Decoding consciousness from different time-scale spatiotemporal dynamics in resting-state electroencephalogram

To explore the factors that affect the recovery of consciousness in patients with disorders of consciousness following brain trauma. We analyzed the data of 114 patients with disorders of consciousness following brain trauma admitted for rehabilitation. Bilateral logistic regression analysis was used to explore the factors that affected the recovery of the patients' consciousness. A logistic regression model was established and the ROC curve was drawn to obtain the optimal threshold of the prognostic model. Univariate analysis showed that vegetative state duration (P<0.001), CRS-R scores (P<0.001), hydrocephalus (P=0.037), hypertonia (P=0.034), central fever (P=0.035), paroxysmal sympathetic hyperactivity (PSH) (P=0.004), and epilepsy seizures were correlated with the recovery of consciousness. Logistic multivariate analysis showed that central fever (OR=3.493, P=0.044), vegetative state duration (OR=1.016, P=0.008), PSH (OR=4.223, P=0.034) and CRS-R scores (OR=0.640, P=0.002) all significantly affected the recovery of consciousness. The χ2 value of the Hosmer-Lemeshow test was 10.214 (P=0.250), and the goodness of fit of this model indicated an outstanding fitting (c=0.91). The presence of PSH is the one of the most important factor followed by centric fever to affect the outcome of patients with disorders of consciousness. A lower CRS-R score and a longer duration of vegetative state also predict a poor recovery of consciousness in these patients.

Very severe traumatic brain injury may cause disorders of consciousness in the form of coma, unresponsive wakefulness syndrome (also known as vegetative state) or minimally conscious state. Previous studies of outcome for these patients largely pre-date the 2002 definition of minimally conscious state. To establish the numbers of patients with disorder of consciousness at 3 weeks, 3 months and 1 year after severe traumatic brain injury, and to relate conscious state 3 weeks after injury to outcomes at 1 year. Multi-centre, prospective, observational study of severe traumatic brain injury. lowest (non-sedated) Glasgow Coma Scale 3-8 during the first 24 h; requirement for neurosurgical intensive care; age 18-65 years; alive 3 weeks after injury. Diagnosis of coma, unresponsive wakefulness syndrome, minimally conscious state or emerged from minimally conscious state was based on clinical and Coma Recovery Scale Revised assessments 3 weeks, 3 months and 1 year after injury. One-year outcome was measured with Glasgow Outcome Scale Extended (GOSE). A total of 103 patients was included in the study. Of these, 81% were followed up to 1 year (76% alive, 5% dead). Three weeks after injury 36 were in coma, unresponsive wakefulness syndrome or minimally conscious state and 11 were anaesthetized. Numbers of patients who had emerged from minimally conscious state 1 year after injury, according to status at 3 weeks were: coma (0/6), unresponsive wakefulness syndrome (9/17), minimally conscious state (13/13), anaesthetized (9/11). Outcome at 1 year was good (GOSE > 4) for half of patients in minimally conscious state or anaesthetized at 3 weeks, but for none of the patients in coma or unresponsive wakefulness syndrome. These differences in outcome were not revealed by prognostic predictions based on acute data. Patients in minimally conscious state or anaesthetized 3 weeks after injury have a better prognosis than patients in coma or unresponsive wakefulness syndrome, which could not be explained by acute prognostic models.

Chapter 13 - Disorders of Consciousness

Electroencephalography quadratic phase self-coupling correlates with consciousness states and restoration in patients with disorders of consciousness

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.

Low back pain (LBP) is a prevalent pain condition whose persistence can lead to changes in the brain regions responsible for sensory, cognitive, attentional, and emotional processing. Previous neuroimaging studies have identified various structural and functional abnormalities in patients with LBP; however, how the static and dynamic large-scale functional network connectivity (FNC) of the brain is affected in these patients remains unclear. Forty-one patients with chronic low back pain (cLBP) and 42 healthy controls underwent resting-state functional MRI scanning. The independent component analysis method was employed to extract the resting-state networks. Subsequently, we calculate and compare between groups for static intra- and inter-network functional connectivity. In addition, we investigated the differences between dynamic functional network connectivity and dynamic temporal metrics between cLBP patients and healthy controls. Finally, we tried to distinguish cLBP patients from healthy controls by support vector machine method. The results showed that significant reductions in functional connectivity within the network were found within the DMN,DAN, and ECN in cLBP patients. Significant between-group differences were also found in static FNC and in each state of dynamic FNC. In addition, in terms of dynamic temporal metrics, fraction time and mean dwell time were significantly altered in cLBP patients. In conclusion, our study suggests the existence of static and dynamic large-scale brain network alterations in patients with cLBP. The findings provide insights into the neural mechanisms underlying various brain function abnormalities and altered pain experiences in patients with cLBP.

ABSTRACTObjective: To obtain a CRS-R index suitable for diagnosis of patients with disorders of consciousness (DOC) and compare it to other CRS-R based scores to evaluate its potential for clinics and research.Design: We evaluated the diagnostic accuracy of several CRS-R-based scores in 124 patients with DOC. ROC analysis of the CRS-R total score, the Rasch-based CRS-R score, CRS-R-MS and the CRS-R index evaluated the diagnostic accuracy for patients with the Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS). Correlations were computed between the CRS-R-MS, CRS-R index, the Rasch-based score and the CRS-R total score.Results: Both the CRS-R-MS and CRS-R index ranged from 0 to 100, with a cut-off of 8.315 that perfectly distinguishes between patients with UWS and MCS. The CRS-R total score and Rasch-based score did not provide a cut-off score for patients with UWS and MCS. The proposed CRS-R index correlated with the CRS-R total score, Rasch-based score and the CRS-R-MS.Conclusion: The CRS-R index is reliable to diagnose patients with UWS and MCS and can be used in compliance with the CRS-R scoring guidelines. The obtained index offers the opportunity to improve the interpretation of clinical assessment and can be used in (longitudinal) research protocols.Abbreviations: CRS-R: Coma Recovery Scale-Revised; CRS-R-MS: Coma Recovery Scale-Revised Modified Score; DOC: Disorders of Consciousness; MCS: Minimally Conscious State; UWS: Unresponsive Wakefulness Syndrome; ROC: Receiver Operating Characteristic; AUC: Area Under the Curve; IRT: Item Response Theory.

The assessment of awareness in patients with chronic Disorders of Consciousness (DoC), including Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS), is challenging. The level of awareness impairment may depend on the degree of deterioration of the large-scale cortical-thalamo-cortical networks induced by brain injury. Electrophysiological approaches may shed light on awareness presence in patients with DoC by estimating cortical functions related to the cortical-thalamo-cortical networks including, for example, the cortico-subcortical processes generating motor responses to the perturbation of the peri-personal space (PPS). We measured the amplitude, latency, and duration of the hand-blink reflex (HBR) responses by recording electromyography (EMG) signals from both the orbicularis oculi muscles while electrically stimulating the median nerve at the wrist. Such a BR is thought to be mediated by a neural circuit at the brainstem level. Despite its defensive-response nature, HBR can be modulated by the distance between the stimulated hand and the face. This suggests a functional top-down control of HBR as reflected by HBR features changes (latency, amplitude, and magnitude). We therefore estimated HBR responses in a sample of patients with DoC (8 MCS and 12 UWS, compared to 15 healthy controls −HC) while performing a motor task targeting the PPS. This consisted of passive movements in which the hand of the subject was positioned at different distances from the participant’s face. We aimed at demonstrating a residual top-down modulation of HBR properties, which could be useful to differentiate patients with DoC and, potentially, demonstrate awareness preservation. We found a decrease in latency, and an increase in duration and magnitude of HBR responses, which were all inversely related to the hand-to-face distance in HC and patients with MCS, but not in individuals with UWS. Our data suggest that only patients with MCS have preserved, residual, top-down modulation of the processes related to the PPS from higher-order cortical areas to sensory-motor integration network. Although the sample size was relatively small, being thus our data preliminary, HBR assessment seems a rapid, easy, and first-level tool to differentiate patients with MCS from those with UWS. We may also hypothesize that such a HBR modulation suggests awareness preservation.

The objective of this study was to investigate the value of mismatch negativity (MMN) and P300 event-related potentials for discriminating the consciousness state and predicting improvement of consciousness at 6months in patients with coma and other disorders of consciousness (DOC). We performed MMN and P300 on 42 patients with DOC with a mean onset time of 40.21 ± 19.43days. These patients with DOC were categorized into coma, unresponsive wakefulness syndrome (UWS), minimal consciousness minus (MCS-), and minimal consciousness plus (MCS +) groups according to neurobehavioral assessment and the Coma Recovery Scale-Revised score. The primary outcome was the improvement of consciousness at 6months in patients with DOC. We assessed the efficacy of MMN and P300 in quantitatively predicting the prognosis at 6months and the capability of MMN and P300 parameters to differentiate between DOC. At least one significant difference in either MMN or P300 parameters was displayed among the DOC groups, but not between the MCS- and MCS+ groups (significance level: 0.05). Both MMN and P300 amplitudes showed desirable predictive accuracy at 6months, with areas under the curve (AUCs) of 0.859 and 0.856, respectively. The optimal thresholds for MMN and P300 amplitudes were 2.044 and 1.095μV. However, the combined MMN-P300 amplitude showed better 6-month predictive accuracy (AUC 0.934, 95% confidence interval 0.860-1.000), with a sensitivity of 85% and a specificity of 90.9%. MMN and P300 may help discriminate among coma, UWS, and MCS, but not between patients with MCS- and patients with MCS+ . The MMN amplitude, P300 amplitude, and especially combined MMN-P300 amplitude at 6months may be interesting predictors of consciousness improvement at 6months in patients with DOC. Chinese Clinical Trial Registry identifier ChiCTR2400083798.

Cerebral small vessel disease (CSVD) is a common disease that seriously endangers people’s health, and is easily overlooked by both patients and clinicians due to its near-silent onset. Dynamic functional connectivity (DFC) is a new concept focusing on the dynamic features and patterns of brain networks that represents a powerful tool for gaining novel insight into neurological diseases. To assess alterations in DFC in CSVD patients, and the correlation of DFC with cognitive function. We enrolled 35 CSVD patients and 31 normal control subjects (NC). Resting-state functional MRI (rs-fMRI) with a sliding-window approach and k-means clustering based on independent component analysis (ICA) was used to evaluate DFC. The temporal properties of fractional windows and the mean dwell time in each state, as well as the number of transitions between each pair of DFC states, were calculated. Additionally, we assessed the functional connectivity (FC) strength of the dynamic states and the associations of altered neuroimaging measures with cognitive performance. A dynamic analysis of all included subjects suggested four distinct functional connectivity states. Compared with the NC group, the CSVD group had more fractional windows and longer mean dwell times in state 4 characterized by sparse FC both inter-network and intra-networks. Additionally, the CSVD group had a reduced number of windows and shorter mean dwell times compared to the NC group in state 3 characterized by highly positive FC between the somatomotor and visual networks, and negative FC in the basal ganglia and somatomotor and visual networks. The number of transitions between state 2 and state 3 and between state 3 and state 4 was significantly reduced in the CSVD group compared to the NC group. Moreover, there was a significant difference in the FC strength between the two groups, and the altered temporal properties of DFC were significantly related to cognitive performance. Our study indicated that CSVD is characterized by altered temporal properties in DFC that may be sensitive neuroimaging biomarkers for early disease identification. Further study of DFC alterations could help us to better understand the progressive dysfunction of networks in CSVD patients.

This study explored the value of nonlinear features extracted from EEG signals to facilitate the assessment of patients with disorders of consciousness (DOC) with limited communication capacity. We utilized a dataset comprising 104 participants, 56 with vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and 48 in a minimally conscious state (MCS). For each participant, we computed channel-wise approximate entropy (ApEn) from EEG time-series data using a sliding window approach under two experimental paradigms: resting state and preferred music stimulation. These nonlinear measures were then spatially interpolated to generate topographical maps. Both resting state and preferred music stimulation data were processed as 1-second epochs using identical convolutional neural networks (CNN) architectures. The classification performance and validity of the CNN were compared against support vector machine (SVM) and generalized regression neural network (GRNN) models. ApEn in the resting state and under stimulation with preferred music correlated with the Coma Recovery Scale-Revised scores in patients with DOC, showing varied regional responses. Notably, the CNNs resulted in a positive diagnostic performance with an accuracy of 90.00% and an AUC of 0.902. The CNN was better than the SVM and GRNN in differentiating between the VS/UWS and MCS states. This study offers a convenient and accurate method for detecting awareness in patients with VS/UWS and MCS using ApEn features in the resting state and under preferred music stimulation using deep learning.

The evaluation of the level of consciousness in patients with disorders of consciousness (DOC) is primarily based on behavioural assessments. Patients with unresponsive wakefulness syndrome (UWS) do not show any sign of awareness of their environment, while minimally conscious state (MCS) patients show reproducible but fluctuating signs of awareness. Some patients, although with remaining cognitive abilities, are not able to exhibit overt voluntary responses at the bedside and may be misdiagnosed as UWS. Several studies investigated functional neuroimaging and neurophysiology as an additional tool to evaluate the level of consciousness and to detect covert command following in DOC. Most of these studies are based on auditory stimulation, neglecting patients suffering from decreased or absent hearing abilities. In the present study, we aim to assess the response to a P3-based paradigm in 40 patients with DOC and 12 healthy participants using auditory (AEP) and vibrotactile (VTP) stimulation. To this end, an EEG-based brain-computer interface was used at DOC patient’s bedside. We compared the significance of the P3 performance (i.e., the interpretation of significance of the evoked P3 response) as obtained by ‘direct processing’ (i.e., theoretical-based significance threshold) and ‘offline processing’ (i.e., permutation-based single subject level threshold). We evaluated whether the P3 performances were dependent on clinical variables such as diagnosis (UWS and MCS), aetiology and time since injury. Last we tested the dependency of AEP and VTP performances at the single subject level. Direct processing tends to overestimate P3 performance. We did not find any difference in the presence of a P3 performance according to the level of consciousness (UWS vs. MCS) or the aetiology (traumatic vs. non-traumatic brain injury). The performance achieved at the AEP paradigm was independent from what was achieved at the VTP paradigm, indicating that some patients performed better on the AEP task while others performed better on the VTP task. Our results support the importance of using multimodal approaches in the assessment of DOC patients in order to optimise the evaluation of patient’s abilities.

Background: Disorders of consciousness are challenging to diagnose, with inconsistent behavioral responses, motor and cognitive disabilities, leading to approximately 40% misdiagnoses. Heart rate variability (HRV) reflects the complexity of the heart-brain two-way dynamic interactions. HRV entropy analysis quantifies the unpredictability and complexity of the heart rate beats intervals. We here investigate the complexity index (CI), a score of HRV complexity by aggregating the non-linear multi-scale entropies over a range of time scales, and its discriminative power in chronic patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), and its relation to brain functional connectivity. Methods: We investigated the CI in short (CIs) and long (CIl) time scales in 14 UWS and 16 MCS sedated. CI for MCS and UWS groups were compared using a Mann-Whitney exact test. Spearman's correlation tests were conducted between the Coma Recovery Scale-revised (CRS-R) and both CI. Discriminative power of both CI was assessed with One-R machine learning model. Correlation between CI and brain connectivity (detected with functional magnetic resonance imagery using seed-based and hypothesis-free intrinsic connectivity) was investigated using a linear regression in a subgroup of 10 UWS and 11 MCS patients with sufficient image quality. Results: Higher CIs and CIl values were observed in MCS compared to UWS. Positive correlations were found between CRS-R and both CI. The One-R classifier selected CIl as the best discriminator between UWS and MCS with 90% accuracy, 7% false positive and 13% false negative rates after a 10-fold cross-validation test. Positive correlations were observed between both CI and the recovery of functional connectivity of brain areas belonging to the central autonomic networks (CAN). Conclusion: CI of MCS compared to UWS patients has high discriminative power and low false negative rate at one third of the estimated human assessors' misdiagnosis, providing an easy, inexpensive and non-invasive diagnostic tool. CI reflects functional connectivity changes in the CAN, suggesting that CI can provide an indirect way to screen and monitor connectivity changes in this neural system. Future studies should assess the extent of CI's predictive power in a larger cohort of patients and prognostic power in acute patients.

IntroductionBehavioral and cerebral dissociation has been now clearly established in some patients with acquired disorders of consciousness (DoC). Altogether, these studies mainly focused on the preservation of high-level cognitive markers in prolonged DoC, but did not specifically investigate lower but key-cognitive functions to consciousness emergence, such as the ability to take a first-person perspective, notably at the acute stage of coma. We made the hypothesis that the preservation of self-recognition (i) is independent of the behavioral impairment of consciousness, and (ii) can reflect the ability to recover consciousness.MethodsHence, using bedside Electroencephalography (EEG) recordings, we acquired, in a large cohort of 129 severely brain damaged patients, the brain response to the passive listening of the subject’s own name (SON) and unfamiliar other first names (OFN). One hundred and twelve of them (mean age ± SD = 46 ± 18.3 years, sex ratio M/F: 71/41) could be analyzed for the detection of an individual and significant discriminative P3 event-related brain response to the SON as compared to OFN (‘SON effect’, primary endpoint assessed by temporal clustering permutation tests).ResultsPatients were either coma (n = 38), unresponsive wakefulness syndrome (UWS, n = 30) or minimally conscious state (MCS, n = 44), according to the revised version of the Coma Recovery Scale (CRS-R). Overall, 33 DoC patients (29%) evoked a ‘SON effect’. This electrophysiological index was similar between coma (29%), MCS (23%) and UWS (34%) patients (p = 0.61). MCS patients at the time of enrolment were more likely to emerged from MCS (EMCS) at 6 months than coma and UWS patients (p = 0.013 for comparison between groups). Among the 72 survivors’ patients with event-related responses recorded within 3 months after brain injury, 75% of the 16 patients with a SON effect were EMCS at 6 months, while 59% of the 56 patients without a SON effect evolved to this favorable behavioral outcome.DiscussionAbout 30% of severely brain-damaged patients suffering from DoC are capable to process salient self-referential auditory stimuli, even in case of absence of behavioral detection of self-conscious processing. We suggest that self-recognition covert brain ability could be an index of consciousness recovery, and thus could help to predict good outcome.

Disorders of consciousness (DOC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), are complex brain dysfunctions with various causes. Misdiagnosis is common when relying solely on neurological exams, highlighting the need for accurate differentiation to guide clinical rehabilitation. This study explores metabolomic differences between UWS and MCS across cerebrospinal fluid (CSF), serum, and urine samples to identify biomarkers and metabolic pathways. Fifty-one subjects were categorized into UWS (n = 35) and MCS (n = 16) based on coma recovery scale-revised (CRS-R) scores. Ultraperformance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was used to analyze samples, and statistical methods identified 14, 24, and 22 differential metabolites in CSF, serum, and urine, respectively. CSF metabolites were linked to necrosis, apoptosis, and neuroprotection; serum metabolites to lipid metabolism and immune response; and urine metabolites to cell signaling and neural function. Metabolomic panels showed AUC values of 0.85 (95% CI: 0.73-0.96) for CSF (95% CI: 0.86-1.00), 0.94 for serum, and 0.93 (95% CI: 0.79-1.00) for urine in distinguishing UWS from MCS. This profiling offers valuable insights into DOC pathophysiology and aids in accurate differentiation of these states.