Detecting the Potential for Consciousness in Unresponsive Patients Using the Perturbational Complexity Index

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.

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).

Perturbational complexity index in assessing responsiveness to rTMS treatment in patients with disorders of consciousness: a cross-over randomized controlled trial study

BACKGROUND

Disorders of Consciousness (DoC) caused by severe brain injuries represent a challenging clinical entity, which is easy to misdiagnosis and lacks effective treatment options. Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive neuroelectric stimulation method that shows promise in improving consciousness for DoC, especially in minimally conscious state (MCS). However, there is little evidence of its effectiveness, especially in RCT studies.

METHODS

Twenty MCS patients participated in a double-blind, randomized, crossover, sham-controlled clinical study to evaluate the safety and efficacy of rTMS for MCS. Subjects were randomized into two groups: one group received rTMS-active for 10 consecutive days (n = 10), and the other group received rTMS-sham for 10 consecutive days (n = 10). After a 10-day washout period, the two groups were crossed over and received the opposite treatment. the rTMS protocol consisted of 2,000 pulses per day in the left dorsolateral prefrontal cortex (L-DLPFC), sent at 10 Hz. The stimulation intensity was 90% of the resting motor threshold. Coma Recovery Scale Revised (CRS-R), the main evaluation index, was evaluated before and after each phase in a double-blind manner. Meanwhile RS-EEG and TMS-EEG data were acquired and relative alpha power (RAP), and perturbational complexity index based on state transitions (PCIst) were caculated.

RESULTS

One-way ANOVA revealed significantly higher scores in rTMS-active treatment compared to rTMS-sham across various measures, including CRS-R total score, RAP, PCIst (all P < 0.05). Among the 20 MCS patients, 7 (35%) were identified as responders following rTMS treatment. Compared to rTMS-sham, responder scores for CRS-R, RAP, and PCIst (all P < 0.05) were significantly elevated after rTMS-active treatment. Conversely, there was no significant difference observed in non-responders. Furthermore, post-hoc analysis revealed that baseline PCIst was significantly higher in responders than non-responders. Upon a 6-month follow-up, CRS-R scores significantly increased in all 20 patients (P = 0.026). However, the responder group exhibited a more favorable prognosis compared to the non-responder group (P = 0.031).

CONCLUSIONS

Applying 10 Hz rTMS to L-DLPFC significantly increased consciousness level in MCS patients. PCIst is a neurophysiological index that has the potential to evaluate and predict therapeutic efficacy.

TRIAL REGISTRATION

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CLINICALTRIALS

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Sleep-like cortical dynamics during wakefulness and their network effects following brain injury

By connecting old and recent notions, different spatial scales, and research domains, we introduce a novel framework on the consequences of brain injury focusing on a key role of slow waves. We argue that the long-standing finding of EEG slow waves after brain injury reflects the intrusion of sleep-like cortical dynamics during wakefulness; we illustrate how these dynamics are generated and how they can lead to functional network disruption and behavioral impairment. Finally, we outline a scenario whereby post-injury slow waves can be modulated to reawaken parts of the brain that have fallen asleep to optimize rehabilitation strategies and promote recovery.

Real-time optimization to enhance noninvasive cortical excitability assessment in the human dorsolateral prefrontal cortex

Objective

We currently lack a robust noninvasive method to measure prefrontal excitability in humans. Concurrent TMS and EEG in the prefrontal cortex is usually confounded by artifacts. Here we asked if real-time optimization could reduce artifacts and enhance a TMS-EEG measure of left prefrontal excitability.

Methods

This closed-loop optimization procedure adjusts left dlPFC TMS coil location, angle, and intensity in real-time based on the EEG response to TMS. Our outcome measure was the left prefrontal early (20-60 ms) and local TMS-evoked potential (EL-TEP).

Results

In 18 healthy participants, this optimization of coil angle and brain target significantly reduced artifacts by 63% and, when combined with an increase in intensity, increased EL-TEP magnitude by 75% compared to a non-optimized approach.

Conclusions

Real-time optimization of TMS parameters during dlPFC stimulation can enhance the EL-TEP.

Significance

Enhancing our ability to measure prefrontal excitability is important for monitoring pathological states and treatment response.

Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks

Recurrent neural networks exhibit chaotic dynamics when the variance in their connection strengths exceed a critical value. Recent work indicates connection variance also modulates learning strategies; networks learn "rich" representations when initialized with low coupling and "lazier" solutions with larger variance. Using Watts-Strogatz networks of varying sparsity, structure, and hidden weight variance, we find that the critical coupling strength dividing chaotic from ordered dynamics also differentiates rich and lazy learning strategies. Training moves both stable and chaotic networks closer to the edge of chaos, with networks learning richer representations before the transition to chaos. In contrast, biologically realistic connectivity structures foster stability over a wide range of variances. The transition to chaos is also reflected in a measure that clinically discriminates levels of consciousness, the perturbational complexity index (PCIst). Networks with high values of PCIst exhibit stable dynamics and rich learning, suggesting a consciousness prior may promote rich learning. The results suggest a clear relationship between critical dynamics, learning regimes and complexity-based measures of consciousness.

Extrapolating animal consciousness

I argue that the question of animal consciousness is an extrapolation problem and, as such, is best tackled by deploying currently accepted methodology for validating experimental models of a phenomenon of interest. This methodology relies on an assessment of similarities and dissimilarities between experimental models, the partial replication of findings across complementary models, and evidence from the successes and failures of explanations, technologies and medical applications developed by extrapolating and aggregating findings from multiple models. Crucially important, this methodology does not require a commitment to any particular theory or construct of consciousness, thus avoiding theory-biased reinterpretations of empirical findings rampant in the literature.

Dissociations between spontaneous electroencephalographic features and the perturbational complexity index in the minimally conscious state

The analysis of spontaneous electroencephalogram (EEG) is a cornerstone in the assessment of patients with disorders of consciousness (DoC). Although preserved EEG patterns are highly suggestive of consciousness even in unresponsive patients, moderately or severely abnormal patterns are difficult to interpret. Indeed, growing evidence shows that consciousness can be present despite either large delta or reduced alpha activity in spontaneous EEG. Quantifying the complexity of EEG responses to direct cortical perturbations (perturbational complexity index [PCI]) may complement the observational approach and provide a reliable assessment of consciousness even when spontaneous EEG features are inconclusive. To seek empirical evidence of this hypothesis, we compared PCI with EEG spectral measures in the same population of minimally conscious state (MCS) patients (n = 40) hospitalized in rehabilitation facilities. We found a remarkable variability in spontaneous EEG features across MCS patients as compared with healthy controls: in particular, a pattern of predominant delta and highly reduced alpha power-more often observed in vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients-was found in a non-negligible number of MCS patients. Conversely, PCI values invariably fell above an externally validated empirical cutoff for consciousness in all MCS patients, consistent with the presence of clearly discernible, albeit fleeting, behavioural signs of awareness. These results confirm that, in some MCS patients, spontaneous EEG rhythms may be inconclusive about the actual capacity for consciousness and suggest that a perturbational approach can effectively compensate for this pitfall with practical implications for the individual patient's stratification and tailored rehabilitation.

Unconsciousness or unresponsiveness in akinetic mutism? Insights from a multimodal longitudinal exploration

The clinical assessment of patients with disorders of consciousness (DoC) relies on the observation of behavioural responses to standardised sensory stimulation. However, several medical comorbidities may directly impair the production of reproducible and appropriate responses, thus reducing the sensitivity of behaviour-based diagnoses. One such comorbidity is akinetic mutism (AM), a rare neurological syndrome characterised by the inability to initiate volitional motor responses, sometimes associated with clinical presentations that overlap with those of DoC. In this paper, we describe the case of a patient with large bilateral mesial frontal lesions, showing prolonged behavioural unresponsiveness and severe disorganisation of electroencephalographic (EEG) background, compatible with a vegetative state/unresponsive wakefulness syndrome (VS/UWS). By applying an unprecedented multimodal battery of advanced imaging and electrophysiology-based techniques (AIE) encompassing spontaneous EEG, evoked potentials, event-related potentials, transcranial magnetic stimulation combined with EEG and structural and functional MRI, we provide the following: (i) a demonstration of the preservation of consciousness despite unresponsiveness in the context of AM, (ii) a plausible neurophysiological explanation for behavioural unresponsiveness and its subsequent recovery during rehabilitation stay and (iii) novel insights into the relationships between DoC, AM and parkinsonism. The present case offers proof-of-principle evidence supporting the clinical utility of a multimodal hierarchical workflow that combines AIEs to detect covert signs of consciousness in unresponsive patients.

Multimodal approaches supporting the diagnosis, prognosis and investigation of neural correlates of disorders of consciousness: A systematic review

The limits of the standard, behaviour-based clinical assessment of patients with disorders of consciousness (DoC) prompted the employment of functional neuroimaging, neurometabolic, neurophysiological and neurostimulation techniques, to detect brain-based covert markers of awareness. However, uni-modal approaches, consisting in employing just one of those techniques, are usually not sufficient to provide an exhaustive exploration of the neural underpinnings of residual awareness. This systematic review aimed at collecting the evidence from studies employing a multimodal approach, that is, combining more instruments to complement DoC diagnosis, prognosis and better investigating their neural correlates. Following the PRISMA guidelines, records from PubMed, EMBASE and Scopus were screened to select peer-review original articles in which a multi-modal approach was used for the assessment of adult patients with a diagnosis of DoC. Ninety-two observational studies and 32 case reports or case series met the inclusion criteria. Results highlighted a diagnostic and prognostic advantage of multi-modal approaches that involve electroencephalography-based (EEG-based) measurements together with neuroimaging or neurometabolic data or with neurostimulation. Multimodal assessment deepened the knowledge on the neural networks underlying consciousness, by showing correlations between the integrity of the default mode network and the different clinical diagnosis of DoC. However, except for studies using transcranial magnetic stimulation combined with electroencephalography, the integration of more than one technique in most of the cases occurs without an a priori-designed multi-modal diagnostic approach. Our review supports the feasibility and underlines the advantages of a multimodal approach for the diagnosis, prognosis and for the investigation of neural correlates of DoCs.

Cognitive Motor Dissociation: Gap Analysis and Future Directions

BACKGROUND

Patients with disorders of consciousness who are behaviorally unresponsive may demonstrate volitional brain responses to motor imagery or motor commands detectable on functional magnetic resonance imaging or electroencephalography. This state of cognitive motor dissociation (CMD) may have prognostic significance.

METHODS

The Neurocritical Care Society's Curing Coma Campaign identified an international group of experts who convened in a series of monthly online meetings between September 2021 and April 2023 to examine the science of CMD and identify key knowledge gaps and unmet needs.

RESULTS

The group identified major knowledge gaps in CMD research: (1) lack of information about patient experiences and caregiver accounts of CMD, (2) limited epidemiological data on CMD, (3) uncertainty about underlying mechanisms of CMD, (4) methodological variability that limits testing of CMD as a biomarker for prognostication and treatment trials, (5) educational gaps for health care personnel about the incidence and potential prognostic relevance of CMD, and (6) challenges related to identification of patients with CMD who may be able to communicate using brain-computer interfaces.

CONCLUSIONS

To improve the management of patients with disorders of consciousness, research efforts should address these mechanistic, epidemiological, bioengineering, and educational gaps to enable large-scale implementation of CMD assessment in clinical practice.

Technological Modalities in the Assessment and Treatment of Disorders of Consciousness

Over the last 10 years, there have been rapid advances made in technologies that can be utilized in the diagnosis and treatment of patients with a disorder of consciousness (DoC). This article provides a comprehensive review of these modalities including the evidence supporting their potential use in DoC. This review specifically addresses diagnostic, non-invasive therapeutic, and invasive therapeutic technological modalities except for neuroimaging, which is discussed in another article. While technologic advances appear promising for both assessment and treatment of patients with a DoC, high-quality evidence supporting widespread clinical adoption remains limited.

Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group

BACKGROUND

Electroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group.

METHODS

After assessing previously published pertinent CDEs, we developed new CDEs and categorized them into "disease core," "basic," "supplemental," and "exploratory." Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs.

RESULTS

After identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group.

CONCLUSIONS

Adherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research.

The technology, opportunities, and challenges of Synthetic Biological Intelligence

Integrating neural cultures developed through synthetic biology methods with digital computing has enabled the early development of Synthetic Biological Intelligence (SBI). Recently, key studies have emphasized the advantages of biological neural systems in some information processing tasks. However, neither the technology behind this early development, nor the potential ethical opportunities or challenges, have been explored in detail yet. Here, we review the key aspects that facilitate the development of SBI and explore potential applications. Considering these foreseeable use cases, various ethical implications are proposed. Ultimately, this work aims to provide a robust framework to structure ethical considerations to ensure that SBI technology can be both researched and applied responsibly.

Evaluation of consciousness rehabilitation via neuroimaging methods

Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.

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

Patients with disorders of consciousness (DoC) represent a group of severely brain-injured patients with varying capacities for consciousness in terms of both wakefulness and awareness. The current state-of-the-art for assessing these patients is through standardised behavioural examinations, but inaccuracies are commonplace. Neuroimaging and electrophysiological techniques have revealed vast insights into the relationships between neural alterations, andcognitive and behavioural features of consciousness in patients with DoC. This has led to the establishment of neuroimaging paradigms for the clinical assessment of DoC patients. Here, we review selected neuroimaging findings on the DoC population, outlining key findings of the dysfunction underlying DoC and presenting the current clinical utility of neuroimaging tools. We discuss that whilst individual brain areas play instrumental roles in generating and supporting consciousness, activation of these areas alone is not sufficient for conscious experience. Instead, for consciousness to arise, we need preserved thalamo-cortical circuits, in addition to sufficient connectivity between distinctly differentiated brain networks, underlined by connectivity both within, and between such brain networks. Finally, we present recent advances and future perspectives in computational methodologies applied to DoC, supporting the notion that progress in the science of DoC will be driven by a symbiosis of these data-driven analyses, and theory-driven research. Both perspectives will work in tandem to provide mechanistic insights contextualised within theoretical frameworks which ultimately inform the practice of clinical neurology.

Clinical diagnostic utility of transcranial magnetic stimulation in neurological disorders. Updated report of an IFCN committee

The review provides a comprehensive update (previous report: Chen R, Cros D, Curra A, Di Lazzaro V, Lefaucheur JP, Magistris MR, et al. The clinical diagnostic utility of transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol 2008;119(3):504-32) on clinical diagnostic utility of transcranial magnetic stimulation (TMS) in neurological diseases. Most TMS measures rely on stimulation of motor cortex and recording of motor evoked potentials. Paired-pulse TMS techniques, incorporating conventional amplitude-based and threshold tracking, have established clinical utility in neurodegenerative, movement, episodic (epilepsy, migraines), chronic pain and functional diseases. Cortical hyperexcitability has emerged as a diagnostic aid in amyotrophic lateral sclerosis. Single-pulse TMS measures are of utility in stroke, and myelopathy even in the absence of radiological changes. Short-latency afferent inhibition, related to central cholinergic transmission, is reduced in Alzheimer's disease. The triple stimulation technique (TST) may enhance diagnostic utility of conventional TMS measures to detect upper motor neuron involvement. The recording of motor evoked potentials can be used to perform functional mapping of the motor cortex or in preoperative assessment of eloquent brain regions before surgical resection of brain tumors. TMS exhibits utility in assessing lumbosacral/cervical nerve root function, especially in demyelinating neuropathies, and may be of utility in localizing the site of facial nerve palsies. TMS measures also have high sensitivity in detecting subclinical corticospinal lesions in multiple sclerosis. Abnormalities in central motor conduction time or TST correlate with motor impairment and disability in MS. Cerebellar stimulation may detect lesions in the cerebellum or cerebello-dentato-thalamo-motor cortical pathways. Combining TMS with electroencephalography, provides a novel method to measure parameters altered in neurological disorders, including cortical excitability, effective connectivity, and response complexity.

Measuring Consciousness in the Intensive Care Unit

Early reemergence of consciousness predicts long-term functional recovery for patients with severe brain injury. However, tools to reliably detect consciousness in the intensive care unit are lacking. Transcranial magnetic stimulation electroencephalography has the potential to detect consciousness in the intensive care unit, predict recovery, and prevent premature withdrawal of life-sustaining therapy.

Assessing awareness in severe Alzheimer's disease

There is an urgent need to understand the nature of awareness in people with severe Alzheimer's disease (AD) to ensure effective person-centered care. Objective biomarkers of awareness validated in other clinical groups (e.g., anesthesia, minimally conscious states) offer an opportunity to investigate awareness in people with severe AD. In this article we demonstrate the feasibility of using Transcranial magnetic stimulation (TMS) combined with EEG, event related potentials (ERPs) and fMRI to assess awareness in severe AD. TMS-EEG was performed in six healthy older controls and three people with severe AD. The perturbational complexity index (PCIST) was calculated as a measure of capacity for conscious awareness. People with severe AD demonstrated a PCIST around or below the threshold for consciousness, suggesting reduced capacity for consciousness. ERPs were recorded during a visual perception paradigm. In response to viewing faces, two patients with severe AD provisionally demonstrated similar visual awareness negativity to healthy controls. Using a validated fMRI movie-viewing task, independent component analysis in two healthy controls and one patient with severe AD revealed activation in auditory, visual and fronto-parietal networks. Activation patterns in fronto-parietal networks did not significantly correlate between the patient and controls, suggesting potential differences in conscious awareness and engagement with the movie. Although methodological issues remain, these results demonstrate the feasibility of using objective measures of awareness in severe AD. We raise a number of challenges and research questions that should be addressed using these biomarkers of awareness in future studies to improve understanding and care for people with severe AD.

The role of neuronavigation in TMS-EEG studies: current applications and future perspectives

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) allows measuring non-invasively the electrical response of the human cerebral cortex to a direct perturbation. Complementing TMS-EEG with a structural neuronavigation tool (nTMS-EEG) is key for accurately selecting cortical areas, targeting them, and adjusting the stimulation parameters based on some relevant anatomical priors. This step, together with the employment of visualization tools designed to perform a quality check of TMS-evoked potentials (TEPs) in real-time during acquisition, is key for maximizing the impact of the TMS pulse on the cortex and in ensuring highly reproducible measurements within sessions and across subjects. Moreover, storing stimulation parameters in the neuronavigation system can help in reproducing the stimulation parameters within and across experimental sessions and sharing them across research centers. Finally, the systematic employment of neuronavigation in TMS-EEG studies is also key to standardize measurements in clinical populations in search for reliable diagnostic and prognostic TMS-EEG-based biomarkers for neurological and psychiatric disorders.

Measures of differentiation and integration: One step closer to consciousness

Interpreting empirical measures of integration and differentiation as indices of cortical performance and memory consolidation during wakefulness rather than consciousness per se is inconsistent with the literature. Recent studies show that these theory-inspired measures can dissociate from such processes and reliably index the brain's capacity for experience. We consider this as a positive trend in consciousness research.

Consciousness and complexity: a consilience of evidence

Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.

Update on neuroimaging in disorders of consciousness

PURPOSE OF REVIEW

Neuroimaging has acquired a prominent place in the assessment of disorders of consciousness (DoC). Rapidly evolving technologies combined with state-of-the-art data analyses open new horizons to probe brain activity, but selecting appropriate imaging modalities from the plethora of available techniques can be challenging for clinicians. This update reviews selected advances in neuroimaging that demonstrate clinical relevance and translational potential in the assessment of severely brain-injured patients with DoC.

RECENT FINDINGS

Magnetic resonance imaging and high-density electroencephalography provide measurements of brain connectivity between functional networks, assessments of language function, detection of covert consciousness, and prognostic markers of recovery. Positron emission tomography can identify patients with preserved brain metabolism despite clinical unresponsiveness and can measure glucose consumption rates in targeted brain regions. Transcranial magnetic stimulation and near-infrared spectroscopy are noninvasive and practical tools with promising clinical applications.

SUMMARY

Each neuroimaging technique conveys advantages and pitfalls to assess consciousness. We recommend a multimodal approach in which complementary techniques provide diagnostic and prognostic information about brain function. Patients demonstrating neuroimaging evidence of covert consciousness may benefit from early adapted rehabilitation. Translating methodological advances to clinical care will require the implementation of recently published international guidelines and the integration of neuroimaging techniques into patient-centered decision-making algorithms.