Narrative Review: Quantitative EEG in Disorders of Consciousness.
Ontology highlight
ABSTRACT:
SUBMITTER: Wutzl B
PROVIDER: S-EPMC8228474 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Background/objectiveBehavioral examinations may fail to detect language function in patients with severe traumatic brain injury (TBI) due to confounds such as having an endotracheal tube. We investigated whether resting and stimulus-evoked electroencephalography (EEG) methods detect the presence of language function in patients with severe TBI.MethodsFour EEG measures were assessed: (1) resting background (applying Forgacs' criteria), (2) reactivity to speech, (3) background and reactivity (applying Synek's criteria); and (4) an automated support vector machine (classifier for speech versus rest). Cohen's kappa measured agreement between the four EEG measures and evidence of language function on a behavioral coma recovery scale-revised (CRS-R) and composite (CRS-R or functional MRI) reference standard. Sensitivity and specificity of each EEG measure were calculated against the reference standards.ResultsWe enrolled 17 adult patients with severe TBI (mean ± SD age 27.0 ± 7.0 years; median [range] 11.5 [2-1173] days post-injury) and 16 healthy subjects (age 28.5 ± 7.8 years). The classifier, followed by Forgacs' criteria for resting background, demonstrated the highest agreement with the behavioral reference standard. Only Synek's criteria for background and reactivity showed significant agreement with the composite reference standard. The classifier and resting background showed balanced sensitivity and specificity for behavioral (sensitivity = 84.6% and 80.8%; specificity = 57.1% for both) and composite reference standards (sensitivity = 79.3% and 75.9%, specificity = 50% for both).ConclusionsMethods applying an automated classifier, resting background, or resting background with reactivity may identify severe TBI patients with preserved language function. Automated classifier methods may enable unbiased and efficient assessment of larger populations or serial timepoints, while qualitative visual methods may be practical in community settings.
| S-EPMC7373666 | biostudies-literature
Project description:Providing an accurate prognosis for prolonged disorder of consciousness (pDOC) patients remains a clinical challenge. Large cross-sectional studies have demonstrated the diagnostic and prognostic value of functional brain networks measured using high-density electroencephalography (hdEEG). Nonetheless, the prognostic value of these neural measures has yet to be assessed by longitudinal follow-up. We address this gap by assessing the utility of hdEEG to prognosticate long-term behavioural outcome, employing longitudinal data collected from a cohort of patients assessed systematically with resting hdEEG and the Coma Recovery Scale-Revised (CRS-R) at the bedside over a period of two years. We used canonical correlation analysis to relate clinical (including CRS-R scores combined with demographic variables) and hdEEG variables to each other. This analysis revealed that the patient's age, and the hdEEG theta band power and alpha band connectivity, contributed most significantly to the relationship between hdEEG and clinical variables. Further, we found that hdEEG measures recorded at the time of assessment augmented clinical measures in predicting CRS-R scores at the next assessment. Moreover, the rate of hdEEG change not only predicted later changes in CRS-R scores, but also outperformed clinical measures in terms of prognostic power. Together, these findings suggest that improvements in functional brain networks precede changes in behavioural awareness in pDOC. We demonstrate here that bedside hdEEG assessments conducted at specialist nursing homes are feasible, have clinical utility, and can complement clinical knowledge and systematic behavioural assessments to inform prognosis and care.
| S-EPMC7426558 | biostudies-literature
Project description:Objective: Clinical assessment of auditory attention in patients with disorders of consciousness is often limited by motor impairment. Here, we employ intersubject correlations among electroencephalography responses to naturalistic speech in order to assay auditory attention among patients and healthy controls.Methods: Electroencephalographic data were recorded from 20 subjects with disorders of consciousness and 14 healthy controls during of two narrative audio stimuli, presented both forwards and time-reversed. Intersubject correlation of evoked electroencephalography signals were calculated, comparing responses of both groups to those of the healthy control subjects. This analysis was performed blinded and subsequently compared to the diagnostic status of each patient based on the Coma Recovery Scale-Revised.Results: Subjects with disorders of consciousness exhibit significantly lower intersubject correlation than healthy controls during narrative speech. Additionally, while healthy subjects had higher intersubject correlation values in forwards versus backwards presentation, neural responses did not vary significantly with the direction of playback in subjects with disorders of consciousness. Increased intersubject correlation values in the backward speech condition were noted with improving disorder of consciousness diagnosis, both in cross-sectional analysis and in a subset of patients with longitudinal data.Interpretation: Intersubject correlation of neural responses to narrative speech audition differentiates healthy controls from patients and appears to index clinical diagnoses in disorders of consciousness.
| S-EPMC5682119 | biostudies-literature
Project description:Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks. Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n = 6), MCS+ (n = 29), MCS- (n = 17) and UWS (n = 9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate their relationship with decreasing levels of consciousness as well as group differences between healthy volunteers and DOC patient groups. Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness.
| S-EPMC6503216 | biostudies-literature
Project description:BackgroundDisorders of Consciousness (DoC) are clinical conditions following a severe acquired brain injury (ABI) characterized by absent or reduced awareness, known as coma, Vegetative State (VS)/Unresponsive Wakefulness Syndrome (VS/UWS), and Minimally Conscious State (MCS). Misdiagnosis rate between VS/UWS and MCS is attested around 40% due to the clinical and behavioral fluctuations of the patients during bedside consciousness assessments. Given the large body of evidence that some patients with DoC possess "covert" awareness, revealed by neuroimaging and neurophysiological techniques, they are candidates for intervention with brain-computer interfaces (BCIs).ObjectivesThe aims of the present work are (i) to describe the characteristics of BCI systems based on electroencephalography (EEG) performed on DoC patients, in terms of control signals adopted to control the system, characteristics of the paradigm implemented, classification algorithms and applications (ii) to evaluate the performance of DoC patients with BCI.MethodsThe search was conducted on Pubmed, Web of Science, Scopus and Google Scholar. The PRISMA guidelines were followed in order to collect papers published in english, testing a BCI and including at least one DoC patient.ResultsAmong the 527 papers identified with the first run of the search, 27 papers were included in the systematic review. Characteristics of the sample of participants, behavioral assessment, control signals employed to control the BCI, the classification algorithms, the characteristics of the paradigm, the applications and performance of BCI were the data extracted from the study. Control signals employed to operate the BCI were: P300 (N = 19), P300 and Steady-State Visual Evoked Potentials (SSVEP; hybrid system, N = 4), sensorimotor rhythms (SMRs; N = 5) and brain rhythms elicited by an emotional task (N = 1), while assessment, communication, prognosis, and rehabilitation were the possible applications of BCI in DoC patients.ConclusionDespite the BCI is a promising tool in the management of DoC patients, supporting diagnosis and prognosis evaluation, results are still preliminary, and no definitive conclusions may be drawn; even though neurophysiological methods, such as BCI, are more sensitive to covert cognition, it is suggested to adopt a multimodal approach and a repeated assessment strategy.
| S-EPMC9760911 | biostudies-literature
Project description:Neurophysiological markers can overcome the limitations of behavioural assessments of Disorders of Consciousness (DoC). EEG alpha power emerged as a promising marker for DoC, although long-standing literature reported alpha power being sustained during anesthetic-induced unconsciousness, and reduced during dreaming and hallucinations. We hypothesized that EEG power suppression caused by severe anoxia could explain this conflict. Accordingly, we split DoC patients (n = 87) in postanoxic and non-postanoxic cohorts. Alpha power was suppressed only in severe postanoxia but failed to discriminate un/consciousness in other aetiologies. Furthermore, it did not generalize to an independent reference dataset (n = 65) of neurotypical, neurological, and anesthesia conditions. We then investigated EEG spatio-spectral gradients, reflecting anteriorization and slowing, as alternative markers. In non-postanoxic DoC, these features, combined in a bivariate model, reliably stratified patients and indexed consciousness, even in unresponsive patients identified as conscious by an independent neural marker (the Perturbational Complexity Index). Crucially, this model optimally generalized to the reference dataset. Overall, alpha power does not index consciousness; rather, its suppression entails diffuse cortical damage, in postanoxic patients. As an alternative, EEG spatio-spectral gradients, reflecting distinct pathophysiological mechanisms, jointly provide a robust, parsimonious, and generalizable marker of consciousness, whose clinical application may guide rehabilitation efforts.
| S-EPMC10233271 | biostudies-literature
Project description:IntroductionFunctional brain-imaging techniques have revealed that clinical examination of disorders of consciousness (DoC) can underestimate the conscious level of patients. FDG-PET metabolic index of the best preserved hemisphere (MIBH) has been reported as a promising measure of consciousness but has never been externally validated and compared with other brain-imaging diagnostic procedures such as quantitative EEG.MethodsFDG-PET, quantitative EEG and cognitive evoked potential using an auditory oddball paradigm were performed in minimally conscious state (MCS) and vegetative state (VS) patient. We compared out-sample diagnostic and prognostic performances of PET-MIBH and EEG-based classification of conscious state to the current behavioral gold-standard, the Coma Recovery Scale - revised (CRS-R).ResultsBetween January 2016 and October 2019, 52 patients were included: 21 VS and 31 MCS. PET-MIBH had an AUC of 0.821 [0.694-0.930], sensitivity of 79% [62-91] and specificity of 78% [56-93], not significantly different from EEG (p = 0.628). Their combination accurately identified almost all MCS patients with a sensitivity of 94% [79-99%] and specificity of 67% [43-85]. Multimodal assessment also identified VS patients with neural correlate of consciousness (4/7 (57%) vs. 1/14 (7%), p = 0.025) and patients with 6-month recovery of command-following (9/24 (38%) vs. 0/16 (0%), p = 0.006), outperforming each technique taken in isolation.ConclusionFDG-PET MIBH is an accurate and robust procedure across sites to diagnose MCS. Its combination with EEG-based classification of conscious state not only optimizes diagnostic performances but also allows to detect covert cognition and to predict 6-month command-following recovery demonstrating the added value of multimodal assessment of DoC.
| S-EPMC7921007 | biostudies-literature
Project description:Further development of an EEG based communication device for patients with disorders of consciousness (DoC) could benefit from addressing the following gaps in knowledge-first, an evaluation of different types of motor imagery; second, an evaluation of passive feet movement as a mean of an initial classifier setup; and third, rapid delivery of biased feedback. To that end we investigated whether complex and/or familiar mental imagery, passive, and attempted feet movement can be reliably detected in patients with DoC using EEG recordings, aiming to provide them with a means of communication. Six patients in a minimally conscious state (MCS) took part in this study. The patients were verbally instructed to perform different mental imagery tasks (sport, navigation), as well as attempted feet movements, to induce distinctive event-related (de)synchronization (ERD/S) patterns in the EEG. Offline classification accuracies above chance level were reached in all three tasks (i.e., attempted feet, sport, and navigation), with motor tasks yielding significant (p < 0.05) results more often than navigation (sport: 10 out of 18 sessions; attempted feet: 7 out of 14 sessions; navigation: 4 out of 12 sessions). The passive feet movements, evaluated in one patient, yielded mixed results: whereas time-frequency analysis revealed task-related EEG changes over neurophysiological plausible cortical areas, the classification results were not significant enough (p < 0.05) to setup an initial classifier for the detection of attempted movements. Concluding, the results presented in this study are consistent with the current state of the art in similar studies, to which we contributed by comparing different types of mental tasks, notably complex motor imagery and attempted feet movements, within patients. Furthermore, we explored new venues, such as an evaluation of passive feet movement as a mean of an initial classifier setup, and rapid delivery of biased feedback.
| S-EPMC4264500 | biostudies-literature
Project description:Sleep disorders are among the main comorbidities in patients with a Disorder of Consciousness (DOC). Given the key role of sleep in neural and cognitive functioning, detecting and treating sleep disorders in DOCs might be an effective therapeutic strategy to boost consciousness recovery and levels of awareness. To date, no systematic reviews have been conducted that explore the effect of sleep treatments in DOCs; thus, we systematically reviewed the existing studies on both pharmacological and non-pharmacological treatments for sleep disorders in DOCs. Among 2267 assessed articles, only 7 were included in the systematic review. The studies focused on two sleep disorder categories (sleep-related breathing disorders and circadian rhythm dysregulation) treated with both pharmacological (Modafinil and Intrathecal Baclofen) and non-pharmacological (positive airway pressure, bright light stimulation, and central thalamic deep brain stimulation) interventions. Although the limited number of studies and their heterogeneity do not allow generalized conclusions, all the studies highlighted the effectiveness of treatments on both sleep disorders and levels of awareness. For this reason, clinical and diagnostic evaluations able to detect sleep disorders in DOC patients should be adopted in the clinical routine for the purpose of intervening promptly with the most appropriate treatment.
| S-EPMC8775271 | biostudies-literature
Project description:Over the last 20 years, studies have provided greater insight into disorders of consciousness (DOC), also known as altered state of consciousness. Increased brain residual functions have been identified in patients with DOC due to the successful application of novel next-generation imaging technologies. Many unconscious patients have now been confirmed to retain considerable cognitive functions. It is hoped that greater insight regarding the psychological state of patients may be achieved through the use of functional magnetic resonance imaging and brain-computer interfaces. However, issues surrounding the research and treatment of DOC remain problematic. These include differing opinions on the definition of consciousness, difficulties in diagnosis, assessment, prognosis and/or treatment, and newly emerging ethical, legal and social issues. To overcome these, appropriate care must be offered to patients with DOC by clinicians and families, as DOC patients may now be considered to live in more than just a vegetative state. The present article reviews the controversy surrounding the definition of consciousness and the reliability of novel technologies, prognostic prediction, communication with DOC patients and treatment methods. The ethical and social issues surrounding the treatment of DOC and future perspectives are also considered.
| S-EPMC5526117 | biostudies-other