Precise detection of awareness in disorders of consciousness using deep learning framework.

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

Early Intraventricular Antibiotic Therapy Improved In-Hospital-Mortality in Neurocritical Patients with Multidrug-Resistant Bacterial Nosocomial Meningitis and Ventriculitis.

BACKGROUND: Hospital-acquired multidrug-resistant (MDR) bacterial meningitis and/or ventriculitis (MEN) is a severe condition associated with high mortality. The risk factors related to in-hospital mortality of patients with MDR bacterial MEN are unknown. We aimed to examine factors related to in-hospital mortality and evaluate their prognostic value in patients with MDR bacterial MEN treated in the neurointensive care unit. METHODS: This was a single-center retrospective cohort study of critically ill neurosurgical patients with MDR bacterial MEN admitted to our hospital between January 2003 and March 2021. Data on demographics, admission variables, treatment, time to start of intraventricular (IVT) therapy, and in-hospital mortality were analyzed. Both univariate and multivariable analyses were performed to identify determinants of in-hospital mortality. RESULTS: All 142 included patients received systemic antibiotic therapy, and 102 of them received concomitant IVT treatment. The median time to start of IVT treatment was 2 days (interquartile range 1-5 days). The time to start of IVT treatment had an effect on in-hospital mortality (hazard ratio 1.17; 95% confidence interval 1.02-1.34; adjusted p = 0.030). The cutoff time to initiate IVT treatment was identified at 3 days: patients treated within 3 days had a higher cerebrospinal fluid (CSF) sterilization rate (81.5%) and a shorter median time to CSF sterilization (7 days) compared with patients who received delayed IVT treatment (> 3 days) (48.6% and 11.5 days, respectively) and those who received intravenous antibiotics alone (42.5% and 10 days, respectively). CONCLUSIONS: Early IVT antibiotics were associated with superior outcomes in terms of the in-hospital mortality rate, time to CSF sterilization, and CSF sterilization rate compared with delayed IVT antibiotics and intravenous antibiotics alone.

Disrupted relationship between intrinsic neural timescales and alpha peak frequency during unconscious states - A high-density EEG study.

Our brain processes the different timescales of our environment's temporal input stochastics. Is such a temporal input processing mechanism key for consciousness? To address this research question, we calculated measures of input processing on shorter (alpha peak frequency, APF) and longer (autocorrelation window, ACW) timescales on resting-state high-density EEG (256 channels) recordings and compared them across different consciousness levels (awake/conscious, ketamine and sevoflurane anaesthesia, unresponsive wakefulness, minimally conscious state). We replicate and extend previous findings of: (i) significantly longer ACW values, consistently over all states of unconsciousness, as measured with ACW-0 (an unprecedented longer version of the well-know ACW-50); (ii) significantly slower APF values, as measured with frequency sliding, in all four unconscious states. Most importantly, we report a highly significant correlation of ACW-0 and APF in the conscious state, while their relationship is disrupted in the unconscious states. In sum, we demonstrate the relevance of the brain's capacity for input processing on shorter (APF) and longer (ACW) timescales - including their relationship - for consciousness. Albeit indirectly, e.g., through the analysis of electrophysiological activity at rest, this supports the mechanism of temporo-spatial alignment to the environment's temporal input stochastics, through relating different neural timescales, as one key predisposing factor of consciousness.

Identifying patients with cognitive motor dissociation using resting-state temporal stability.

Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness.

Harmonic viscoelastic response of 3D histology-informed white matter model.

White matter (WM) consists of bundles of long axons embedded in a glial matrix, which lead to anisotropic mechanical properties of brain tissue, and this complicates direct numerical simulations of WM viscoelastic response. The detailed axonal geometry contains scales that range from axonal diameter (microscale) to many diameters (mesoscale) imposing an additional challenge to numerical simulations. Here we describe the development of a 3D homogenization model for the central nervous system (CNS) that accounts for the anisotropy introduced by the axon/neuroglia composite, the axonal trace curvature, and the tissue dynamic response in the frequency domain. Homogenized models that allow the incorporation of all the above factors are important for accurately simulating the tissue's mechanical behavior, and this in turn is essential in interpreting non-invasive elastography measurements. Geometric and material parameters affect the material properties and thus the response of the brain tissue. More complex, orthotropic, or anisotropic material properties are to be considered as necessitated by the 3D tissue structure. An assembly of micro-scale 3D representative elemental volumes (REVs) is constructed, leading to an integrated mesoscale WM finite element model. Assemblies of microscopic REVs, with orientations based on geometrical reconstructions driven by confocal microscopy data are employed to form the elements of the WM model. Each REV carries local material properties based on a finite element model of biphasic (axon-glial matrix) unidirectional composite. The viscoelastic response of the microscopic REVs is extracted based on geometric information and fiber volume fractions calculated from the relative distance between the local elements and global axonal trace. The response of the WM tissue model is homogenized by averaging the shear moduli over the total volume (thus deriving effective properties) under realistic external loading conditions. Under harmonic shear loading, it is proven that that the effective transverse shear moduli are higher than the axial moduli when the axon moduli are higher than the glial. Methodologically, the process of using micro-scale 3D REVs to describe more complex axon geometries avoids the partition process in traditional composite finite element methods (based on partition of finite element grids) and constitutes a robust algorithm to automatically build a WM model based on available axonal trace information. Analytically, the model provides unmatched simulation flexibility and computational power as the position, orientation, and the magnitude of each tissue building block is calculated using real tissue data, as are the training and testing processes at each level of the multiscale WM tissue.

Probing Intrinsic Neural Timescales in EEG with an Information-Theory Inspired Approach: Permutation Entropy Time Delay Estimation (PE-TD).

Time delays are a signature of many physical systems, including the brain, and considerably shape their dynamics; moreover, they play a key role in consciousness, as postulated by the temporo-spatial theory of consciousness (TTC). However, they are often not known a priori and need to be estimated from time series. In this study, we propose the use of permutation entropy (PE) to estimate time delays from neural time series as a more robust alternative to the widely used autocorrelation window (ACW). In the first part, we demonstrate the validity of this approach on synthetic neural data, and we show its resistance to regimes of nonstationarity in time series. Mirroring yet another example of comparable behavior between different nonlinear systems, permutation entropy-time delay estimation (PE-TD) is also able to measure intrinsic neural timescales (INTs) (temporal windows of neural activity at rest) from hd-EEG human data; additionally, this replication extends to the abnormal prolongation of INT values in disorders of consciousness (DoCs). Surprisingly, the correlation between ACW-0 and PE-TD decreases in a state-dependent manner when consciousness is lost, hinting at potential different regimes of nonstationarity and nonlinearity in conscious/unconscious states, consistent with many current theoretical frameworks on consciousness. In summary, we demonstrate the validity of PE-TD as a tool to extract relevant time scales from neural data; furthermore, given the divergence between ACW and PE-TD specific to DoC subjects, we hint at its potential use for the characterization of conscious states.

Higher-order sensorimotor circuit of the brain's global network supports human consciousness.

Consciousness is a mental characteristic of the human mind, whose exact neural features remain unclear. We aimed to identify the critical nodes within the brain's global functional network that support consciousness. To that end, we collected a large fMRI resting state dataset with subjects in at least one of the following three consciousness states: preserved (including the healthy awake state, and patients with a brain injury history (BI) that is fully conscious), reduced (including the N1-sleep state, and minimally conscious state), and lost (including the N3-sleep state, anesthesia, and unresponsive wakefulness state). We also included a unique dataset of subjects in rapid eye movement sleep state (REM-sleep) to test for the presence of consciousness with minimum movements and sensory input. To identify critical nodes, i.e., hubs, within the brain's global functional network, we used a graph-theoretical measure of degree centrality conjoined with ROI-based functional connectivity. Using these methods, we identified various higher-order sensory and motor regions including the supplementary motor area, bilateral supramarginal gyrus (part of inferior parietal lobule), supragenual/dorsal anterior cingulate cortex, and left middle temporal gyrus, that could be important hubs whose degree centrality was significantly reduced when consciousness was reduced or absent. Additionally, we identified a sensorimotor circuit, in which the functional connectivity among these regions was significantly correlated with levels of consciousness across the different groups, and remained present in the REM-sleep group. Taken together, we demonstrated that regions forming a higher-order sensorimotor integration circuit are involved in supporting consciousness within the brain's global functional network. That offers novel and more mechanism-guided treatment targets for disorders of consciousness.

Are intrinsic neural timescales related to sensory processing? Evidence from abnormal behavioral states.

The brain exhibits a complex temporal structure which translates into a hierarchy of distinct neural timescales. An open question is how these intrinsic timescales are related to sensory or motor information processing and whether these dynamics have common patterns in different behavioral states. We address these questions by investigating the brain's intrinsic timescales in healthy controls, motor (amyotrophic lateral sclerosis, locked-in syndrome), sensory (anesthesia, unresponsive wakefulness syndrome), and progressive reduction of sensory processing (from awake states over N1, N2, N3). We employed a combination of measures from EEG resting-state data: auto-correlation window (ACW), power spectral density (PSD), and power-law exponent (PLE). Prolonged neural timescales accompanied by a shift towards slower frequencies were observed in the conditions with sensory deficits, but not in conditions with motor deficits. Our results establish that the spontaneous activity's intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain.

Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI.

Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differences between DOC patients and healthy controls. Although substantial progress has been made towards this endeavor, the identification of robust rs-fMRI-based biomarkers for level of consciousness is still lacking. Recent developments in machine learning show promise as a tool to augment the discrimination between different states of consciousness in clinical practice. Here, we investigated whether machine learning models trained to make a binary distinction between conscious wakefulness and anesthetic-induced unconsciousness would then be capable of reliably identifying pathologically induced unconsciousness. We did so by extracting rs-fMRI-based features associated with local activity, regional homogeneity, and interregional functional activity in 44 subjects during wakefulness, light sedation, and unresponsiveness (deep sedation and general anesthesia), and subsequently using those features to train three distinct candidate machine learning classifiers: support vector machine, Extra Trees, artificial neural network. First, we show that all three classifiers achieve reliable performance within-dataset (via nested cross-validation), with a mean area under the receiver operating characteristic curve (AUC) of 0.95, 0.92, and 0.94, respectively. Additionally, we observed comparable cross-dataset performance (making predictions on the DOC data) as the anesthesia-trained classifiers demonstrated a consistent ability to discriminate between unresponsive wakefulness syndrome (UWS/VS) patients and healthy controls with mean AUC's of 0.99, 0.94, 0.98, respectively. Lastly, we explored the potential of applying the aforementioned classifiers towards discriminating intermediate states of consciousness, specifically, subjects under light anesthetic sedation and patients diagnosed as having a minimally conscious state (MCS). Our findings demonstrate that machine learning classifiers trained on rs-fMRI features derived from participants under anesthesia have potential to aid the discrimination between degrees of pathological unconsciousness in clinical patients.

Altered Global Brain Signal during Physiologic, Pharmacologic, and Pathologic States of Unconsciousness in Humans and Rats.

BACKGROUND: Consciousness is supported by integrated brain activity across widespread functionally segregated networks. The functional magnetic resonance imaging-derived global brain signal is a candidate marker for a conscious state, and thus the authors hypothesized that unconsciousness would be accompanied by a loss of global temporal coordination, with specific patterns of decoupling between local regions and global activity differentiating among various unconscious states. METHODS: Functional magnetic resonance imaging global signals were studied in physiologic, pharmacologic, and pathologic states of unconsciousness in human natural sleep (n = 9), propofol anesthesia (humans, n = 14; male rats, n = 12), and neuropathological patients (n = 21). The global signal amplitude as well as the correlation between global signal and signals of local voxels were quantified. The former reflects the net strength of global temporal coordination, and the latter yields global signal topography. RESULTS: A profound reduction of global signal amplitude was seen consistently across the various unconscious states: wakefulness (median [1st, 3rd quartile], 0.46 [0.21, 0.50]) versus non-rapid eye movement stage 3 of sleep (0.30 [0.24, 0.32]; P = 0.035), wakefulness (0.36 [0.31, 0.42]) versus general anesthesia (0.25 [0.21, 0.28]; P = 0.001), healthy controls (0.30 [0.27, 0.37]) versus unresponsive wakefulness syndrome (0.22 [0.15, 0.24]; P < 0.001), and low dose (0.07 [0.06, 0.08]) versus high dose of propofol (0.04 [0.03, 0.05]; P = 0.028) in rats. Furthermore, non-rapid eye movement stage 3 of sleep was characterized by a decoupling of sensory and attention networks from the global network. General anesthesia and unresponsive wakefulness syndrome were characterized by a dissociation of the majority of functional networks from the global network. This decoupling, however, was dominated by distinct neuroanatomic foci (e.g., precuneus and anterior cingulate cortices). CONCLUSIONS: The global temporal coordination of various modules across the brain may distinguish the coarse-grained state of consciousness versus unconsciousness, while the relationship between the global and local signals may define the particular qualities of a particular unconscious state.

Admission circulating monocytes level is an independent predictor of outcome in traumatic brain injury.

OBJECTIVE: The aim of this study was to assess the prognostic value of admission immune cell levels in the peripheral blood in determining outcomes in patients with TBI. METHOD: We studied 141 adult patients with mild-to-severe TBI (Glasgow Coma Scale (GCS) 3-15). Patient outcome was assessed using the Extended Glasgow Outcome Scale (GOSE) at 6 months post-injury. The area under the curve (AUC) was used to evaluate the predictive ability of immune cell levels. Uni- and multivariate analyses were performed to assess the independent predictors of 6-month outcome. RESULTS: We found that admission monocyte count was not only a better predictor (AUC = 0.778; 95% confidence interval (CI), 0.679-0.858) of favourable outcomes (GOSE 5-8) at 6 months post-injury than were admission haemoglobin (AUC = 0.629; 95% CI, 0.522-0.728) and blood glucose (AUC = 0.616; 95% CI, 0.508-0.716) levels for patients with moderate-to-severe TBI (GCS ≤ 12), but also an independent predictor of 6-month outcome (adjusted odds ratio, 1.35; 95% CI, 1.10-1.65; p = 0.004). CONCLUSIONS: The present study suggests that an increase in admission monocyte count is correlated with a favourable 6-month outcome in patients with moderate-to-severe TBI.

Longitudinal recovery of local neuronal activity and consciousness level in acquired brain injury.

Decreased brain activity in the default mode network, particularly in the precuneus (PCU), has been consistently shown in acquired brain injury (ABI) patients. However, it is unclear whether resting-state brain activity recovers longitudinally in ABI patients and whether functional activity restoration is associated with improvements in consciousness level. Here, resting-state fMRI data were acquired from 23 ABI patients and 30 age- and gender-matched controls with two longitudinal observations for each participant. The fMRI data were analyzed using amplitude of low-frequency fluctuation (ALFF) to measure the fluctuation strength of local spontaneous activity, and seed-based functional connectivity was used to measure functional relationship with the seed region in the whole brain. The level of consciousness was assessed using the Glasgow Coma Scale (GCS) and Coma Recovery Scale-Revised (CRS-R) on both scanning days of the patients. Interaction effect between the two groups and two scans in ALFF was observed in the PCU, which was driven by restored ALFF in the ABI, while a stable ALFF in the control group. Moreover, restoration of ALFF in the PCU correlated with improvements in both the CRS-R and GCS. Specifically, recovery of ALFF in the PCU primarily reflected the signals of the slow-4 frequency band (0.027-0.073 Hz). Based on the functional connectivity maps of the PCU, we observed a nonsignificant interaction effect or correlation with consciousness level. These findings suggest local activity in the PCU but possibly not its functional connectivity, is related to the longitudinal changes in behavioral responsiveness in ABI. Hum Brain Mapp 38:3579-3591, 2017. © 2017 Wiley Periodicals, Inc.

Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury.

For accurate diagnosis and prognostic prediction of acquired brain injury (ABI), it is crucial to understand the neurobiological mechanisms underlying loss of consciousness. However, there is no consensus on which regions and networks act as biomarkers for consciousness level and recovery outcome in ABI. Using resting-state fMRI, we assessed intrinsic functional connectivity strength (FCS) of whole-brain networks in a large sample of 99 ABI patients with varying degrees of consciousness loss (including fully preserved consciousness state, minimally conscious state, unresponsive wakefulness syndrome/vegetative state, and coma) and 34 healthy control subjects. Consciousness level was evaluated using the Glasgow Coma Scale and Coma Recovery Scale-Revised on the day of fMRI scanning; recovery outcome was assessed using the Glasgow Outcome Scale 3 months after the fMRI scanning. One-way ANOVA of FCS, Spearman correlation analyses between FCS and the consciousness level and recovery outcome, and FCS-based multivariate pattern analysis were performed. We found decreased FCS with loss of consciousness primarily distributed in the posterior cingulate cortex/precuneus (PCC/PCU), medial prefrontal cortex, and lateral parietal cortex. The FCS values of these regions were significantly correlated with consciousness level and recovery outcome. Multivariate support vector machine discrimination analysis revealed that the FCS patterns predicted whether patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%, and the most discriminative region was the PCC/PCU. These findings suggest that intrinsic functional connectivity patterns of the human posteromedial cortex could serve as a potential indicator for consciousness level and recovery outcome in individuals with ABI. SIGNIFICANCE STATEMENT: Varying degrees of consciousness loss and recovery are commonly observed in acquired brain injury patients, yet the underlying neurobiological mechanisms remain elusive. Using a large sample of patients with varying degrees of consciousness loss, we demonstrate that intrinsic functional connectivity strength in many brain regions, especially in the posterior cingulate cortex and precuneus, significantly correlated with consciousness level and recovery outcome. We further demonstrate that the functional connectivity pattern of these regions can predict patients with unresponsive wakefulness syndrome/vegetative state and coma would regain consciousness with an accuracy of 81.25%. Our study thus provides potentially important biomarkers of acquired brain injury in clinical diagnosis, prediction of recovery outcome, and decision making for treatment strategies for patients with severe loss of consciousness.

Decoupled temporal variability and signal synchronization of spontaneous brain activity in loss of consciousness: An fMRI study in anesthesia.

Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which "couple" the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC.

How are different neural networks related to consciousness?

OBJECTIVE: We aimed to investigate the roles of different resting-state networks in predicting both the actual level of consciousness and its recovery in brain injury patients. METHODS: We investigated resting-state functional connectivity within different networks in patients with varying levels of consciousness: unresponsive wakefulness syndrome (UWS; n = 56), minimally conscious state (MCS; n = 29), and patients with brain lesions but full consciousness (BL; n = 48). Considering the actual level of consciousness, we compared the strength of network connectivity among the patient groups. We then checked the presence of connections between specific regions in individual patients and calculated the frequency of this in the different patient groups. Considering the recovery of consciousness, we split the UWS group into 2 subgroups according to recovery: those who emerged from UWS (UWS-E) and those who remained in UWS (UWS-R). The above analyses were repeated on these 2 subgroups. RESULTS: Functional connectivity strength in salience network (SN), especially connectivity between the supragenual anterior cingulate cortex (SACC) and left anterior insula (LAI), was reduced in the unconscious state (UWS) compared to the conscious state (MCS and BL). Moreover, at the individual level, SACC-LAI connectivity was more present in MCS than in UWS. Default-mode network (DMN) connectivity strength, especially between the posterior cingulate cortex (PCC) and left lateral parietal cortex (LLPC), was reduced in UWS-R compared with UWS-E. Furthermore, PCC-LLPC connectivity was more present in UWS-E than in UWS-R. INTERPRETATION: Our findings show that SN (SACC-LAI) connectivity correlates with behavioral signs of consciousness, whereas DMN (PCC-LLPC) connectivity instead predicts recovery of consciousness.

GABAA receptor deficits predict recovery in patients with disorders of consciousness: A preliminary multimodal [(11) C]Flumazenil PET and fMRI study.

OBJECTIVES: Disorders of consciousness (DoC)-that is, unresponsive wakefulness syndrome/vegetative state and minimally conscious state-are debilitating conditions for which no reliable markers of consciousness recovery have yet been identified. Evidence points to the GABAergic system being altered in DoC, making it a potential target as such a marker. EXPERIMENTAL DESIGN: In our preliminary study, we used [(11) C]Flumazenil positron emission tomography to establish global GABAA receptor binding potential values and the local-to-global (LTG) ratio of these for specific regions. These values were then compared between DoC patients and healthy controls. In addition, they were correlated with behavioral improvements for the patients between the time of scanning and 3 months later. Functional magnetic resonance imaging resting-state functional connectivity was also calculated and the same comparisons made. PRINCIPAL OBSERVATIONS: lobal GABAA receptor binding was reduced in DoC, as was the LTG ratio in specifically the supragenual anterior cingulate. Both of these measures correlated with behavioral improvement after 3 months. In contrast to these measures of GABAA receptor binding, functional connectivity did not correlate with behavioral improvement. CONCLUSIONS: Our preliminary findings point toward GABAA receptor binding being a marker of consciousness recovery in DoC.

Subdural hygroma following decompressive craniectomy or non-decompressive craniectomy in patients with traumatic brain injury: Clinical features and risk factors.

OBJECTIVE: Subdural hygroma (SDG) is a common complication that can occur after head trauma or secondary to decompressive craniectomy (DC). SDGs can be located not only ipsilateral or contralateral to the side of the DC, but also bilateral or unilateral in patients without DC. This study investigated the incidence and risk factors for different types of SDG in a large cohort of patients with traumatic brain injury (TBI). METHODS: A retrospective study was conducted involving 379 adult patients with TBI who were admitted to Huashan Hospital, Fudan University between January 2009 and December 2013. As the outcome was dichotomous (SDG vs no SDG or hydrocephalus vs no hydrocephalus), multivariate logistic regression analyses were used to identify independent risk factors for the development of SDGs in patients without DC, ipsilateral SDG after unilateral DC, contralateral SDG after unilateral DC or SDG after bilateral DC. Risk factors for the development of hydrocephalus were also evaluated in patients with and without DC. RESULTS: Among the 207 (54.6%) patients without DC, 30 (14.5%) had unilateral SDGs and 34 (16.4%) had bilateral SDGs. Of the 172 patients (45.4%) with DC, 134 (77.9%) underwent unilateral DC and 38 (22.1%) underwent bilateral DC. Of the 134 patients who underwent unilateral DC, 49 developed SDG, including 22 (16.4%) ipsilateral SDG, 19 (14.2%) contralateral SDG and eight (6.0%) both ipsilateral and contralateral SDGs. For patients undergoing bilateral DC, 13 (34.2%) developed a SDG. No significant difference in the incidence of SDG was observed between the patients with and without DC (36.0% vs 30.9%, p = 0.291), but the characteristics of SDGs were different between the two groups. Logistic regression analysis showed that factors independently associated with the development of SDG were male sex (odds ratio [OR] = 3.861; 95% CI = 1.642-9.091; p = 0.002), older age (OR = 1.046; 95% CI = 1.021-1.070; p < 0.001), basal cistern haemorrhage (OR = 4.608; 95% CI = 1.510-14.064; p = 0.007), diffuse injury and swelling (OR = 3.158; 95% CI = 1.341-7.435; p = 0.008) or diffuse injury and shift (OR = 3.826; 95% CI = 1.141-12.830; p = 0.030) in patients without DC. Temporal haematoma or contusion in the non-DC side (OR = 2.623; 95% CI = 1.070-6.428; p = 0.035) and traumatic SAH (OR = 3.751; 95% CI = 1.047-13.438; p = 0.042) were independently associated with the development of ipsilateral SDG in patients who underwent unilateral DC. However, factors independently associated with the development of contralateral SDG were frontal haematoma or contusion on the non-DC side (OR = 3.145; 95% CI = 1.272-7.774; p = 0.013) and SDH on the non-DC side (OR = 7.024; 95% CI = 1.477-33.390; p = 0.014). Only craniectomy area (OR = 1.030; 95% CI = 1.008-1.052; p = 0.008) was independently associated with the development of SDG in patients with bilateral DC. In the multivariate analysis, SDG in patients without DC was not associated with the development of hydrocephalus. However, SDG was significantly associated with the development of hydrocephalus for patients who underwent DC (OR = 2.173; 95% CI = 1.362-3.467; p = 0.001). CONCLUSIONS: This study suggested that the incidence of SDG in patients who have and have not undergone DC was identical; however, the patients' characteristics and risk factors differed. Therefore, the management and prediction of SDG should be performed according to SDG type.

The self and its resting state in consciousness: an investigation of the vegetative state.

Recent studies have demonstrated resting-state abnormalities in midline regions in vegetative state/unresponsive wakefulness syndrome and minimally conscious state patients. However, the functional implications of these resting-state abnormalities remain unclear. Recent findings in healthy subjects have revealed a close overlap between the neural substrate of self-referential processing and the resting-state activity in cortical midline regions. As such, we investigated task-related neural activity during active self-referential processing and various measures of resting-state activity in 11 patients with disorders of consciousness (DOC) and 12 healthy control subjects. Overall, the results revealed that DOC patients exhibited task-specific signal changes in anterior and posterior midline regions, including the perigenual anterior cingulate cortex (PACC) and posterior cingulate cortex (PCC). However, the degree of signal change was significantly lower in DOC patients compared with that in healthy subjects. Moreover, reduced signal differentiation in the PACC predicted the degree of consciousness in DOC patients. Importantly, the same midline regions (PACC and PCC) in DOC patients also exhibited severe abnormalities in the measures of resting-state activity, that is functional connectivity and the amplitude of low-frequency fluctuations. Taken together, our results provide the first evidence of neural abnormalities in both the self-referential processing and the resting state in midline regions in DOC patients. This novel finding has important implications for clinical utility and general understanding of the relationship between the self, the resting state, and consciousness.

Combination of Conventional EVD and Ommaya Drainage for Intraventricular Hemorrhage (IVH).

Background: The effect of Ommaya reservoirs on the clinical outcomes of patients with intraventricular hemorrhage (IVH) remains unclear. Objective: We aimed to determine the effect of combining the Ommaya reservoir and external ventricular drainage (EVD) therapy on IVH and explore better clinical indicators for Ommaya implantation. Methods: A retrospective analysis was conducted on patients diagnosed with IVH who received EVD-Ommaya drainage between January 2013 and March 2021. The patient population was divided into two groups: the Ommaya-used group, comprising patients in whom the Ommaya drainage system was activated post-surgery, and the Ommaya-unused group, comprising patients in whom the system was not activated. The study analyzed clinical, imaging, and outcome data of the patient population. Results: A total of 123 patients with IVH were included: 75 patients in the Ommaya-used group and 48 patients in the Ommaya-unused group. The patients in the Ommaya-used group showed a lower 3-month GOS than those in the Ommaya-unused group (p<0.0001). The modified Graeb scale (mGS) in the Ommaya-unused group was significantly lower than that in the Ommaya-used group before the operation (p<0.01) but not after surgery (p>0.05). The GCS in the Ommaya-unused group was significantly lower than that in the other group, and there was a close correlation between the GCS and 3-month GOS (p<0.0001). The GCS score showed significance in predicting the use of Ommaya (p<0.001). Conclusion: The study demonstrated that combining EVD and Ommaya drainage was a safe and feasible treatment for IVH. Additionally, preoperative GCS was found to predict the use of Ommaya drainage in subsequent treatment, providing valuable information for pre-surgery decision-making.

The neural correlates of arousal: Ventral posterolateral nucleus-global transient co-activation.

Arousal and awareness are two components of consciousness whose neural mechanisms remain unclear. Spontaneous peaks of global (brain-wide) blood-oxygenation-level-dependent (BOLD) signal have been found to be sensitive to changes in arousal. By contrasting BOLD signals at different arousal levels, we find decreased activation of the ventral posterolateral nucleus (VPL) during transient peaks in the global signal in low arousal and awareness states (non-rapid eye movement sleep and anesthesia) compared to wakefulness and in eyes-closed compared to eyes-open conditions in healthy awake individuals. Intriguingly, VPL-global co-activation remains high in patients with unresponsive wakefulness syndrome (UWS), who exhibit high arousal without awareness, while it reduces in rapid eye movement sleep, a state characterized by low arousal but high awareness. Furthermore, lower co-activation is found in individuals during N3 sleep compared to patients with UWS. These results demonstrate that co-activation of VPL and global activity is critical to arousal but not to awareness.