Of 61 patients, 50 completed aphasia and the EEG-based motor command paradigm. A total of 72% (n = 36) were diagnosed with aphasia. Patients with impaired comprehension (i.e., receptive or global aphasia) were less likely to show brain activation than those with intact comprehension (odds ratio [OR] 0.23 [95% confidence interval 0.05-0.89], p = 0.04). Brain activation was independently associated with Glasgow Outcome Scale-Extended ≥ 4 by 12 months (OR 2.4 [95% confidence interval 1.2-5.0], p = 0.01) accounting for the Functional Outcome in Patients with Primary ICH score (OR1.3 [95% confidence interval 1.0-1.8], p = 0.01).

CONCLUSIONS

Brain activation to motor commands is four times less likely for patients with primary ICH with impaired comprehension. False negative results due to occult receptive aphasia need to be considered when interpreting CMD testing. Early detection of brain activation may help predict long-term recovery in conscious patients with ICH.

Acute biomarkers of consciousness are associated with recovery after severe traumatic brain injury

Objective

Determine whether acute behavioral, electroencephalography (EEG), and functional MRI (fMRI) biomarkers of consciousness are associated with outcome after severe traumatic brain injury (TBI).

Methods

Patients with acute severe TBI admitted consecutively to the intensive care unit (ICU) participated in a multimodal battery assessing behavioral level of consciousness (Coma Recovery Scale-Revised [CRS-R]), cognitive motor dissociation (CMD; task-based EEG and fMRI), covert cortical processing (CCP; stimulus-based EEG and fMRI), and default mode network connectivity (DMN; resting-state fMRI). The primary outcome was 6-month Disability Rating Scale (DRS) total scores.

Results

We enrolled 55 patients with acute severe TBI. Six-month outcome was available in 45 (45.2±20.7 years old, 70% male), of whom 10 died, all due to withdrawal of life-sustaining treatment (WLST). Behavioral level of consciousness and presence of command-following in the ICU were each associated with lower (i.e., better) DRS scores (p=0.003, p=0.011). EEG and fMRI biomarkers did not strengthen this relationship, but higher DMN connectivity was associated with better recovery on multiple secondary outcome measures. In a subsample of participants without command-following on the CRS-R, CMD (EEG:18%; fMRI:33%) and CCP (EEG:91%; fMRI:79%) were not associated with outcome, an unexpected result that may reflect the high rate of WLST. However, higher DMN connectivity was associated with lower DRS scores (ρ[95%CI]=-0.41[-0.707, -0.027]; p=0.046) in this group.

Interpretation

Standardized behavioral assessment in the ICU may improve prediction of recovery from severe TBI. Further research is required to determine whether integrating behavioral, EEG, and fMRI biomarkers of consciousness is more predictive than behavioral assessment alone.

Sleep spindles as a predictor of cognitive motor dissociation and recovery of consciousness after acute brain injury

Cognitive motor dissociation (CMD) can improve the accuracy to predict recovery of behaviorally unresponsive patients with acute brain injury, but acquisition and analysis of task-based electroencephalography (EEG) are technically challenging. N2 sleep patterns, such as sleep spindles on EEG, have been associated with good outcomes, rely on similar thalamocortical networks as consciousness and could provide less technically challenging complementary outcome predictors. In this prospective observational cohort study of 226 acutely brain injured patients, well-formed sleep spindles (WFSS) were more likely present in those with CMD than in those without CMD, often preceding the detection of CMD. WFSS were associated with a shorter time to recovery of consciousness, and both CMD and WFSS independently predicted recovery of independence, controlling for age, admission neurological status and injury type. WFSS are seen in approximately every third behaviorally unresponsive patient after acute brain injury, frequently precede detection of CMD and are a promising complementary predictor for recovery of consciousness and functional independence.

Palliative General Anesthesia at Terminal Extubation: "Go Gentle into that Good Night"

Withdrawal of life-sustaining treatments in the intensive care unit most often culminates into the discontinuation of mechanical ventilation and removal of the endotracheal tube or "terminal extubation." Standards of practice call for the appropriate use of analgesia and sedation before, during and after extubation with an explicit goal to relieve suffering but not to hasten death. Patients subjected to this procedure are exposed to variable pharmacologic agents, modes, and doses, without any knowledge or monitoring of what these patients are experiencing. This practice seems to rest on contestable assumptions regarding the reliability of bedside examination, the experiential states of unresponsive patients, and the scope of the doctrine of double effect; instead, I argue for palliative general anesthesia in order to safeguard against potential suffering at the end of life. I employ philosophical notions of harm to justify the normative status of palliative anesthesia, in conjunction with contemporary evidence as it relates to the phenomena of covert consciousness and cognitive-motor dissociation. If this analysis is correct, then it may serve as a valid challenge toward current practice without having engaged into controversial debates over the soundness of the doctrine of double effect, or euthanasia. Primun non nocere offers the strongest justification for general anesthesia when terminal extubation is planned.

Reconstructing Covert Consciousness: Neural Decoding as a Novel Consciousness Assessment

Determining the level of consciousness in patients with brain injury-and more fundamentally, establishing what they can experience-is ethically and clinically impactful. Patient behaviors may unreliably reflect their level of consciousness: a subset of unresponsive patients demonstrate covert consciousness by willfully modulating their brain activity to commands through fMRI or EEG. However, current paradigms for assessing covert consciousness remain fundamentally limited because they are insensitive, rely on imperfect assumptions of functional neuroanatomy, and do not reflect the spectrum of conscious experience. Neural decoding, in which stimuli and concepts are reconstructed from brain activity, offers a novel approach to covert consciousness assessment that overcomes many of these limitations. In this article, we discuss the current state of covert consciousness assessments, their shortcomings, the state of the science in neural decoding, the potential application of neural decoding to disorders of consciousness, and future directions that may help realize this potential. To do so, we searched PubMed and Google Scholar databases for pertinent articles published between January 1990 and September 2024, using the search terms "covert consciousness," "cognitive motor dissociation," "neural decoding," and "semantic decoding." Redefining covert consciousness with neural decoding may improve sensitivity, enhance granularity, and more directly address the question of what patients can experience after brain injury.

Incoherence in the Brain Death Guideline Regarding Brain Blood Flow Testing: Lessons from the Much-Publicized Case of Zack Dunlap

At age 21, following a severe traumatic brain injury, Zack Dunlap was declared brain-dead according to the American Academy of Neurology guideline (Guideline) when he met the clinical criteria of brain death (minus apnea testing because of bradycardia) with technetium-99m diethylene-triamine-pentaacetate scintigraphy reported as showing no intracranial blood flow. His parents agreed to organ donation. During preparations for organ donation, Zack manifested a purposeful movement in response to a noxious stimulus made by his cousin. Following subsequent neurological recovery, he has returned to a normal life, holding steady employment and raising a family. During an interview, he reported that while in coma, he heard a doctor say that he was brain-dead and felt angry about it. His experience fits the phenomenon of cognitive-motor dissociation. Recently, Zack's medical records were made available to the first author. A critical review of the records uncovered a problem inherent in the logic of the Guideline algorithm regarding brain blood flow scintigraphy. This article discusses the lessons drawn from Zack's case, namely, that both the aforementioned problem and the occurrence of cognitive-motor dissociation in patients deemed to be brain-dead can pose a significant risk of a false-positive declaration of death.

Parallel EEG-fNIRS assessments of covert cognition in behaviorally non-responsive ICU patients: A multi-task feasibility study in a case of acute motor sensory axonal neuropathy

BACKGROUND

Repeat neurological assessment is standard in cases of severe acute brain injury. However, conventional measures rely on overt behavior. Unfortunately, behavioral responses may be difficult or impossible for some patients. As a result, patients who recover consciousness before the ability to express so may go undetected. Recent studies have demonstrated the efficacy of incorporating functional neuroimaging into clinical assessment protocols. The objective of the current study is to assess the feasibility of a multi-task, multimodal bedside technique to evaluate sensory and cognitive function in behaviorally non-responsive patients.

METHODS

We deployed a novel assessment paradigm to evaluate sensory and cognitive processing in one 63-year-old unresponsive patient with acute motor sensory axonal neuropathy (AMSAN). We collected parallel bedside EEG-fNIRS activity during hierarchical auditory processing, movie listening, and motor imagery.

RESULTS

We found appropriate hemodynamic activation in the patient's middle and superior temporal gyri to simple sounds and activation in their superior temporal gyrus, left angular and precentral gyri during speech. During movie listening, the patient produced patterns of EEG and fNIRS activity that were statistically indistinguishable from healthy controls. The patient also showed appropriate fNIRS and source-localized EEG activation of motor areas during motor imagery. Upon recovering, the patient correctly recalled multiple aspects of our assessment procedures.

CONCLUSION

In sum, our assessment protocol effectively captures neural markers of sensory and cognitive function in behaviorally non-responsive patients. Crucially, while AMSAN is distinct from brain injury, the patient's assumed dissociation between behavior and awareness provided an ideal test case to validate our protocol.

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotation by expert neurophysiologists is a major resource limitation in investigating the prognostic and therapeutic implications of these EEG patterns and in expanding EEG use to a broader set of patients who are likely to benefit. Artificial intelligence (AI) has already demonstrated clinical success in guiding cEEG allocation for patients at risk for seizures, and its potential uses in neurocritical care are expanding alongside improvements in AI itself. We review both current clinical uses of AI for EEG-guided management as well as ongoing research directions in automated seizure and ischemia detection, neurologic prognostication, and guidance of medical and surgical treatment.

Advances in neuroimaging in disorders of consciousness

Disorders of consciousness (DoC) are a heterogeneous spectrum of clinical conditions, including coma, unresponsive wakefulness syndrome, and minimally conscious state. DoC are clinically defined on the basis of behavioral cues expressed by the patients, on the assumption that such behavioral responses of the patient are representative of the patient's degree of consciousness impairment. However, many studies have highlighted the issues arising from formulating a DoC diagnosis merely on behavioral assessment. Overcoming the limitations of behavioral assessment, neuroimaging provides a direct window on the cerebral activity of the patient, bypassing the motor, perceptual, or cognitive deficits that might hamper the patient's ability to produce an appropriate behavioral response. This chapter provides an overview of available molecular, functional, and structural neuroimaging evidence in patients with DoC. This chapter introduces the neuroimaging tools available in the clinical settings of nuclear medicine and neuroradiology and presents the evidence on the role of neuroimaging tools to improve the clinical management of DoC patients, from the standpoint of differential diagnosis and prognosis. Last, we outline the open questions in the field, and point at actions that are urgently needed to fully exploit neuroimaging tools to advance scientific understanding and clinical management of DoC.

Medical and surgical treatments in disorders of consciousness

Ever since a 2012 landmark study showed positive effects of amantadine in people with disorders of consciousness (DOC), there has been a shift in research efforts from merely improving diagnostics and prognostication of DOC to also include therapeutic trials, in the quest to improve consciousness recovery after brain injury. Stimulation of residual consciousness in the intensive care unit is critical because failure to do so may lead to unwarranted pessimistic prognosis and premature withdrawal of life-sustaining therapies. Similarly, it is crucial to harvest the potential of chronic DOC patients for late consciousness recovery, which is increasingly reported. To this end, medical and nonpharmacologic, including surgical, treatment strategies are being tested. These include dopaminergic and GABAergic drugs (medical), vagal nerve stimulation (noninvasive or surgical), and deep brain stimulation (surgical). In addition, transcranial magnetic stimulation, transcranial direct current stimulation, and low-intensity ultrasound (nonpharmacologic and nonsurgical) are covered in another chapter in this volume of the Handbook. Although overall, DOC treatment studies are subject to small sample sizes, unblinded protocols, and limited follow-up, this will likely change in the foreseeable future with the advent of adequately powered multicenter studies, randomized, double-blind, placebo-controlled designs, and standardized outcome measures. This chapter discusses the present state and outlooks of the field of medical and surgical options to boost arousal and awareness in patients with DOC, indicating the future of DOC treatment is bright.

Current Challenges in Neurocritical Care: A Narrative Review

Neurocritical care as a field aims to treat patients who are neurologically critically ill due to a variety of pathologies. As a recently developed subspecialty, the field faces challenges, several of which are outlined in this review. The authors discuss aneurysmal subarachnoid hemorrhage, status epilepticus, and traumatic brain injury as specific disease processes with opportunities for growth in diagnosis, management, and treatment, as well as disorders of consciousness that can arise as a result of many neurological injuries. They also address logistical challenges, such as the need for specialized resources needed to successfully run a neurosciences intensive care unit (neuro-ICU), the variations in training of those who staff neuro-ICUs, and different interdisciplinary team structures. Although an immense amount of data is collected in the neuro-ICU, leveraging the data for clinical research is an area with room for further innovation. Additionally, developing accurate basic science models for these disease processes is an ongoing area of exploration. Finally, the authors explore psychosocial challenges present in the care of neurologically critically ill patients, including limitations in prognostication and religious and cultural perceptions of brain death.

A scientific approach to diagnosis of disorders of consciousness

Disorder of consciousness (DoC) are the shared clinical manifestation of severe brain injuries resulting from a variety of etiologies. The nosology of DoC, as well as the armamentarium of methods available to diagnose it, has rapidly evolved. As a result, the diagnosis of DoC is complex and dynamic. We offer an evidence-based approach to DoC diagnosis, highlighting the challenges and pitfalls therein. Accordingly, we summarize the contemporary taxonomy of DoC and its development. We discuss the standardized behavioral diagnostic tools that form the foundation of DoC diagnosis, the evidence for their use, and their limitations. We also highlight recent advances in functional MRI (fMRI) and electroencephalography (EEG) techniques to increase the sensitivity and specificity of DoC diagnosis. We discuss the concept of covert consciousness (i.e., cognitive motor dissociation) as a discrete diagnostic category of DoC, as well as its diagnostic implications. Finally, we underscore issues of neuroethics and equity raised by contemporary models of DoC.

Electrophysiology in disorders of consciousness

Electroencephalography (EEG) has emerged as a powerful tool in the diagnosis, characterization, and prognostication of patients with disorders of consciousness (DoC). EEG is a well-established monitoring tool for the treatment of specific patient populations with impaired consciousness, such as those with status epilepticus and cardiac arrest. The interrogation of neuronal circuitry using evoked and event-related potentials adds prognostic information in comatose individuals. Novel paradigms integrating transcranial magnetic stimulation may provide insights into the underpinnings of arousal and awareness. Covert consciousness, or willful brain activation to motor commands in behaviorally unresponsive patients, may be diagnosed using EEG recordings and has been linked to better outcomes. These advanced EEG methods are increasingly being explored and integrated into the management of DoC patients.

Neuroscience of coma

Coma and disorders of consciousness are frequently considered in terms of two linked anatomic-functional systems: the arousal system and the awareness system. The mesopontine tegmentum (namely the cuneiform/subcuneiform nuclei of the caudal midbrain and the pontis oralis nucleus of the rostral pons) and the monoamine nuclei generate signals of arousal. These signals are augmented in lateral hypothalamus and basal forebrain, which then project to the thalamus and diffusely across the cortex. The medial dorsal tegmental tract is the main conduit for the ascending arousal system to directly activate the thalamic intralaminar nuclei and modulate thalamocortical networks, while the lateral dorsal tegmental tract connects to the thalamic reticular nucleus for regulation of intrathalamic inhibitory networks. The central thalamus (particularly the intralaminar nuclei) and the mesocircuit regulate the arousal system. Lesions to any part of this system, particularly paramedian and bilateral lesions, result in a depressed level of arousal. Distinct from the arousal pathways, the awareness system runs continuously as a stream of consciousness. It consists of large-scale distributed cortical networks that are necessary for representations of the external (executive control network with the dorsal/ventral attention networks) and the internal world (executive control network in conjunction with the default network). A feature of the awareness system is that it does not capture external and internal worlds at once and instead, holds singular representations, serially moment-by-moment. The medial dorsal nucleus of the thalamus serves as the associative nuclei of the default network, and the thalamic reticular nucleus regulates the awareness system. Lesions that disrupt large-scale networks, particularly nodes of cortical hubs, result in lack of awareness. Integrative paradigms such as the integrated information theory and the global neuronal workspace models are attempts to bind awareness and arousal into a unified experience of consciousness.

The Curing Coma Campaign: A platform for advancing science and clinical care worldwide

Recovery from coma or impaired consciousness is often the central issue in acute neurologic conditions such as traumatic brain injury, hypoxic-ischemic brain injury, stroke, and central nervous system infections. Recent advances in the science underpinning acute disorders of consciousness (DoC) have also served to highlight further scientific gaps and the lack of a coordinated approach to improving care for these patients. The Curing Coma Campaign (CCC) was initiated by the Neurocritical Care Society in 2019 as a platform to bring together the scientific, clinical, and public communities to cohesively address this issue. Comprised of various modules and working groups focused on aspects including fundamental science, prospective clinical studies, ethics, care of the coma patient, and engagement and community, initial efforts of the CCC have ranged from developing strategies for biomarker development to creating World Coma Day as an opportunity for widespread interaction. To achieve the goal of relevance across different geographic and resourced environments, the CCC implemented specific considerations to ensure equity and generalizability. These include international representation of patients in research studies, attention to assessments and interventions that can be implemented in resource-limited settings, and recognition of the impact of culture on the care of DoC patients.

Altered Mental Status at the Extreme: Behavioral Evaluation of Disorders of Consciousness

Disorders of consciousness represent altered mental status at its most severe, comprising a continuum between coma, the vegetative state/unresponsive wakefulness syndrome, the minimally conscious state, and emergence from the minimally conscious state. Patients often transition between these levels throughout their recovery, and determining a patient's current level can be challenging, particularly in the acute care setting. Although healthcare providers have classically relied on a bedside neurological exam or the Glasgow Coma Scale to aid with assessment of consciousness, studies have identified multiple limitations of doing so. Neurobehavioral assessment measures, such as the Coma Recovery Scale-Revised, have been developed to address these shortcomings. Each behavioral metric has strengths as well as weaknesses when applied in the acute care setting. In this review, we appraise common assessment approaches, outline alternative measures for fine-tuning these assessments in the acute care setting, and highlight strategies for implementing these practices in an interdisciplinary manner.

Care of the Patient Nearing the End of Life in the Neurointensive Care Unit

BACKGROUND

Neurologically critically ill patients present with unique disease trajectories, prognostic uncertainties, and challenges to end-of-life (EOL) care. Acute brain injuries place these patients at risk for underrecognized symptoms and unmet EOL management needs, which can negatively affect their quality of care and lead to complicated grief in surviving loved ones. To care for patients nearing the EOL in the neurointensive care unit, health care clinicians must consider neuroanatomic localization, barriers to symptom assessment and management, unique aspects of the dying process, and EOL management needs.

AIM

We aim to define current best practices, barriers, and future directions for EOL care of the neurologically critically ill patient.

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.

Music-Based Interventions for Symptom Management in Critically Ill, Mechanically Ventilated Adults: A Scoping Review of the Literature

Background: Patients in intensive care units experience high symptom burden during mechanical ventilation (MV). Pharmacologic symptom management is associated with side effects and increased morbidity. Music-based interventions (MBIs) have been associated with reductions in both anxiety in MV adults and pain for critically ill adults, yet their use for the management of other burdensome symptoms has not been evaluated. The purpose of this scoping review is to map the state of evidence for the use of prerecorded music listening MBIs for symptom management in MV adults. Methods: A systematic search of the literature was conducted across four electronic databases (PubMed, EMBASE, CINAHL, and Web of Science) for experimental designed studies that measured the efficacy of MBIs for the management of physical and psychological symptoms including anxiety, sedation/agitation, dyspnea, distress, delirium, sleep, stress, fear, loneliness, or depression in critically ill, MV adults between January 1, 1998, and April 18, 2023. Results: A total of 643 abstracts and 29 clinical trials were included. Overall, the risk of bias, assessed using the Evidence Project tool, was moderate. MBIs were mostly delivered with headphones using music selected either by investigators or from a limited selection. MBIs were associated with reduced pain, agitation, dyspnea, distress and anxiety, and improved tolerance of MV and sedative weaning. Outcomes of delirium were mixed. No studies explored sleep disturbances, fear, or loneliness. Conclusions: Use of MBIs improved symptom experience for critically ill adults during MV. Future studies employing unrestricted patient-preferred music selections and exploring outcomes of sleep quality, psychological distress, and delirium are needed in this highly symptomatic patient population.

Assessing consciousness in acute coma using name-evoked responses

Detecting consciousness in clinically unresponsive patients remains a significant challenge. Existing studies demonstrate that electroencephalography (EEG) can detect brain responses in behaviorally unresponsive patients, indicating potential for consciousness detection. However, most of this evidence is based on chronic patients, and there is a lack of studies focusing on acute coma cases. This study aims to detect signs of residual consciousness in patients with acute coma by using bedside EEG and electromyography (EMG) during an auditory oddball paradigm. We recruited patients with acute brain injury (either traumatic brain injury or cardiac arrest) who were admitted to the intensive care unit within two weeks after injury, with a Glasgow Coma Scale (GCS) score of 8 or below. Auditory stimuli included the patients' own names and other common names (referred to as standard names), spoken by the patients' relatives, delivered under two conditions: passive listening (where patients were instructed that sounds would be played) and active listening (where patients were asked to move hands when heard their own names). Brain and muscle activity were recorded using EEG and EMG during the auditory paradigm. Event-related potentials (ERP) and EMG spectra were analyzed and compared between responses to the subject's own name and other standard names in both passive and active listening conditions. A total of 22 patients were included in the final analysis. Subjects exhibited enhanced ERP responses when exposed to their own names, particularly during the active listening task. Compared to standard names or passive listening, distinct differences in brain network connectivity and increased EMG responses were detected during active listening to their own names. These findings suggest the presence of residual consciousness, offering the potential for assessing consciousness in behaviorally unresponsive patients.

The localization of coma

Coma and disorders of consciousness (DoC) are common manifestations of acute severe brain injuries. Research into their neuroanatomical basis can be traced from Hippocrates to the present day. Lesions causing DoC have traditionally been conceptualized as decreasing "alertness" from damage to the ascending arousal system, and/or, reducing level of "awareness" due to structural or functional impairment of large-scale brain networks. Within this framework, pharmacological and neuromodulatory interventions to promote recovery from DoC have hitherto met with limited success. This is partly due to inter-individual heterogeneity of brain injury patterns, and an incomplete understanding of brain network properties that characterize consciousness. Advances in multiscale computational modelling of brain dynamics have opened a unique opportunity to explore the causal mechanisms of brain activity at the biophysical level. These models can provide a novel approach for selection and optimization of potential interventions by simulation of brain network dynamics individualized for each patient.

Task-based EEG and fMRI paradigms in a multimodal clinical diagnostic framework for disorders of consciousness

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).

A Survey of Surrogates and Health Care Professionals Indicates Support of Cognitive Motor Dissociation-Assisted Prognostication

BACKGROUND

Prognostication of patients with acute disorders of consciousness is imprecise but more accurate technology-supported predictions, such as cognitive motor dissociation (CMD), are emerging. CMD refers to the detection of willful brain activation following motor commands using functional magnetic resonance imaging or machine learning-supported analysis of the electroencephalogram in clinically unresponsive patients. CMD is associated with long-term recovery, but acceptance by surrogates and health care professionals is uncertain. The objective of this study was to determine receptiveness for CMD to inform goals of care (GoC) decisions and research participation among health care professionals and surrogates of behaviorally unresponsive patients.

METHODS

This was a two-center study of surrogates of and health care professionals caring for unconscious patients with severe neurological injury who were enrolled in two prospective US-based studies. Participants completed a 13-item survey to assess demographics, religiosity, minimal acceptable level of recovery, enthusiasm for research participation, and receptiveness for CMD to support GoC decisions.

RESULTS

Completed surveys were obtained from 196 participants (133 health care professionals and 63 surrogates). Across all respondents, 93% indicated that they would want their loved one or the patient they cared for to participate in a research study that supports recovery of consciousness if CMD were detected, compared to 58% if CMD were not detected. Health care professionals were more likely than surrogates to change GoC with a positive (78% vs. 59%, p = 0.005) or negative (83% vs. 59%, p = 0.0002) CMD result. Participants who reported religion was the most important part of their life were least likely to change GoC with or without CMD. Participants who identified as Black (odds ratio [OR] 0.12, 95% confidence interval [CI] 0.04-0.36) or Hispanic/Latino (OR 0.39, 95% CI 0.2-0.75) and those for whom religion was the most important part of their life (OR 0.18, 95% CI 0.05-0.64) were more likely to accept a lower minimum level of recovery.

CONCLUSIONS

Technology-supported prognostication and enthusiasm for clinical trial participation was supported across a diverse spectrum of health care professionals and surrogate decision-makers. Education for surrogates and health care professionals should accompany integration of technology-supported prognostication.

Histology-guided MRI segmentation of brainstem nuclei critical to consciousness

While substantial progress has been made in mapping the connectivity of cortical networks responsible for conscious awareness, neuroimaging analysis of subcortical arousal networks that modulate arousal (i.e., wakefulness) has been limited by a lack of a robust segmentation procedures for brainstem arousal nuclei. Automated segmentation of brainstem arousal nuclei is an essential step toward elucidating the physiology of arousal in human consciousness and the pathophysiology of disorders of consciousness. We created a probabilistic atlas of brainstem arousal nuclei built on diffusion MRI scans of five ex vivo human brain specimens scanned at 750 μm isotropic resolution. Labels of arousal nuclei used to generate the probabilistic atlas were manually annotated with reference to nucleus-specific immunostaining in two of the five brain specimens. We then developed a Bayesian segmentation algorithm that utilizes the probabilistic atlas as a generative model and automatically identifies brainstem arousal nuclei in a resolution- and contrast-agnostic manner. The segmentation method displayed high accuracy in both healthy and lesioned in vivo T1 MRI scans and high test-retest reliability across both T1 and T2 MRI contrasts. Finally, we show that the segmentation algorithm can detect volumetric changes and differences in magnetic susceptibility within brainstem arousal nuclei in Alzheimer's disease and traumatic coma, respectively. We release the probabilistic atlas and Bayesian segmentation tool in FreeSurfer to advance the study of human consciousness and its disorders.

Alpha coherence is a network signature of cognitive recovery from disorders of consciousness

Alpha (8-12 Hz) frequency band oscillations are among the most informative features in electroencephalographic (EEG) assessment of patients with disorders of consciousness (DoC). Because interareal alpha synchrony is thought to facilitate long-range communication in healthy brains, coherence measures of resting-state alpha oscillations may provide insights into a patient's capacity for higher-order cognition beyond channel-wise estimates of alpha power. In multi-channel EEG, global coherence methods may be used to augment standard spectral analysis methods by both estimating the strength and identifying the structure of coherent oscillatory networks. We performed global coherence analysis in 95 separate clinical EEG recordings (28 healthy controls and 33 patients with acute or chronic DoC, 25 of whom returned for follow-up) collected between two academic medical centers. We found that posterior alpha coherence is associated with recovery of higher-level cognition. We developed a measure of network organization, based on the distance between eigenvectors of the alpha cross-spectral matrix, that detects recovery of posterior alpha networks. In patients who have emerged from a minimally conscious state, we showed that coherence-based alpha networks are reconfigured prior to restoration of alpha power to resemble those seen in healthy controls. This alpha network measure performs well in classifying recovery from DoC (AUC = 0.78) compared to common representations of functional connectivity using the weighted phase lag index (AUC = 0.50 - 0.57). Lastly, we observed that activity within these alpha networks is suppressed during positive responses to task-based EEG command-following paradigms, supporting the potential utility of this biomarker to detect covert cognition. Our findings suggest that restored alpha networks may represent a sensitive early signature of cognitive recovery in patients with DoC. Therefore, network detection methods may augment the utility of EEG assessments for DoC.

Pre- and post-therapy functional MRI connectivity in severe acute brain injury with suppression of consciousness: a comparative analysis to epilepsy features

Severe acute brain injury (SABI) with suppressed consciousness is a major societal burden, with early prognosis being crucial for life-and-death treatment decisions. Resting-state functional MRI (rs-fMRI) is promising for prognosis and identifying epileptogenic activity in SABI. While established for SABI prognosis and seizure networks (SzNET) identification in epilepsy, the rs-fMRI use for SzNET detection in SABI is limited. This study compared evolution of SzNET and resting-state networks (RSN) pre-to-post treatment in SABI and epilepsy, hypothesizing that changes would align with clinical evolution. Therapies included epilepsy surgery for the epilepsy group and antiseizure medication for the SABI group. Independent component analysis (ICA) was used to identify SzNET and RSNs in all rs-fMRI. High-frequency BOLD (HF-BOLD), an ICA power spectrum-based index, quantified RSN and SzNET changes by the patient. Confidence intervals measured HF-BOLD changes pre-to-post-therapy. Baseline HF-BOLD and HF-BOLD changes were compared using linear-mixed models and interaction tests. Five SABI and ten epilepsy patients were included. SzNET were identified in all SABI's pre-therapy rs-fMRI. The clinical changes in SABI and epilepsy were consistent with rs-fMRI findings across groups. HF-BOLD reduced in the epilepsy group RSN post-therapy (-0.78, 95% CI -3.42 to -0.33), but the evidence was insufficient to determine an HF-BOLD reduction in SABI patients or SzNET. The HF-BOLD change trend in pre-to-post epilepsy surgery scans paralleled the clinical improvement, suggesting that the power spectrum may quantify the degree of abnormality on ICA-derived networks. Despite limitations such as small sample sizes, this exploratory study provides valuable insights into network dysfunction in SABI and epilepsy.

Recovery of consciousness after acute brain injury: a narrative review

BACKGROUND

Disorders of consciousness (DoC) are frequently encountered in both, acute and chronic brain injuries. In many countries, early withdrawal of life-sustaining treatments is common practice for these patients even though the accuracy of predicting recovery is debated and delayed recovery can be seen. In this review, we will discuss theoretical concepts of consciousness and pathophysiology, explore effective strategies for management, and discuss the accurate prediction of long-term clinical outcomes. We will also address research challenges.

MAIN TEXT

DoC are characterized by alterations in arousal and/or content, being classified as coma, unresponsive wakefulness syndrome/vegetative state, minimally conscious state, and confusional state. Patients with willful modulation of brain activity detectable by functional MRI or EEG but not by behavioral examination is a state also known as covert consciousness or cognitive motor dissociation. This state may be as common as every 4th or 5th patient without behavioral evidence of verbal command following and has been identified as an independent predictor of long-term functional recovery. Underlying mechanisms are uncertain but intact arousal and thalamocortical projections maybe be essential. Insights into the mechanisms underlying DoC will be of major importance as these will provide a framework to conceptualize treatment approaches, including medical, mechanical, or electoral brain stimulation.

CONCLUSIONS

We are beginning to gain insights into the underlying mechanisms of DoC, identifying novel advanced prognostication tools to improve the accuracy of recovery predictions, and are starting to conceptualize targeted treatments to support the recovery of DoC patients. It is essential to determine how these advancements can be implemented and benefit DoC patients across a range of clinical settings and global societal systems. The Curing Coma Campaign has highlighted major gaps knowledge and provides a roadmap to advance the field of coma science with the goal to support the recovery of patients with DoC.

Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury

Recent advancements in functional neuroimaging have demonstrated that some unresponsive patients in the intensive care unit retain a level of consciousness that is inconsistent with their behavioral diagnosis of awareness. Functional near-infrared spectroscopy (fNIRS) is a portable optical neuroimaging method that can be used to measure neural activity with good temporal and spatial resolution. However, the reliability of fNIRS for detecting the neural correlates of consciousness remains to be established. In a series of studies, we evaluated whether fNIRS can record sensory, perceptual, and command-driven neural processing in healthy participants and in behaviorally nonresponsive patients. At the individual healthy subject level, we demonstrate that fNIRS can detect commonly studied resting state networks, sensorimotor processing, speech-specific auditory processing, and volitional command-driven brain activity to a motor imagery task. We then tested fNIRS with three acutely brain injured patients and found that one could willfully modulate their brain activity when instructed to imagine playing a game of tennis-providing evidence of preserved consciousness despite no observable behavioral signs of awareness. The successful application of fNIRS for detecting preserved awareness among behaviorally nonresponsive patients highlights its potential as a valuable tool for uncovering hidden cognitive states in critical care settings.

Applications of Functional Magnetic Resonance Imaging to the Study of Functional Connectivity and Activation in Neurological Disease: A Scoping Review of the Literature

BACKGROUND

Functional magnetic resonance imaging (fMRI) has transformed our understanding of brain's functional architecture, providing critical insights into neurological diseases. This scoping review synthesizes the current landscape of fMRI applications across various neurological domains, elucidating the evolving role of both task-based and resting-state fMRI in different settings.

METHODS

We conducted a comprehensive scoping review following the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews guidelines. Extensive searches in Medline/PubMed, Embase, and Web of Science were performed, focusing on studies published between 2003 and 2023 that utilized fMRI to explore functional connectivity and regional activation in adult patients with neurological conditions. Studies were selected based on predefined inclusion and exclusion criteria, with data extracted.

RESULTS

We identified 211 studies, covering a broad spectrum of neurological disorders including mental health, movement disorders, epilepsy, neurodegeneration, traumatic brain injury, cerebrovascular accidents, vascular abnormalities, neurorehabilitation, neuro-critical care, and brain tumors. The majority of studies utilized resting-state fMRI, underscoring its prominence in identifying disease-specific connectivity patterns. Results highlight the potential of fMRI to reveal the underlying pathophysiological mechanisms of various neurological conditions, facilitate diagnostic processes, and potentially guide therapeutic interventions.

CONCLUSIONS

fMRI serves as a powerful tool for elucidating complex neural dynamics and pathologies associated with neurological diseases. Despite the breadth of applications, further research is required to standardize fMRI protocols, improve interpretative methodologies, and enhance the translation of imaging findings to clinical practice. Advances in fMRI technology and analytics hold promise for improving the precision of neurological assessments and interventions.

Explaining recovery from coma with multimodal neuroimaging

The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients' neurological recovery after coma. 32 patients (18-76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient's hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = - 0.74, p < 0.001, 95% CI: - 0.46, - 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.

Covert Consciousness in the ICU

OBJECTIVES

For critically ill patients with acute severe brain injuries, consciousness may reemerge before behavioral responsiveness. The phenomenon of covert consciousness (i.e., cognitive motor dissociation) may be detected by advanced neurotechnologies such as task-based functional MRI (fMRI) and electroencephalography (EEG) in patients who appear unresponsive on the bedside behavioral examination. In this narrative review, we summarize the state-of-the-science in ICU detection of covert consciousness. Further, we consider the prognostic and therapeutic implications of diagnosing covert consciousness in the ICU, as well as its potential to inform discussions about continuation of life-sustaining therapy for patients with severe brain injuries.

DATA SOURCES

We reviewed salient medical literature regarding covert consciousness.

STUDY SELECTION

We included clinical studies investigating the diagnostic performance characteristics and prognostic utility of advanced neurotechnologies such as task-based fMRI and EEG. We focus on clinical guidelines, professional society scientific statements, and neuroethical analyses pertaining to the implementation of advanced neurotechnologies in the ICU to detect covert consciousness.

DATA EXTRACTION AND DATA SYNTHESIS

We extracted study results, guideline recommendations, and society scientific statement recommendations regarding the diagnostic, prognostic, and therapeutic relevance of covert consciousness to the clinical care of ICU patients with severe brain injuries.

CONCLUSIONS

Emerging evidence indicates that covert consciousness is present in approximately 15-20% of ICU patients who appear unresponsive on behavioral examination. Covert consciousness may be detected in patients with traumatic and nontraumatic brain injuries, including patients whose behavioral examination suggests a comatose state. The presence of covert consciousness in the ICU may predict the pace and extent of long-term functional recovery. Professional society guidelines now recommend assessment of covert consciousness using task-based fMRI and EEG. However, the clinical criteria for patient selection for such investigations are uncertain and global access to advanced neurotechnologies is limited.

Cognitive Motor Dissociation in Disorders of Consciousness

BACKGROUND

Patients with brain injury who are unresponsive to commands may perform cognitive tasks that are detected on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). This phenomenon, known as cognitive motor dissociation, has not been systematically studied in a large cohort of persons with disorders of consciousness.

METHODS

In this prospective cohort study conducted at six international centers, we collected clinical, behavioral, and task-based fMRI and EEG data from a convenience sample of 353 adults with disorders of consciousness. We assessed the response to commands on task-based fMRI or EEG in participants without an observable response to verbal commands (i.e., those with a behavioral diagnosis of coma, vegetative state, or minimally conscious state-minus) and in participants with an observable response to verbal commands. The presence or absence of an observable response to commands was assessed with the use of the Coma Recovery Scale-Revised (CRS-R).

RESULTS

Data from fMRI only or EEG only were available for 65% of the participants, and data from both fMRI and EEG were available for 35%. The median age of the participants was 37.9 years, the median time between brain injury and assessment with the CRS-R was 7.9 months (25% of the participants were assessed with the CRS-R within 28 days after injury), and brain trauma was an etiologic factor in 50%. We detected cognitive motor dissociation in 60 of the 241 participants (25%) without an observable response to commands, of whom 11 had been assessed with the use of fMRI only, 13 with the use of EEG only, and 36 with the use of both techniques. Cognitive motor dissociation was associated with younger age, longer time since injury, and brain trauma as an etiologic factor. In contrast, responses on task-based fMRI or EEG occurred in 43 of 112 participants (38%) with an observable response to verbal commands.

CONCLUSIONS

Approximately one in four participants without an observable response to commands performed a cognitive task on fMRI or EEG as compared with one in three participants with an observable response to commands. (Funded by the James S. McDonnell Foundation and others.).

Detecting awareness after acute brain injury

Advances over the past two decades in functional neuroimaging have provided new diagnostic and prognostic tools for patients with severe brain injury. Some of the most pertinent developments in this area involve the assessment of residual brain function in patients in the intensive care unit during the acute phase of severe injury, when they are at their most vulnerable and prognosis is uncertain. Advanced neuroimaging techniques, such as functional MRI and EEG, have now been used to identify preserved cognitive processing, including covert conscious awareness, and to relate them to outcome in patients who are behaviourally unresponsive. Yet, technical and logistical challenges to clinical integration of these advanced neuroimaging techniques remain, such as the need for specialised expertise to acquire, analyse, and interpret data and to determine the appropriate timing for such assessments. Once these barriers are overcome, advanced functional neuroimaging technologies could improve diagnosis and prognosis for millions of patients worldwide.

Return of intracranial beta oscillations and traveling waves with recovery from traumatic brain injury

Traumatic brain injury (TBI) remains a pervasive clinical problem associated with significant morbidity and mortality. However, TBI remains clinically and biophysically ill-defined, and prognosis remains difficult even with the standardization of clinical guidelines and advent of multimodality monitoring. Here we leverage a unique data set from TBI patients implanted with either intracranial strip electrodes during craniotomy or quad-lumen intracranial bolts with depth electrodes as part of routine clinical practice. By extracting spectral profiles of this data, we found that the presence of narrow-band oscillatory activity in the beta band (12-30 Hz) closely corresponds with the neurological exam as quantified with the standard Glasgow Coma Scale (GCS). Further, beta oscillations were distributed over the cortical surface as traveling waves, and the evolution of these waves corresponded to recovery from coma, consistent with the putative role of waves in perception and cognitive activity. We consequently propose that beta oscillations and traveling waves are potential biomarkers of recovery from TBI. In a broader sense, our findings suggest that emergence from coma results from recovery of thalamo-cortical interactions that coordinate cortical beta rhythms.

Behavioral Assessment With the Coma Recovery Scale-Revised Is Safe and Feasible in Critically Ill Patients With Disorders of Consciousness

OBJECTIVES

Accurate classification of disorders of consciousness (DoC) is key in developing rehabilitation plans after brain injury. The Coma Recovery Scale-Revised (CRS-R) is a sensitive measure of consciousness validated in the rehabilitation phase of care. We tested the feasibility, safety, and impact of CRS-R-guided rehabilitation in the ICU for patients with DoC after acute hemorrhagic stroke.

DESIGN

Retrospective cohort study.

SETTING

This single-center study was conducted in the neurocritical care unit at the University of Maryland Medical Center.

PATIENTS

We analyzed records from consecutive patients with subarachnoid hemorrhage (SAH) or intracerebral hemorrhage (ICH), who underwent serial CRS-R assessments during ICU admission from April 1, 2018, to December 31, 2021, where CRS-R less than 8 is vegetative state/unresponsive wakefulness syndrome (VS/UWS); CRS-R greater than or equal to 8 is a minimally conscious state (MCS).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcomes included adverse events during CRS-R evaluations and associations between CRS-R and discharge disposition, therapy-based function, and mobility. We examined the utility of CRS-R compared with other therapist clinical assessment tools in predicting discharge disposition. Seventy-six patients (22 SAH, 54 ICH, median age = 59, 50% female) underwent 276 CRS-R sessions without adverse events. Discharge to acute rehabilitation occurred in 4.4% versus 41.9% of patients with a final CRS-R less than 8 and CRS-R greater than or equal to 8, respectively (odds ratio [OR] 13.4; 95% CI, 2.7-66.1; p < 0.001). Patients with MCS on final CRS-R completed more therapy sessions during hospitalization and had improved mobility and functional performance. Compared with other therapy assessment tools, the CRS-R had the best performance in predicting discharge disposition (area under the curve: 0.83; 95% CI, 0.72-0.94; p < 0.0001).

CONCLUSIONS

Early neurorehabilitation guided by CRS-R appears to be feasible and safe in the ICU following hemorrhagic stroke complicated by DoC and may enhance access to inpatient rehabilitation, with the potential for lasting benefit on recovery. Further research is needed to assess generalizability and understand the impact on long-term outcomes.

Neuroethics, Covert Consciousness, and Disability Rights: What Happens When Artificial Intelligence Meets Cognitive Motor Dissociation?

In this article, we consider the intersection of cognitive motor dissociation (CMD) and artificial intelligence (AI), hence when CMD meets AI. In covert consciousness, there is a discordance between the observed behavior, the traditional bedside mode of assessment, and the response to volitional commands as depicted by neuroimaging or EEG studies. This alphabet soup of acronyms represents both the promise and peril of nascent technology in covert consciousness. On the diagnostic side, there is the complexity and uncertainty of identifying the discordance between cognitive activity and overt behavior. On the therapeutic side, when AI is used to generate speech, there is the possibility of misrepresenting the thoughts and intentions of those who are otherwise voiceless. This concordance of factors makes the application of AI to CMD worthy of deeper consideration. We offer this analysis in the spirit of anticipatory governance, a prudential process by which one plans to prevent or mitigate unintended consequences of novel technology. We first consider the normative challenges posed by CMD for clinical practice, neuroethics, and the law. We then explore the history of covert consciousness and the relationship of severe brain injury to the right-to-die movement, before introducing three biographies of brain injury that highlight the potential impact of disability bias or ableism in clinical practice, assistive technology, and translational research. Subsequently, we explore how AI might give voice to conscious individuals who are unable to communicate and the ethical challenges that this technology must overcome to promote human flourishing drawing upon what Nussbaum and Sen have described as a "capabilities approach" to promote normative reasoning.

Perception of diagnosis by family caregivers in severe brain injury patients in China

OBJECTIVES

Surrogate decision-making by family caregivers for patients with severe brain injury is influenced by the availability and understanding of relevant information and expectations for future rehabilitation. We aimed to compare the consistency of family caregivers' perceptions with clinical diagnoses and to inform their expectation of prognosis in the future.

METHODS

The Coma Recovery Scale-Revised was used to assess the diagnosis of inpatients with severe brain injury between February 2019 and February 2020. A main family caregiver was included per patient. The family caregiver's perception of the patient's consciousness and expectations of future recovery were collected through questionnaires and compared consistently with the clinical diagnosis.

RESULTS

The final sample included 101 main family caregivers of patients (57 UWS, unresponsive wakefulness syndrome, 37 MCS, minimally conscious state, 7 EMCS, emergence from MCS) with severe brain injury. Only 57 family caregivers correctly assessed the level of consciousness as indicated by the CRS-R, showing weak consistency (Kappa = 0.217, P = 0.002). Family caregivers' demographic characteristics and CRS-R diagnosis influenced the consistency between perception and clinical diagnosis. Family caregivers who provided hands-on care to patients showed higher levels of consistent perception (AOR = 12.24, 95% CI = 2.06-73.00, P = 0.006). Compared to UWS, the family caregivers of MCS patients were more likely to have a correct perception (OR = 7.68, 95% CI = 1.34-44.06). Family caregivers had positive expectations for patients' recovery in terms of both communication and returning to normal life.

CONCLUSION

Nearly half of family caregivers have inadequate understanding of their relative's level of consciousness, and most of them report overly optimistic expectations that do not align with clinical diagnosis. Providing more medical information to family caregivers to support their surrogate decision-making process is essential.

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.

Exploring the Significance of Cognitive Motor Dissociation on Patient Outcome in Acute Disorders of Consciousness

Cognitive motor dissociation (CMD) is characterized by a dissociation between volitional brain responses and motor control, detectable only through techniques such as electroencephalography (EEG) and functional magnetic resonance imaging. Hence, it has recently emerged as a major challenge in the assessment of patients with disorders of consciousness. Specifically, this review focuses on the prognostic implications of CMD detection during the acute stage of brain injury. CMD patients were identified in each diagnostic category (coma, unresponsive wakefulness syndrome/vegetative state, minimally conscious state minus) with a relatively similar prevalence of around 20%. Current knowledge tends to indicate that the diagnosis of CMD in the acute phase often predicts a more favorable clinical outcome compared with other unresponsive non-CMD patients. Nevertheless, the review underscores the limited research in this domain, probably at least partially explained by its nascent nature and the lack of uniformity in the nomenclature for CMD-related disorders, hindering the impact of the literature in the field.

Traumatic Brain Injury and Traumatic Spinal Cord Injury

OBJECTIVE

This article reviews the mechanisms of primary traumatic injury to the brain and spinal cord, with an emphasis on grading severity, identifying surgical indications, anticipating complications, and managing secondary injury.

LATEST DEVELOPMENTS

Serum biomarkers have emerged for clinical decision making and prognosis after traumatic injury. Cortical spreading depolarization has been identified as a potentially modifiable mechanism of secondary injury after traumatic brain injury. Innovative methods to detect covert consciousness may inform prognosis and enrich future studies of coma recovery. The time-sensitive nature of spinal decompression is being elucidated.

ESSENTIAL POINTS

Proven management strategies for patients with severe neurotrauma in the intensive care unit include surgical decompression when appropriate, the optimization of perfusion, and the anticipation and treatment of complications. Despite validated models, predicting outcomes after traumatic brain injury remains challenging, requiring prognostic humility and a model of shared decision making with surrogate decision makers to establish care goals. Penetrating injuries, especially gunshot wounds, are often devastating and require public health and policy approaches that target prevention.

Disclosing Results of Tests for Covert Consciousness: A Framework for Ethical Translation

The advent of neurotechnologies including advanced functional magnetic resonance imaging and electroencephalography to detect states of awareness not detectable by traditional bedside neurobehavioral techniques (i.e., covert consciousness) promises to transform neuroscience research and clinical practice for patients with brain injury. As these interventions progress from research tools into actionable, guideline-endorsed clinical tests, ethical guidance for clinicians on how to responsibly communicate the sensitive results they yield is crucial yet remains underdeveloped. Drawing on insights from empirical and theoretical neuroethics research and our clinical experience with advanced neurotechnologies to detect consciousness in behaviorally unresponsive patients, we critically evaluate ethical promises and perils associated with disclosing the results of clinical covert consciousness assessments and describe a semistructured approach to responsible data sharing to mitigate potential risks.

Precise detection of awareness in disorders of consciousness using deep learning framework

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness

BACKGROUND

In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations.

METHODS

We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome.

RESULTS

Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]).

CONCLUSIONS

Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Uncovering sympathetic nervous system dysfunction in disorders of consciousness via heart rate variability during head-up tilt test

Few standardized tools are available for evaluation of disorders of consciousness (DOC). The potential of heart rate variability (HRV) during head-up tilt (HUT) test was investigated as a complementary evaluation tool. Twenty-one DOC patients and 21 healthy participants were enrolled in this study comparing clinical characteristics and HRV time- and frequency-domain outcomes and temporal changes during HUT test. During the 1st-5th min of the HUT, DOC group showed a significant increase and decrease in log low frequency (LF) (p = 0.045) and log normalized high frequency (nHF) (p = 0.02), respectively, compared to the supine position and had lower log normalized LF (nLF) (p = 0.004) and log ratio of low-to-high frequency (LF/HF) (p = 0.001) compared to healthy controls. As the HUT continued from the 6th to the 20th min, DOC group exhibited a significant increase in log LF/HF (16th-20th min) (p < 0.05), along with a decrease in log nHF (6th-10th and 16th-20th min) (p < 0.05) and maintained lower log LF, log nLF, and log LF/HF than controls (p < 0.05). 1st-10th min after returning to the supine position, DOC group demonstrated a significant decrease in log nHF (p < 0.01) and increases in log LF/HF (p < 0.01) and had lower log LF (p < 0.01) and log nLF (p < 0.05) compared to controls. In contrast, the control group exhibited a significant decrease in log nHF (p < 0.05) and increase in log LF/HF (p < 0.05) throughout the entire HUT test. Notably, no significant differences were observed when comparing time-domain outcomes reflecting parasympathetic nervous system between the two groups. HRV during HUT test indicated a delayed and attenuated autonomic response, particularly in the sympathetic nervous system, in DOC patients compared with healthy individuals.