Toward Improving Diagnostic Strategies in Chronic Disorders of Consciousness: An Overview on the (Re-)Emergent Role of Neurophysiology

Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness

The human brain entertains rich spatiotemporal dynamics, which are drastically reconfigured when consciousness is lost due to anaesthesia or disorders of consciousness (DOC). Here, we sought to identify the neurobiological mechanisms that explain how transient pharmacological intervention and chronic neuroanatomical injury can lead to common reconfigurations of neural activity. We developed and systematically perturbed a neurobiologically realistic model of whole-brain haemodynamic signals. By incorporating PET data about the cortical distribution of GABA receptors, our computational model reveals a key role of spatially-specific local inhibition for reproducing the functional MRI activity observed during anaesthesia with the GABA-ergic agent propofol. Additionally, incorporating diffusion MRI data obtained from DOC patients reveals that the dynamics that characterise loss of consciousness can also emerge from randomised neuroanatomical connectivity. Our results generalise between anaesthesia and DOC datasets, demonstrating how increased inhibition and connectome perturbation represent distinct neurobiological paths towards the characteristic activity of the unconscious brain.

Perturbations in a large-scale whole-brain model suggest that anesthesia and injury may be imparting functionally similar effects in terms of brain dynamics.

Critical Illness Polyneuropathy and Myopathy and Clinical Detection of the Recovery of Consciousness in Severe Acquired Brain Injury Patients with Disorders of Consciousness after Rehabilitation

Background: Disorders of consciousness (DoCs) include unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Critical illness polyneuropathy and myopathy (CIPNM) is frequent in severe acquired brain injuries and impacts functional outcomes at discharge from the intensive rehabilitation unit (IRU). We investigated the prevalence of CIPNM in DoCs and its relationship with the consciousness assessment. Methods: Patients with DoCs were retrospectively selected from the database including patients admitted to the IRU of the IRCCS Don Gnocchi Foundation, Florence, from August 2012 to May 2020. Electroneurography/electromyography was performed at admission. Consciousness was assessed using the Coma Recovery Scale-Revised (CRS-R) at admission and discharge. Patients transitioning from a lower consciousness state to a higher one were classified as improved responsiveness (IR). Results: A total of 177 patients were included (UWS: 81 (45.8%); MCS: 96 (54.2%); 78 (44.1%) women; 67 years (IQR: 20). At admission, 108 (61.0%) patients had CIPNM. At discharge, 117 (66.1%) patients presented an IR. In the multivariate analysis, CRS-R at admission (p = 0.006; OR: 1.462) and CIPNM (p = 0.039; OR: −1.252) remained significantly associated with IR only for the UWS patients. Conclusions: CIPNM is frequent in DoCs and needs to be considered during the clinical consciousness assessment, especially in patients with UWS.

Brain Data in Pediatric Disorders of Consciousness: Special Considerations

SUMMARY

The diagnosis and management of disorders of consciousness in children continue to present a clinical, research, and ethical challenge. Though the practice guidelines for diagnosis and management of disorders of consciousness in adults are supported by decades of empirical and pragmatic evidence, similar guidelines for infants and children are lacking. The maturing conscious experience and the limited behavioral repertoire to report consciousness in this age group restrict extrapolation from the adult literature. Equally challenging is the process of heightened structural and functional neuroplasticity in the developing brain, which adds a layer of complexity to the investigation of the neural correlates of consciousness in infants and children. This review discusses the clinical assessment of pediatric disorders of consciousness and delineates the diagnostic and prognostic utility of neurophysiological and neuroimaging correlates of consciousness. The potential relevance of these correlates for the developing brain based on existing theoretical models of consciousness in adults is outlined.

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

Pain Perception in Disorder of Consciousness: A Scoping Review on Current Knowledge, Clinical Applications, and Future Perspective

Pain perception in individuals with prolonged disorders of consciousness (PDOC) is still a matter of debate. Advanced neuroimaging studies suggest some cortical activations even in patients with unresponsive wakefulness syndrome (UWS) compared to those with a minimally conscious state (MCS). Therefore, pain perception has to be considered even in individuals with UWS. However, advanced neuroimaging assessment can be challenging to conduct, and its findings are sometimes difficult to be interpreted. Conversely, multichannel electroencephalography (EEG) and laser-evoked potentials (LEPs) can be carried out quickly and are more adaptable to the clinical needs. In this scoping review, we dealt with the neurophysiological basis underpinning pain in PDOC, pointing out how pain perception assessment in these individuals might help in reducing the misdiagnosis rate. The available literature data suggest that patients with UWS show a more severe functional connectivity breakdown among the pain-related brain areas compared to individuals in MCS, pointing out that pain perception increases with the level of consciousness. However, there are noteworthy exceptions, because some UWS patients show pain-related cortical activations that partially overlap those observed in MCS individuals. This suggests that some patients with UWS may have residual brain functional connectivity supporting the somatosensory, affective, and cognitive aspects of pain processing (i.e., a conscious experience of the unpleasantness of pain), rather than only being able to show autonomic responses to potentially harmful stimuli. Therefore, the significance of the neurophysiological approach to pain perception in PDOC seems to be clear, and despite some methodological caveats (including intensity of stimulation, multimodal paradigms, and active vs. passive stimulation protocols), remain to be solved. To summarize, an accurate clinical and neurophysiological assessment should always be performed for a better understanding of pain perception neurophysiological underpinnings, a more precise differential diagnosis at the level of individual cases as well as group comparisons, and patient-tailored management.

Multiplex and Multilayer Network EEG Analyses: A Novel Strategy in the Differential Diagnosis of Patients with Chronic Disorders of Consciousness

The deterioration of specific topological network measures that quantify different features of whole-brain functional network organization can be considered a marker for awareness impairment. Such topological measures reflect the functional interactions of multiple brain structures, which support the integration of different sensorimotor information subtending awareness. However, conventional, single-layer, graph theoretical analysis (GTA)-based approaches cannot always reliably differentiate patients with Disorders of Consciousness (DoC). Using multiplex and multilayer network analyses of frequency-specific and area-specific networks, we investigated functional connectivity during resting-state EEG in 17 patients with Unresponsive Wakefulness Syndrome (UWS) and 15 with Minimally Conscious State (MCS). Multiplex and multilayer network metrics indicated the deterioration and heterogeneity of functional networks and, particularly, the frontal-parietal (FP), as the discriminant between patients with MCS and UWS. These data were not appreciable when considering each individual frequency-specific network. The distinctive properties of multiplex/multilayer network metrics and individual frequency-specific network metrics further suggest the value of integrating the networks as opposed to analyzing frequency-specific network metrics one at a time. The hub vulnerability of these regions was positively correlated with the behavioral responsiveness, thus strengthening the clinically-based differential diagnosis. Therefore, it may be beneficial to adopt both multiplex and multilayer network analyses when expanding the conventional GTA-based analyses in the differential diagnosis of patients with DoC. Multiplex analysis differentiated patients at a group level, whereas the multilayer analysis offered complementary information to differentiate patients with DoC individually. Although further studies are necessary to confirm our preliminary findings, these results contribute to the issue of DoC differential diagnosis and may help in guiding patient-tailored management.

Virtual Reality Based Cognitive Rehabilitation in Minimally Conscious State: A Case Report with EEG Findings and Systematic Literature Review

Chronic disorders of consciousness cause a total or partial and fluctuating unawareness of the surrounding environment. Virtual reality (VR) can be useful as a diagnostic and/or a neurorehabilitation tool, and its effects can be monitored by means of both clinical and electroencephalography (EEG) data recording of brain activity. We reported on the case of a 17-year-old patient with a disorder of consciousness (DoC) who was provided with VR training to improve her cognitive-behavioral outcomes, which were assessed using clinical scales (the Coma Recovery Scale-Revised, the Disability Rating Scale, and the Rancho Los Amigos Levels of Cognitive Functioning), as well as EEG recording, during VR training sessions. At the end of the training, significant improvements in both clinical and neurophysiological outcomes were achieved. Then, we carried out a systematic review of the literature to investigate the role of EEG and VR in the management of patients with DoC. A search on PubMed, Web of Science, Scopus, and Google Scholar databases was performed, using the keywords: “disorders of consciousness” and “virtual reality”, or “EEG”. The results of the literature review suggest that neurophysiological data in combination with VR could be useful in evaluating the reactions induced by different paradigms in DoC patients, helping in the differential diagnosis. In conclusion, the EEG plus VR approach used with our patient could be promising to define the most appropriate stimulation protocol, so as to promote a better personalization of the rehabilitation program. However, further clinical trials, as well as meta-analysis of the literature, are needed to be affirmative on the role of VR in patients with DoC.