Diagnostic Developments in Differentiating Unresponsive Wakefulness Syndrome and the Minimally Conscious State

When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals.

Statistical learning in patients in the minimally conscious state

When listening to an unknown language, listeners could learn the transitional probability between syllables and group frequently co-occurred syllables into a whole unit. Such statistical learning ability has been demonstrated for both pre-verbal infants and adults, even during passive listening. Here, we investigated whether statistical learning occurred in patients in minimally conscious state (MCS) and patients emerged from the minimally conscious state (EMCS) using electroencephalography (EEG). We presented to participants an isochronous sequence of syllables, which were composed of either 2-word real phrases or 2-word artificial phrases that were defined by the transitional probability between words. An inter-trial phase coherence (ITPC) analysis revealed that the phrase-rate EEG response was weakened in EMCS patients compared with healthy individuals, and was even more severely weakened in MCS patients. Although weak, the phrase-rate response or its harmonics remained statistically significant in MCS patients, suggesting that the statistical learning ability was preserved in MCS patients. The word-rate response was also weakened with a decreased level of consciousness. The harmonics of the word-rate response, however,were more salient in MCS than EMCS patients in the alpha and beta bands. Together with previous studies, the current results suggest that MCS patients retain residual learning ability, which can potentially be harnessed to induce neural plasticity, and that different frequency bands are differentially related to the consciousness level.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, all efforts of capturing the causal mechanistic generating principles have proven elusive, since they have been unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing the topological structure of the brain at each moment in time (its ‘information structure’), we are able to classify different brain states by using the statistics across time of these exact ‘information structures’ hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify the neuroimaging data from two classes of comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision.

Nociceptive Response Is a Possible Marker of Evolution in the Level of Consciousness in Unresponsive Wakefulness Syndrome Patients

The Nociception Coma Scale (NCS) and its revised version (NCS-R) were used to evaluate behavioral responses to pain in non-communicative patients. We hypothesized that if patients demonstrate changes to their NCS(-R) scores over time, their evolving behavioral abilities could indicate a forthcoming diagnostic improvement with the Coma Recovery Scale-Revised (CRS-R). Forty-three Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) patients were enrolled in the study. The patients were assessed weekly using the CRS-R and NCS(-R) for four consecutive weeks. The first assessment was within 10 days after hospitalization. The assessments were performed between 09:30 and 11:30 AM in a room with constant levels of humidity, light and temperature, as well as an absence of transient noise. Noxious stimuli were administered using a Newton-meter, with pressure applied to the fingernail bed for a maximum of 5 s unless interrupted by a behavioral response from subjects. Seventeen patients demonstrated improvements in their level of consciousness, 13 of whom showed significant behavioral changes through the NCS(-R) before being diagnosed with a Minimally Conscious State (MCS) according to the CRS-R. The behavioral changes observed using the NCS(-R) corresponded to a high probability of observing an improvement from VS/UWS to MCS. To characterize the increased likelihood of this transition, our results present threshold scores of ≥5 for the NCS (accuracy 86%, sensitivity 87%, and specificity 86%) and ≥3 for the NCS-R (accuracy 77%, sensitivity 89%, and specificity 73%). In conclusion, a careful evaluation of responses to nociceptive stimuli in DOC patients could constitute an effective procedure in assessing their evolving conscious state.

Disruption in structural-functional network repertoire and time resolved subcortical-frontoparietal connectivity in disorders of consciousness

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, i.e. so-called eigenmodes, were supplemented as potential markers. By analysing time-resolved functional connectivity from fMRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and fronto-parietal subcortical network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the global neuronal workspace theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.