How can we probe consciousness in unresponsive patients?

Clinical exam

Intensivists are used to probing the consciousness of brain injured patients during rounds using standard neurological examination techniques. The general principle of the clinical approach for assessment of consciousness is to probe behavior that is non-reflexive and can be considered as intentional. The most common items assessed are reactivity to sound and touch and, if necessary, responsiveness to nociceptive stimuli: is the patient able to open his/her eyes, is he/she attentive or eventually tracking? Clinicians also use simple verbal commands such as “stick out your tongue” and “show me two fingers”. These commands are often combined with a visual cue of the expected response also known as ‘mimicking’. This is employed for patients to minimize the impact of acoustic (i.e., deafness) or speech perception problems (i.e., aphasia). This clinical assessment requires good neurological examination skills to minimize the risk of misinterpretation (e.g., motor responses as part of reflexive responses to pain are thought to be intentional). However, even when performed by experienced clinicians, non-standardized neurological examinations have a high error rate (estimated to be as high as 40% in the chronic setting [6]).

As a part of the neurological exam, many clinicians quantify impairment of consciousness using the Glasgow Coma Scale (GCS). Even though this scale was originally developed to triage acute neurosurgical interventions for TBI patients and to assist prognostication, it may have some utility when applied for this purpose. However, the GCS is a very crude assessment of consciousness, especially when applied to tracheally intubated patients. The Full Outline of Unresponsiveness (FOUR) score may offer an alternative and has been rigorously validated [7]. Since the FOUR does not require verbal responses, it is more applicable for tracheally intubated patients. Probing visual tracking, it also allows a better detection of patients in minimally conscious and locked-in states (Table 2). Currently, the most widely accepted clinical scale designed for assessment of consciousness is the Coma Recovery Scale-Revised (CRS-R) [8]. This comprehensive scale is the gold standard in the field of consciousness research. CRS-R scoring ranges from 0 to 23 according to the presence or the absence of behavioral responses on a set of hierarchically ordered items testing auditory, visual, motor, oromotor, communication and arousal function. State of consciousness is determined by specific key behaviors (and not the total score) probed during the CRS-R assessment. For example, visual pursuit, reproducible movements to command and/or complex motor behavior scores distinguish minimally conscious state from the unresponsive wakefulness syndrome [9], also called vegetative state (see Table 1). However, application of the CRS-R in the ICU can be challenging since assessments of patients may at times require up to 45 min of examination. Another issue is that patients can fluctuate so exams need to be repeated several times before drawing any conclusions. All behavioral scales may incorrectly classify aphasic patients as unconscious but the FOUR score and CRS-R include assessments using visual cues, which may detect signs of awareness in aphasic patients.

Table 2 Approaches to assess consciousness Full size table

Assessing biomarkers that correlate with level of consciousness

The fundamental concept of this approach is based on using neurophysiologic correlates of brain activity as surrogates for levels of consciousness. Such markers include measures of brain metabolism, blood flow and electrical activity. These measures are assessed in a resting condition and correlated with current or future behavioral states. Comparisons are made of the obtained measures in patients appearing unconscious and healthy volunteers. Brain metabolism evaluated using 18F-fluorodeoxyglucose (FDG) position emission tomography (PET)-scans showed that hypometabolism in frontal and parietal cortices is seen in unresponsive wakefulness syndrome [10]. More generally, consciousness seems to vanish when brain metabolism drops below normal activity. Similar approaches have been developed using MRI arterial spin labelling sequences [11].

EEG can, amongst other approaches, be analyzed by decomposing it into spectral patterns, and quantifying complexity and connectivity. Spectral analysis is based on a Fourier transformation and provides information on the power distribution within the respective frequency bands (typically in the δ, θ, α, β and γ bands). Complexity of the EEG can be assessed by entropy measures (e.g., spectral entropy, K complexity, or permutation entropy). Functional connectivity between distant electrodes can be assessed using the spectral dimension (e.g., the debiased weighed phase lag index [wdPLI]) or information theoretical approaches (e.g., the weighed symbolic mutual information [wSMI]) [12, 13]. Similar methodologies assessing cortical functional connectivity have been developed using fMRI [14]. Candidate EEG features can then be used to train on an existing dataset of patients with known level of consciousness using a multivariate classifier to evaluate the EEG of a new patient [15].

Among these techniques, markers derived from resting state EEG seem to be the most promising in the ICU as imaging tests cannot be easily repeated and transport-related risks need to be considered for these sick patients. One study found a correlation between these EEG features (associating spectral, complexity and connectivity measures) and level of consciousness in ICU patients [16]. It is worth noting that the success of any multivariate classification approaches not only relies on the feature’s selection and the quality of the EEG processing but also largely on the quality of the labels provided to the algorithm as a training set. So far, these labels are usually based on behavioral assessments with obvious limitations.

Detecting correlates of conscious processing

Another approach to probe consciousness in unresponsive patients derives from neuroscientific and neuropsychological studies that have proposed physiological signatures of conscious processing in response to a given stimulus. One of these techniques focused on a late component of the evoked potential called P300 (it derives its name from the fact that it appears as a positive voltage around 300 ms following a stimulus). One of the most studied paradigms using this phenomenon in consciousness research is called the “Local-Global” paradigm [17]. Schematically this paradigm consists of delivering a subject sequences of sounds that embed two levels of auditory regularity, respectively at a local (within trial) and at a global (across trials) time scale. Whereas detection of local regularity can occur without awareness, detection of global regularity is highly correlated with access consciousness [17] (see [18] for a recent review).

A more recent approach that is at the boundaries between the two approaches described above (i.e., assessing biomarkers that correlate with the level of consciousness as well as detecting correlates of conscious processing itself) consists of measuring the complexity of brain responses to a direct stimulation of the brain using transcranial magnetic (TMS) pulses directly delivered to the parietal cortex. Using a specially designed EEG measure called perturbational complexity index, it is possible to estimate one’s level of consciousness with great accuracy in different settings (i.e., sleep, anesthesia and following brain injury) [19, 20]. This measure summarizes the complexity of the response as well as the functional connectivity in its temporal dynamic dimension. This technique paved the way for alternative interpretations of late evoked brain responses (e.g., the N70) to sensory stimulations observed during the acquisition of somatosensory evoked potentials (SSEP). These brain responses have been associated with prognosis of comatose patients but clinical application has been primarily limited due to a much larger degree of variability when compared to early components of evoked potentials (i.e., the N20 of the SSEP) [21]. Revisiting the neuro-correlates of these late components that follow the classical N20 using new computational measures that quantify connectivity (e.g., wdPLI, wSMI), complexity measures (e.g., permutation entropy) or the perturbational complexity index may provide innovative approaches at the bedside in the ICU to quantify measures that not only correlate with the current state but with future recovery of consciousness.

Detecting correlates of command following

Measuring physiologic changes to verbal commands allows the investigator to identify the state of cognitive motor dissociation. This approach has been the first to reliably demonstrate the existence of covert consciousness in patients that clinically meet the criteria of unresponsive wakefulness syndrome [1]. Using fMRI, it is possible to detect whether a patient is able to follow a simple verbal command (e.g., “imagine playing tennis” vs “imagine visiting your home”) by comparing the elicited blood oxygen level dependent (BOLD) imaging signal changes in an unresponsive appearing patient to those seen in a group of healthy volunteers. The first large study suggested that 10% of patients with unresponsive wakefulness syndrome are able to reliably do this [22], a state later termed cognitive motor dissociation [2]. Subsequently, the feasibility of using fEEG paradigms to detect cognitive motor dissociation in unresponsive patients was demonstrated by several teams using a motor imagery EEG paradigm at the bedside [23,24,25,26,27]. Patients with cognitive motor dissociation should be distinguished from patients who are able to process language stimuli (using EEG or fMRI) but without evidence of conscious processing (coined high-order cortex motor dissociation [HMD] [26], see Table 1).

Pitfalls and caveats of these techniques in the ICU

Technical aspects

Limitations of the clinical examination for assessing consciousness have been discussed extensively above. Imaging techniques allow spatial assessments of physical properties of interest (e.g., metabolism, blood flow) but for the purposes of studying ICU patients with impaired consciousness also have limitations in the real world. MRI and PET-scans both require transportation of the patient to the scanner, which potentially exposes patients to multiple risks (e.g., inferior monitoring, non-optimal environment in case of emergency, risk of accidental dislodging of tubes and catheters). To safely acquire MRI scans, sedation and paralytics may be required. Probing for correlates of consciousness under sedation is suboptimal. PET-scans are less problematic since the tracer can be administered just before the scan and the imaging can then be acquired under sedation. However, even for PET scans, transport will be necessary with all of the above outlined risks. Logistical challenges created include the additional personnel required to safely transport patients (e.g., nurse, physician, respiratory technician, MRI technician).

Among all the available techniques, EEG based approaches have enormous advantages in the ICU setting. EEG can be acquired at the bedside within the safe ICU environment. Associated costs for these tests will depend on local reimbursement and healthcare structures. Regardless, imaging tests like MRI or PET scans will likely be much more expensive in most health care systems when compared to EEG or evoked potentials. Additionally, EEG can be repeated many times per day, which is a huge advantage as consciousness is not a static phenomenon but may fluctuate throughout the day (see discussion about clinical limitations above). Assessments before and after interventions, for example, the administration of a medication, are more easily facilitated if repeat testing is easily available. Challenges unique to EEG assessments include electrical noise, which is very prevalent in the ICU environment, and artifacts created by involuntary movements such as myoclonus, respiratory artifact, and coughing. Seizures and other epileptiform patterns are additional major confounders to detect clear signatures of consciousness on the EEG. EEG leads may, in the hectic ICU environment, be removed to allow emergent head CT scans to be obtained to evaluate neurological changes, and placement of EEG leads may be limited by surgical wounds or bandages.

Mental imagery tasks are used both for fMRI and fEEG and are essentially very similar. These probe command following mostly to verbal commands and are fundamentally extensions of the neurological examination. Typically, patients are asked to perform (or to imagine) a motor movement that is thought to elicit a consistent BOLD or EEG signal change to be detected by fMRI or EEG, respectively. Major limitations are that patients need to be able to understand the command (challenging in patients that speak a different language, are deaf or aphasic), are interested in participating (challenging in patients with poor attention, abulia, delirium or in pain), and can keep the command in their working memory long enough to perform the task and to allow the classifier to detect the different brain activities (challenging in patients with advanced dementia). Importantly, both with fMRI and fEEG, patients can be identified that are conscious but for the reasons outlined above, consciousness cannot be ruled out for any of the patients that are classified as unconscious.

Confounding factors

Among the major confounding factors for the assessment of consciousness in the ICU setting, the following take a central role: sedation and delirium. Determining the exact level of consciousness is usually not a major concern for the clinician treating deeply sedated patients (e.g., those receiving treatment of refractory status epilepticus or intracranial hypertension). Medications used in this context include those used for general anesthesia. On the other hand, in patients who receive lower doses of sedation, assessments of consciousness can be very challenging. However, pharmacodynamics and pharmacokinetics are altered in deeply sedated patients in the ICU receiving prolonged courses of sedatives. Inferring absence of consciousness from the absence of responsiveness may lead to the erroneous assumption of unconsciousness in sedated patients as recently demonstrated in a study revealing that conscious experiences may occur under propofol-induced unresponsiveness [28].

Another caveat is the high prevalence (30%) of delirium in the ICU [29]. Although it is relatively easy to diagnose the classical form of delirium and to demonstrate consciousness (even though in an altered form), the commonly coined ‘hypoactive delirium’ that can represent half of the delirium can be more difficult to identify [30]. Delirious patients usually have attention deficit that could hamper the focus required for the detection of signature of conscious processing of a stimulus (e.g., the Local-Global paradigm) or the sustained attempt to follow verbal commands.

What are we detecting exactly?

Applying the above introduced techniques, three different kinds of signals as surrogates of covert consciousness can be detected in an unresponsive ICU patient: (1) a physiological biomarker that correlates with level of consciousness at the group level (e.g., FDG-PET scan measure of global brain metabolism or multivariate analysis of EEG features [perturbational complexity index]); (2) a correlate of conscious processing of a given stimulus at the individual level (e.g., the P3b using the Local-Global paradigm, perturbational complexity index); and (3) appropriate and sustainable brain activities in response to verbal commands at the individual level (e.g., using the motor imagery EEG paradigm). We propose that the last category represents at this point the most direct and convincing evidence of covert consciousness that has been termed cognitive motor dissociation. Indeed, physiologic biomarkers that correlate with consciousness have been developed on models trained on large datasets using clinical labels. Consequently, the confidence of how accurately a given patient is classified using these approaches mainly relies on the quality of the labels used in the training set. These labels are derived from clinical examinations which we know are imprecise. The relevance of neural correlates of conscious processing such as the P3b are still debated. In addition, evidence of conscious processing of simple stimuli does not imply the existence of conscious processing of more complex mental contents. Following commands revealed by fMRI or fEEG appears as the strongest evidence since it usually relies on statistics performed at the patient level (using for instance machine learning and permutation test).