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Background: Previous studies have separately reported impaired functional, structural, and effective connectivity in patients with disorders of consciousness (DOC). The perturbational complexity index (PCI) is a transcranial magnetic stimulation (TMS) derived marker of effective connectivity. The global fractional anisotropy (FA) is a marker of structural integrity. Little is known about how these parameters are related to each other., Objective: We aimed at testing the relationship between structural integrity and effective connectivity., Methods: We assessed 23 patients with severe brain injury more than 4 weeks post-onset, leading to DOC or locked-in syndrome, and 14 healthy subjects. We calculated PCI using repeated single pulse TMS coupled with high-density electroencephalography, and used it as a surrogate of effective connectivity. Structural integrity was measured using the global FA, derived from diffusion weighted imaging. We used linear regression modelling to test our hypothesis, and computed the correlation between PCI and FA in different groups., Results: Global FA could predict 74% of PCI variance in the whole sample and 56% in the patients' group. No other predictors (age, gender, time since onset, behavioural score) improved the models. FA and PCI were correlated in the whole population (r = 0.86, p < 0.0001), the patients, and the healthy subjects subgroups., Conclusion: We here demonstrated that effective connectivity correlates with structural integrity in brain-injured patients. Increased structural damage level decreases effective connectivity, which could prevent the emergence of consciousness., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Making an accurate diagnosis in patients with disorders of consciousness remains challenging. 18 F-fluorodeoxyglucose (FDG)-PET has been validated as a diagnostic tool in this population, and allows identifying unresponsive patients with a capacity for consciousness. In parallel, the perturbational complexity index (PCI), a new measure based on the analysis of the electroencephalographic response to transcranial magnetic stimulation, has also been suggested as a tool to distinguish between unconscious and conscious states. The aim of the study was to cross-validate FDG-PET and PCI, and to identify signs of consciousness in otherwise unresponsive patients., Methods: We jointly applied the Coma Recovery Scale-Revised, FDG-PET and PCI to assess 24 patients with non-acute disorders of consciousness or locked-in syndrome (13 male; 19-54 years old; 12 traumatic; 9 unresponsive wakefulness syndrome, 11 minimally conscious state; 2 emergence from the minimally conscious state, and 2 locked-in syndrome)., Results: FDG-PET and PCI provided congruent results in 22 patients, regardless of their behavioural diagnosis. Notably, FDG-PET and PCI revealed preserved metabolic rates and high complexity levels in four patients who were behaviourally unresponsive., Conclusion: We propose that jointly measuring the metabolic activity and the electrophysiological complexity of cortical circuits is a useful complement to the diagnosis and stratification of patients with disorders of consciousness.

Objective: Validating objective, brain-based indices of consciousness in behaviorally unresponsive patients represents a challenge due to the impossibility of obtaining independent evidence through subjective reports. Here we address this problem by first validating a promising metric of consciousness-the Perturbational Complexity Index (PCI)-in a benchmark population who could confirm the presence or absence of consciousness through subjective reports, and then applying the same index to patients with disorders of consciousness (DOCs)., Methods: The benchmark population encompassed 150 healthy controls and communicative brain-injured subjects in various states of conscious wakefulness, disconnected consciousness, and unconsciousness. Receiver operating characteristic curve analysis was performed to define an optimal cutoff for discriminating between the conscious and unconscious conditions. This cutoff was then applied to a cohort of noncommunicative DOC patients (38 in a minimally conscious state [MCS] and 43 in a vegetative state [VS])., Results: We found an empirical cutoff that discriminated with 100% sensitivity and specificity between the conscious and the unconscious conditions in the benchmark population. This cutoff resulted in a sensitivity of 94.7% in detecting MCS and allowed the identification of a number of unresponsive VS patients (9 of 43) with high values of PCI, overlapping with the distribution of the benchmark conscious condition., Interpretation: Given its high sensitivity and specificity in the benchmark and MCS population, PCI offers a reliable, independently validated stratification of unresponsive patients that has important physiopathological and therapeutic implications. In particular, the high-PCI subgroup of VS patients may retain a capacity for consciousness that is not expressed in behavior. Ann Neurol 2016;80:718-729., (© 2016 The Authors. Annals of Neurology published by Wiley Periodicals, Inc. on behalf of American Neurological Association.)

We normally assess another individual's level of consciousness based on her or his ability to interact with the surrounding environment and communicate. Usually, if we observe purposeful behavior, appropriate responses to sensory inputs, and, above all, appropriate answers to questions, we can be reasonably sure that the person is conscious. However, we know that consciousness can be entirely within the brain, even in the absence of any interaction with the external world; this happens almost every night, while we dream. Yet, to this day, we lack an objective, dependable measure of the level of consciousness that is independent of processing sensory inputs and producing appropriate motor outputs. Theoretically, consciousness is thought to require the joint presence of functional integration and functional differentiation, otherwise defined as brain complexity. Here we review a series of recent studies in which Transcranial Magnetic Stimulation combined with electroencephalography (TMS/EEG) has been employed to quantify brain complexity in wakefulness and during physiological (sleep), pharmacological (anesthesia) and pathological (brain injury) loss of consciousness. These studies invariably show that the complexity of the cortical response to TMS collapses when consciousness is lost during deep sleep, anesthesia and vegetative state following severe brain injury, while it recovers when consciousness resurges in wakefulness, during dreaming, in the minimally conscious state or locked-in syndrome. The present paper will also focus on how this approach may contribute to unveiling the pathophysiology of disorders of consciousness affecting brain-injured patients. Finally, we will underline some crucial methodological aspects concerning TMS/EEG measurements of brain complexity.

The neural mechanisms underlying electrophysiological changes observed in patients with disorders of consciousness following a coma remain poorly understood. The aim of this study is to investigate the mechanisms underlying the differences in spontaneous electroencephalography (EEG) between patients in vegetative/unresponsive wakefulness syndrome, minimally conscious state, emergence of the minimally conscious state and age-matched healthy control subjects. Forty recordings of spontaneous scalp EEG were performed in 27 patients who were comatose on admission, and on healthy controls. Multivariate Granger causality and transfer entropy were applied to the data. Distinctive patterns of putative bottlenecks of information were associated with each conscious state. Healthy controls are characterized by a greater amount of synergetic contributions from duplets of variables. In conclusion a novel set of measures was tested to get a new insight on the pattern of information transfer in a network of scalp electrodes in patients with disorders of consciousness.

Background: This review discusses the advantages of transcranial magnetic stimulation combined with high-density electroencephalography (TMS-hdEEG) over other current techniques of brain imaging., Methods and Results: Its application was reviewed, focusing particularly on disorders of consciousness, in the perspective of recent theories of consciousness. Assessment of non-communicative patients with disorders of consciousness remains a clinical challenge and objective measures of the level of consciousness are still needed. Current theories suggest that a key requirement for consciousness is the brain's capacity to rapidly integrate information across different specialized cortical areas. TMS-EEG allows the stimulation of any given cortical area and the recording of the immediate electrical cortical response. This technique has recently been successfully employed to measure changes in brain complexity under physiological, pharmacological and pathological conditions., Conclusions: This suggests that TMS-EEG is a reliable tool to discriminate between conscious and unconscious patients at the single subject level. Future works are needed to validate and implement this technique as a clinical tool.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness. The authors propose an explainable consciousness indicator using deep learning to quantify arousal and awareness under sleep, anesthesia, and in patients with disorders of consciousness. [ABSTRACT FROM AUTHOR]

Although the clinical evaluation remains central for the diagnosis of DOC patients, several neurophysiological techniques and metrics ([2]) can help in stratifying patients and reduce misdiagnosis. Keywords: disorders of consciousness; EEG; evoked potential; cognitive motor dissociation; coma; TMS; TCDS EN disorders of consciousness EEG evoked potential cognitive motor dissociation coma TMS TCDS 1 3 3 11/18/21 20211116 NES 211116 Disorders of consciousness(DOC) occur in nearly 20% of patients who underwent a brain injury due to different etiologies ([1]). Rehabilitation of severely injured patients with DOC aims at stimulating recovery of sensory perception and interaction of patients with themselves and their environment. [Extracted from the article]

Neuroimaging tools could open a window on residual neurofunctional activity in the absence of detectable behavioural responses in patients with disorders of consciousness (DOC). Nevertheless, the literature on this topic is characterised by a large heterogeneity of paradigms and methodological approaches that can undermine the reproducibility of the results. To explicitly test whether task-related functional magnetic resonance imaging (fMRI) can be used to systematically detect neurofunctional differences between different classes of DOC, and whether these differences are related with a specific category of cognitive tasks (either active or passive), we meta-analyzed 22 neuroimaging studies published between 2005 and 2017 using the Activation Likelihood Estimate method. The results showed that: (1) active and passive tasks rely on well-segregated patterns of activations; (2) both unresponsive wakeful syndrome and patients in minimally conscious state activated a large portion of the dorsal-attentional network; (3) shared activations between patients fell mainly in the passive activation map (7492 voxels), while only 48 voxels fell in a subcortical region of the active-map. Our results suggest that DOCs can be described along a continuum—rather than as separated clinical categories—and characterised by a widespread dysfunction of brain networks rather than by the impairment of a well functionally anatomically defined one. [ABSTRACT FROM AUTHOR]