Correlation between Thalamocortical Tract and Default Mode Network with Consciousness Levels in Hypoxic-Ischemic Brain Injury Patients: A Comparative Study Using the Coma Recovery Scale-Revised.

BACKGROUND The thalamocortical tract (TCT) links nerve fibers between the thalamus and cerebral cortex, relaying motor/sensory information. The default mode network (DMN) comprises bilateral, symmetrical, isolated cortical regions of the lateral and medial parietal and temporal brain cortex. The Coma Recovery Scale-Revised (CRS-R) is a standardized neurobehavioral assessment of disorders of consciousness (DOC). In the present study, 31 patients with hypoxic-ischemic brain injury (HI-BI) were compared for changes in the TCT and DMN with consciousness levels assessed using the CRS-R. MATERIAL AND METHODS In this retrospective study, 31 consecutive patients with HI-BI (17 DOC,14 non-DOC) and 17 age- and sex-matched normal control subjects were recruited. Magnetic resonance imaging was used to diagnose HI-BI, and the CRS-R was used to evaluate consciousness levels at the time of diffusion tensor imaging (DTI). The fractional anisotropy (FA) values and tract volumes (TV) of the TCT and DMN were compared. RESULTS In patients with DOC, the FA values and TV of both the TCT and DMN were significantly lower compared to those of patients without DOC and the control subjects (p<0.05). When comparing the non-DOC and control groups, the TV of the TCT and DMN were significantly lower in the non-DOC group (p<0.05). Moreover, the CRS-R score had strong positive correlations with the TV of the TCT (r=0.501, p<0.05), FA of the DMN (r=0.532, p<0.05), and TV of the DMN (r=0.501, p<0.05) in the DOC group. CONCLUSIONS This study suggests that both the TCT and DMN exhibit strong correlations with consciousness levels in DOC patients with HI-BI.

Increased task-relevant fMRI responsiveness in comatose cardiac arrest patients is associated with improved neurologic outcomes.

Early prediction of the recovery of consciousness in comatose cardiac arrest patients remains challenging. We prospectively studied task-relevant fMRI responses in 19 comatose cardiac arrest patients and five healthy controls to assess the fMRI's utility for neuroprognostication. Tasks involved instrumental music listening, forward and backward language listening, and motor imagery. Task-specific reference images were created from group-level fMRI responses from the healthy controls. Dice scores measured the overlap of individual subject-level fMRI responses with the reference images. Task-relevant responsiveness index (Rindex) was calculated as the maximum Dice score across the four tasks. Correlation analyses showed that increased Dice scores were significantly associated with arousal recovery (P < 0.05) and emergence from the minimally conscious state (EMCS) by one year (P < 0.001) for all tasks except motor imagery. Greater Rindex was significantly correlated with improved arousal recovery (P = 0.002) and consciousness (P = 0.001). For patients who survived to discharge (n = 6), the Rindex's sensitivity was 75% for predicting EMCS (n = 4). Task-based fMRI holds promise for detecting covert consciousness in comatose cardiac arrest patients, but further studies are needed to confirm these findings. Caution is necessary when interpreting the absence of task-relevant fMRI responses as a surrogate for inevitable poor neurological prognosis.

[Metastatic brain lesion following gastrointestinal stromal tumor of the stomach complicated by coma.( A case report and literature review)]. / Metastaticheskoe porazhenie golovnogo mozga gastrointestinal'noi stromal'noi opukhol'yu zheludka, oslozhnivsheesya razvitiem komatoznogo sostoyaniya. (Opisanie sluchaya i obzor literatury).

The authors present an extremely rare case of metastatic brain lesion in a patient with gastrointestinal stromal tumor of the stomach. There are literature data on 23 cases of metastatic lesions of the brain, skull and soft tissues of the head in similar patients. Atypical localization of metastases can lead to some diagnostic difficulties, unreasonable cancellation of chemotherapy and delayed surgical treatment. A feature of our observation was postoperative coma determined by the features of the underlying disease.

Structural connectivity in recovery after coma: Connectome atlas approach.

AIM: Pathological states of recovery after coma as a result of a severe brain injury are marked with changes in structural connectivity of the brain. This study aimed to identify a topological correlation between white matter integrity and the level of functional and cognitive impairment in patients recovering after coma. METHODS: Structural connectomes were computed based on fractional anisotropy maps from 40 patients using a probabilistic human connectome atlas. We used a network based statistics approach to identify potential brain networks associated with a more favorable outcome, assessed with clinical neurobehavioral scores at the patient's discharge from the acute neurorehabilitation unit. RESULTS: We identified a subnetwork whose strength of connectivity correlated with a more favorable outcome as measured with the Disability Rating Scale (network based statistics: t >3.5, P =.010). The subnetwork predominated in the left hemisphere and included the thalamic nuclei, putamen, precentral and postcentral gyri, and medial parietal regions. Spearman correlation between the mean fractional anisotropy value of the subnetwork and the score was ρ = -0.60 (P <.0001). A less extensive overlapping subnetwork correlated with the Coma Recovery Scale Revised score, consisting mostly of the left hemisphere connectivity between the thalamic nuclei and pre- and post-central gyri (network based statistics: t >3.5, P =.033; Spearman's ρ = 0.58, P <.0001). CONCLUSION: The present findings suggest an important role of structural connectivity between the thalamus, putamen and somatomotor cortex in the recovery from coma as evaluated with neurobehavioral scores. These structures are part of the motor circuit involved in the generation and modulation of voluntary movement, as well as the forebrain mesocircuit supposedly underlying the maintenance of consciousness. As behavioural assessment of consciousness depends heavily on the signs of voluntary motor behaviour, further work will elucidate whether the identified subnetwork reflects the structural architecture underlying the recovery of consciousness or rather the ability to communicate its content.

Default mode network overshadow executive control network in coma emergence and awakening prediction of patients with sTBI.

OBJECTIVE: We aimed to explore the pathogenesis of traumatic coma related to functional connectivity (FC) within the default mode network (DMN), within the executive control network (ECN) and between the DMN and ECN and to investigate its capacity for predicting awakening. METHODS: We carried out resting-state functional magnetic resonance imaging (fMRI) examinations on 28 traumatic coma patients and 28 age-matched healthy controls. DMN and ECN nodes were split into regions of interest (ROIs), and node-to-node FC analysis was conducted on individual participants. To identify coma pathogenesis, we compared the pairwise FC differences between coma patients and healthy controls. Meanwhile, we divided the traumatic coma patients into different subgroups based on their clinical outcome scores at 6 months postinjury. Considering the awakening prediction, we calculated the area under the curve (AUC) to evaluate the predictive ability of changed FC pairs. RESULTS: We found a massive pairwise FC alteration in the patients with traumatic coma compared to the healthy controls [45% (33/74) pairwise FC located in the DMN, 27% (20/74) pairwise FC located in the ECN, and 28% (21/74) pairwise FC located between the DMN and ECN]. Moreover, in the awake and coma groups, there were 67% (12/18) pairwise FC alterations located in the DMN and 33% (6/18) pairwise FC alterations located between the DMN and ECN. We also indicated that pairwise FC that showed a predictive value of 6-month awakening was mainly located in the DMN rather than in the ECN. Specifically, decreased FC between the right superior frontal gyrus and right parahippocampal gyrus (in the DMN) showed the highest predictive ability (AUC = 0.827). CONCLUSION: In the acute phase of severe traumatic brain injury (sTBI), the DMN plays a more prominent role than the ECN and the DMN-ECN interaction in the emergence of traumatic coma and the prediction of 6-month awakening.

Parieto-Occipital Injury on Diffusion MRI Correlates with Poor Neurologic Outcome following Cardiac Arrest.

BACKGROUND AND PURPOSE: MR imaging of the brain provides unbiased neuroanatomic evaluation of brain injury and is useful for neurologic prognostication following cardiac arrest. Regional analysis of diffusion imaging may provide additional prognostic value and help reveal the neuroanatomic underpinnings of coma recovery. The purpose of this study was to evaluate global, regional, and voxelwise differences in diffusion-weighted MR imaging signal in patients in a coma after cardiac arrest. MATERIALS AND METHODS: We retrospectively analyzed diffusion MR imaging data from 81 subjects who were comatose for >48 hours following cardiac arrest. Poor outcome was defined as the inability to follow simple commands at any point during hospitalization. ADC differences between groups were evaluated across the whole brain, locally by using voxelwise analysis and regionally by using ROI-based principal component analysis. RESULTS: Subjects with poor outcome had more severe brain injury as measured by lower average whole-brain ADC (740 [SD, 102] × 10-6 mm2/s versus 833 [SD, 23] × 10-6 mm2/s, P < .001) and larger average volumes of tissue with ADC below 650 × 10-6 mms/s (464 [SD, 469] mL versus 62 [SD, 51] mL, P < .001). Voxelwise analysis showed lower ADC in the bilateral parieto-occipital areas and perirolandic cortices for the poor outcome group. ROI-based principal component analysis showed an association between lower ADC in parieto-occipital regions and poor outcome. CONCLUSIONS: Brain injury affecting the parieto-occipital region measured with quantitative ADC analysis was associated with poor outcomes after cardiac arrest. These results suggest that injury to specific brain regions may influence coma recovery.

Development and Internal Validation of a Prediction Model for Difficult Laryngoscopy Using Ultrasound-Derived Factor in Comatose Patients.

OBJECTIVES: The distance from skin to the hyoid bone (DSHB) and skin to the anterior commissure of vocal cords (DSAC) are reliable parameters for pre-operative airway ultrasound assessment in awake patients and can be assessed in comatose patients. This study aimed to inspect its feasibility and accuracy in predicting difficult laryngoscopy for comatose patients. METHODS: A prospective cohort study included patients with a Glasgow Coma Scale (GCS) of ≤8 who underwent emergency tracheal intubation between November 2019 and August 2020. The outcome was difficult laryngoscopy and classified according to the Cormack-Lehane grading. RESULTS: A total of 151 patients were included in the study. Fifty-two (34.4%) patients were categorized as having difficult laryngoscopy. The DSHB add DSAC (hereinafter referred to as the "DSBAC") was superior to either parameter alone in the predictive performance, and the optimal cut-off value was 1.90. To optimize the predictive value, DSBAC (adjusted odds ratio [OR]: 7.76; 95% confidence interval [CI]: 2.88-20.94; P < .001), GCS (adjusted OR: 1.39; 95% CI: 3.93-26.28; P = .039), mandibular retraction (adjusted OR: 8.20; 95% CI: 1.92-35.09; P = .005) and edentulous (adjusted OR: 4.23; 95% CI: 1.40-12.80; P = .011) were included in a multivariable model and constructed a nomogram. Discrimination and calibration statistics were satisfactory, with C-index above 0.80 from both model development and internal validation. CONCLUSIONS: Ultrasound-derived factor, DSBAC, can be easily assessed and help predict difficult laryngoscopy among comatose patients. A simple nomogram including only four clinical items exhibited excellent discrimination performance and was useful when comatose patients underwent emergency tracheal intubation.

Comparison of the prognostic value of early-phase proton magnetic resonance spectroscopy and diffusion tensor imaging with serum neuron-specific enolase at 72 h in comatose survivors of out-of-hospital cardiac arrest-a substudy of the XeHypotheca trial.

PURPOSE: We compared the predictive accuracy of early-phase brain diffusion tensor imaging (DTI), proton magnetic resonance spectroscopy (1H-MRS), and serum neuron-specific enolase (NSE) against the motor score and epileptic seizures (ES) for poor neurological outcome after out-of-hospital cardiac arrest (OHCA). METHODS: The predictive accuracy of DTI, 1H-MRS, and NSE along with motor score at 72 h and ES for the poor neurological outcome (modified Rankin Scale, mRS, 3 - 6) in 92 comatose OHCA patients at 6 months was assessed by area under the receiver operating characteristic curve (AUROC). Combined models of the variables were included as exploratory. RESULTS: The predictive accuracy of fractional anisotropy (FA) of DTI (AUROC 0.73, 95% CI 0.62-0.84), total N-acetyl aspartate/total creatine (tNAA/tCr) of 1H-MRS (0.78 (0.68 - 0.88)), or NSE at 72 h (0.85 (0.76 - 0.93)) was not significantly better than motor score at 72 h (0.88 (95% CI 0.80-0.96)). The addition of FA and tNAA/tCr to a combination of NSE, motor score, and ES provided a small but statistically significant improvement in predictive accuracy (AUROC 0.92 (0.85-0.98) vs 0.98 (0.96-1.00), p = 0.037). CONCLUSION: None of the variables (FA, tNAA/tCr, ES, NSE at 72 h, and motor score at 72 h) differed significantly in predicting poor outcomes in this patient group. Early-phase quantitative neuroimaging provided a statistically significant improvement for the predictive value when combined with ES and motor score with or without NSE. However, in clinical practice, the additional value is small, and considering the costs and challenges of imaging in this patient group, early-phase DTI/MRS cannot be recommended for routine use. TRIAL REGISTRATION: ClinicalTrials.gov NCT00879892, April 13, 2009.

A Simple Echocardiographic Approach in Assessing the Prognosis of Comatose Patients with Acute Intracerebral Hemorrhage by Using Right Ventricle-Pulmonary Artery Coupling.

BACKGROUND: It remains a challenge to judge whether comatose patients with acute intracerebral hemorrhage (ICH) can wake up. Here, we aimed to investigate the changes in right ventricle-pulmonary artery (RV-PA) coupling over time in these patients and to evaluate its performance for discriminating between those who woke up within 60 days and those who did not. METHODS: Thirty-five comatose patients with acute spontaneous ICH underwent bedside echocardiography on days 1, 3, and 5 after onset with the measurement of tricuspid annular plane systolic excursion and mean pulmonary artery pressure. The RV-PA coupling (the ratio of tricuspid annular plane systolic excursion to mean pulmonary artery pressure) was calculated. RESULTS: Within 60 days of the onset of coma, 11 individuals awakened and survived, and 24 individuals died. In awakened patients, RV-PA couplings did not differ among days 1, 3, and 5 (1.62 ± 0.38 vs. 1.61 ± 0.32 vs. 1.64 ± 0.25 mm/mm Hg, P > 0.05), whereas in unawakened patients, they decreased drastically from day 1 to day 3 and then to day 5 (1.26 ± 0.32 vs. 0.63 ± 0.05 vs. 0.43 ± 0.06 mm/mm Hg, P < 0.05). The area under receiver operating characteristic curve of 0.992 for the ratio of RV-PA coupling on day 5 to day 1 of the coma was superior to that for the Glasgow Coma Scale (area under receiver operating characteristic curve of 0.606) in the discrimination of comatose patients with ICH who woke up within 60 days from those who did not. The optimal cutoff value was 0.536, with a sensitivity of 100.00%, a specificity of 96.24%, and an accuracy of 97.13%. CONCLUSIONS: Right ventricle-pulmonary artery coupling demonstrated a high performance for discriminating comatose patients with ICH who woke up within 60 days from those who did not.

MRI markers of brain network integrity relate to neurological outcome in postanoxic coma.

AIM: Current multimodal approaches leave approximately half of the comatose patients after cardiac arrest with an indeterminate prognosis. Here we investigated whether early MRI markers of brain network integrity can distinguish between comatose patients with a good versus poor neurological outcome six months later. METHODS: We performed a prospective cohort study in 48 patients after cardiac arrest submitted in a comatose state to the Intensive Care Unit of two Dutch hospitals. MRI was performed at three days after cardiac arrest, including resting state functional MRI and diffusion-tensor imaging (DTI). Resting state fMRI was used to quantify functional connectivity within ten resting-state networks, and DTI to assess mean diffusivity (MD) in these same networks. We contrasted two groups of patients, those with good (n = 29, cerebral performance category 1-2) versus poor (n = 19, cerebral performance category 3-5) outcome at six months. Mutual associations between functional connectivity, MD, and clinical outcome were studied. RESULTS: Patients with good outcome show higher within-network functional connectivity (fMRI) and higher MD (DTI) than patients with poor outcome across 8/10 networks, most prominent in the default mode network, salience network, and visual network. While the anatomical distribution of outcome-related changes was similar for functional connectivity and MD, the pattern of inter-individual differences was very different: functional connectivity showed larger inter-individual variability in good versus poor outcome, while the opposite was observed for MD. Exploratory analyses suggested that it is possible to define network-specific cut-off values that could help in outcome prediction: (1) high functional connectivity and high MD, associated with good outcome; (2) low functional connectivity and low MD, associated with poor outcome; (3) low functional connectivity and high MD, associated with uncertain outcome. DISCUSSION: Resting-state functional connectivity and mean diffusivity-three days after cardiac arrest are strongly associated with neurological recovery-six months later in a complementary fashion. The combination of fMRI and MD holds potential to improve prediction of outcome.

Multimodal MRI-Based Whole-Brain Assessment in Patients In Anoxoischemic Coma by Using 3D Convolutional Neural Networks.

BACKGROUND: There is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma. METHODS: This proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020. All patients were scanned in coma state at least 2 days (4 ± 2 days) after cardiac arrest. Over the same period, age-matched healthy volunteers were recruited and included. Brain MRI quantification encompassed both "functional data" from regions of interest (precuneus and posterior cingulate cortex) with whole-brain functional connectivity analysis and "structural data" (gray matter volume, T1-weighted, fractional anisotropy, and mean diffusivity). A specifically designed 3D convolutional neuronal network (CNN) was created to allow conscious state discrimination (coma vs. controls) by using raw MRI indices as the input. A voxel-wise visualization method based on the study of convolutional filters was applied to support CNN outcome. The Ethics Committee of the University Teaching Hospital of Toulouse, France (2018-A31) approved the study and informed consent was obtained from all participants. RESULTS: The final cohort consisted of 29 patients in postanoxic coma and 34 healthy volunteers. Coma patients were successfully discerned from controls by using 3D CNN in combination with different MR indices. The best accuracy was achieved by functional MRI data, in particular with resting-state functional MRI of the posterior cingulate cortex, with an accuracy of 0.96 (range 0.94-0.98) on the test set from 10-time repeated tenfold cross-validation. Even more satisfactory performances were achieved through the majority voting strategy, which was able to compensate for mistakes from single MR indices. Visualization maps allowed us to identify the most relevant regions for each MRI index, notably regions previously described as possibly being involved in consciousness emergence. Interestingly, a posteriori analysis of misclassified patients indicated that they may present some common functional MRI traits with controls, which suggests further favorable outcomes. CONCLUSIONS: A fully automated identification of clinically relevant signals from complex multimodal neuroimaging data is a major research topic that may bring a radical paradigm shift in the neuroprognostication of patients with severe brain injury. We report for the first time a successful discrimination between patients in postanoxic coma patients from people serving as controls by using 3D CNN whole-brain structural and functional MRI data. Clinical Trial Number http://ClinicalTrials.gov (No. NCT03482115).

Prognosis After Cardiac Arrest: The Additional Value of DWI and FLAIR to EEG.

BACKGROUND: Despite application of the multimodal European Resuscitation Council and European Society of Intensive Care Medicine algorithm, neurological prognosis of patients who remain comatose after cardiac arrest remains uncertain in a large group of patients. In this study, we investigate the additional predictive value of visual and quantitative brain magnetic resonance imaging (MRI) to electroencephalography (EEG) for outcome estimation of comatose patients after cardiac arrest. METHODS: We performed a prospective multicenter cohort study in patients after cardiac arrest submitted in a comatose state to the intensive care unit of two Dutch hospitals. Continuous EEG was recorded during the first 3 days and MRI was performed at 3 ± 1 days after cardiac arrest. EEG at 24 h and ischemic damage in 21 predefined brain regions on diffusion weighted imaging and fluid-attenuated inversion recovery on a scale from 0 to 4 were related to outcome. Quantitative MRI analyses included mean apparent diffusion coefficient (ADC) and percentage of brain volume with ADC < 450 × 10-6 mm2/s, < 550 × 10-6 mm2/s, and < 650 × 10-6 mm2/s. Poor outcome was defined as a Cerebral Performance Category score of 3-5 at 6 months. RESULTS: We included 50 patients, of whom 20 (40%) demonstrated poor outcome. Visual EEG assessment correctly identified 3 (15%) with poor outcome and 15 (50%) with good outcome. Visual grading of MRI identified 13 (65%) with poor outcome and 25 (89%) with good outcome. ADC analysis identified 11 (55%) with poor outcome and 3 (11%) with good outcome. EEG and MRI combined could predict poor outcome in 16 (80%) patients at 100% specificity, and good outcome in 24 (80%) at 63% specificity. Ischemic damage was most prominent in the cortical gray matter (75% vs. 7%) and deep gray nuclei (45% vs. 3%) in patients with poor versus good outcome. CONCLUSIONS: Magnetic resonance imaging is complementary with EEG for the prediction of poor and good outcome of patients after cardiac arrest who are comatose at admission.

Nasal Air Puff Promotes Default Mode Network Activity in Mechanically Ventilated Comatose Patients: A Noninvasive Brain Stimulation Approach.

OBJECTIVES: Coma state and loss of consciousness are associated with impaired brain activity, particularly gamma oscillations, that integrate functional connectivity in neural networks, including the default mode network (DMN). Mechanical ventilation (MV) in comatose patients can aggravate brain activity, which has decreased in coma, presumably because of diminished nasal airflow. Nasal airflow, known to drive functional neural oscillations, synchronizing distant brain networks activity, is eliminated by tracheal intubation and MV. Hence, we proposed that rhythmic nasal air puffing in mechanically ventilated comatose patients may promote brain activity and improve network connectivity. MATERIALS AND METHODS: We recorded electroencephalography (EEG) from 15 comatose patients (seven women) admitted to the intensive care unit because of opium poisoning and assessed the activity, complexity, and connectivity of the DMN before and during the nasal air-puff stimulation. Nasal cavity air puffing was done through a nasal cannula controlled by an electrical valve (open duration of 630 ms) with a frequency of 0.2 Hz (ie, 12 puff/min). RESULTS: Our analyses demonstrated that nasal air puffing enhanced the power of gamma oscillations (30-100 Hz) in the DMN. In addition, we found that the coherence and synchrony between DMN regions were increased during nasal air puffing. Recurrence quantification and fractal dimension analyses revealed that EEG global complexity and irregularity, typically seen in wakefulness and conscious state, increased during rhythmic nasal air puffing. CONCLUSIONS: Rhythmic nasal air puffing, as a noninvasive brain stimulation method, opens a new window to modifying the brain connectivity integration in comatose patients. This approach may potentially influence comatose patients' outcomes by increasing brain reactivity and network connectivity.

Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest.

BACKGROUND: Establishing whether a patient who survived a cardiac arrest has suffered hypoxic-ischemic brain injury (HIBI) shortly after return of spontaneous circulation (ROSC) can be of paramount importance for informing families and identifying patients who may benefit the most from neuroprotective therapies. We hypothesize that using deep transfer learning on normal-appearing findings on head computed tomography (HCT) scans performed after ROSC would allow us to identify early evidence of HIBI. METHODS: We analyzed 54 adult comatose survivors of cardiac arrest for whom both an initial HCT scan, done early after ROSC, and a follow-up HCT scan were available. The initial HCT scan of each included patient was read as normal by a board-certified neuroradiologist. Deep transfer learning was used to evaluate the initial HCT scan and predict progression of HIBI on the follow-up HCT scan. A naive set of 16 additional patients were used for external validation of the model. RESULTS: The median age (interquartile range) of our cohort was 61 (16) years, and 25 (46%) patients were female. Although findings of all initial HCT scans appeared normal, follow-up HCT scans showed signs of HIBI in 29 (54%) patients (computed tomography progression). Evaluating the first HCT scan with deep transfer learning accurately predicted progression to HIBI. The deep learning score was the most significant predictor of progression (area under the receiver operating characteristic curve = 0.96 [95% confidence interval 0.91-1.00]), with a deep learning score of 0.494 having a sensitivity of 1.00, specificity of 0.88, accuracy of 0.94, and positive predictive value of 0.91. An additional assessment of an independent test set confirmed high performance (area under the receiver operating characteristic curve = 0.90 [95% confidence interval 0.74-1.00]). CONCLUSIONS: Deep transfer learning used to evaluate normal-appearing findings on HCT scans obtained early after ROSC in comatose survivors of cardiac arrest accurately identifies patients who progress to show radiographic evidence of HIBI on follow-up HCT scans.

Prognostication of neurologic outcome using gray-white-matter-ratio in comatose patients after cardiac arrest.

BACKGROUND: This study aimed to assess the prognostic value regarding neurologic outcome of CT neuroimaging based Gray-White-Matter-Ratio measurement in patients after resuscitation from cardiac arrest. METHODS: We retrospectively evaluated CT neuroimaging studies of 91 comatose patients resuscitated from cardiac arrest and 46 non-comatose controls. We tested the diagnostic performance of Gray-White-Matter-Ratio compared with established morphologic signs of hypoxic-ischaemic brain injury, e. g. loss of distinction between gray and white matter, and laboratory parameters, i. e. neuron-specific enolase, for the prediction of poor neurologic outcomes after resuscitated cardiac arrest. Primary endpoint was neurologic function assessed with cerebral performance category score 30 days after the index event. RESULTS: Gray-White-Matter-Ratio showed encouraging interobserver variability (ICC 0.670 [95% CI: 0.592-0.741] compared to assessment of established morphologic signs of hypoxic-ischaemic brain injury (Fleiss kappa 0.389 [95% CI: 0.320-0.457]) in CT neuroimaging studies. It correlated with cerebral performance category score with lower Gray-White-Matter-Ratios associated with unfavourable neurologic outcomes. A cut-off of 1.17 derived from the control population predicted unfavourable neurologic outcomes in adult survivors of cardiac arrest with 100% specificity, 50.3% sensitivity, 100% positive predictive value, and 39.3% negative predictive value. Gray-White-Matter-Ratio prognostic power depended on the time interval between circulatory arrest and CT imaging, with increasing sensitivity the later the image acquisition was executed. CONCLUSIONS: A reduced Gray-White-Matter-Ratio is a highly specific prognostic marker of poor neurologic outcomes early after resuscitation from cardiac arrest. Sensitivity seems to be dependent on the time interval between circulatory arrest and image acquisition, with limited value within the first 12 h.

Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness.

To determine the role of early acquisition of blood oxygen level-dependent (BOLD) signals and diffusion tensor imaging (DTI) for analysis of the connectivity of the ascending arousal network (AAN) in predicting neurological outcomes after acute traumatic brain injury (TBI), cardiopulmonary arrest (CPA), or stroke. A prospective analysis of 50 comatose patients was performed during their ICU stay. Image processing was conducted to assess structural and functional connectivity of the AAN. Outcomes were evaluated after 3 and 6 months. Nineteen patients (38%) had stroke, 18 (36%) CPA, and 13 (26%) TBI. Twenty-three patients were comatose (44%), 11 were in a minimally conscious state (20%), and 16 had unresponsive wakefulness syndrome (32%). Univariate analysis demonstrated that measurements of diffusivity, functional connectivity, and numbers of fibers in the gray matter, white matter, whole brain, midbrain reticular formation, and pontis oralis nucleus may serve as predictive biomarkers of outcome depending on the diagnosis. Multivariate analysis demonstrated a correlation of the predicted value and the real outcome for each separate diagnosis and for all the etiologies together. Findings suggest that the above imaging biomarkers may have a predictive role for the outcome of comatose patients after acute TBI, CPA, or stroke.

[Combination of apparent diffusion coefficient and amplitude-integrated electroencephalogram to predict the outcome of comatose patients with severe traumatic brain injury].

OBJECTIVE: To examine whether the combination of quantitative regional apparent diffusion coefficient (ADC) and amplitude-integrated electroencephalogram (aEEG) can predict the outcome of comatose patients with severe traumatic brain injury (sTBI). METHODS: A prospective study was conducted. The patients with coma caused by sTBI [Glasgow coma scale (GCS) < 8] admitted to Suqian First Hospital from January 2016 to June 2019 were enrolled. All patients underwent aEEG examination and magnetic resonance imaging (MRI) scan within 1 week after emergency treatment. The ADC values of 9 regions of interest (frontal gray matter and white matter, parietal gray matter and white matter, temporal gray matter and white matter, caudate nucleus of basal ganglia, lenticular nucleus and thalamus) were measured by head MRI, and the mean ADC values of frontal lobe, parietal lobe, temporal lobe and basal ganglia were calculated respectively. According to the follow-up results after 12 months, the differences of each index between patients with poor prognosis [Glasgow outcome score (GOS) 1-2] and patients with good prognosis (GOS 3-5) were compared; the receiver operating characteristic curve (ROC curve) was drawn to evaluate the predictive ability of aEEG and ADC for the good prognosis of patients with sTBI, and the predictive value of the combination of aEEG and ADC. RESULTS: A total of 52 patients with sTBI were enrolled, with mean age of (36.7±13.9) years old, 35 of whom were male. Within 12 months follow-up, 29 patients had achieved favorable outcomes and 23 patients had unfavorable outcome. There were 21, 17 and 14 patients with aEEG, and grade, respectively, and 19, 10 and 0 patients had good prognosis respectively. ADC values of 9 regions of interest in patients with good prognosis were significantly higher than those in patients with poor prognosis (×10-6 mm2/s: 924±107 vs. 531±87 in frontal gray matter, 804±95 vs. 481±74 in frontal white matter, 831±93 vs. 683±72 in temporal gray matter, 726±87 vs. 654±63 in temporal white matter, 767±79 vs. 690±75 in parietal gray matter, 716±84 vs. 642±62 in parietal white matter, 689±70 vs. 465±68 in caudate nucleus, 723±84 vs. 587±71 in lenticular nucleus, 807±79 vs. 497±67 in thalamus, all P < 0.01). ROC curve analysis showed that the area under ROC curve (AUC) of aEEG for predicting good prognosis of sTBI patients was 0.826, when the cut-off value of aEEG was < 1.5, the sensitivity was 94.7% and the specificity was 72.8%. Among the ADC value prediction abilities in the interested areas, the prediction of ADC value in frontal lobe and basal ganglia area were better than that in sTBI patients. AUC was 0.817 and 0.903 respectively. The best cut-off values were > 726×10-6 mm2/s and > 624×10-6 mm2/s respectively, the sensitivity of predicting prognosis were both 100%, and the specificity was 63.4% and 61.8%. A model combining frontal ADC and basal ganglia ADC with aEEG was 91.0% sensitive and 93.7% specific for favorable outcome of sTBI patients. CONCLUSIONS: Combination of the quantitative measurement of regional ADC and aEEG may be useful for predicting the outcome of the patients with sTBI.

Location of Subcortical Microbleeds and Recovery of Consciousness After Severe Traumatic Brain Injury.

BACKGROUND: In patients with severe traumatic brain injury (TBI), coma is associated with impaired subcortical arousal mechanisms. However, it is unknown which nuclei involved in arousal (arousal nuclei) are implicated in coma pathogenesis and are compatible with coma recovery. METHODS: We mapped an atlas of arousal nuclei in the brainstem, thalamus, hypothalamus, and basal forebrain onto 3 tesla susceptibility-weighted images (SWI) in 12 patients with acute severe TBI who presented in coma and recovered consciousness within 6 months. We assessed the spatial distribution and volume of SWI microbleeds and evaluated the association of microbleed volume with the duration of unresponsiveness and functional recovery at 6 months. RESULTS: There was no single arousal nucleus affected by microbleeds in all patients. Rather, multiple combinations of microbleeds in brainstem, thalamic, and hypothalamic arousal nuclei were associated with coma and were compatible with recovery of consciousness. Microbleeds were frequently detected in the midbrain (100%), thalamus (83%), and pons (75%). Within the brainstem, the microbleed incidence was largest within the mesopontine tegmentum (e.g., pedunculotegmental nucleus, mesencephalic reticular formation) and ventral midbrain (e.g., substantia nigra, ventral tegmental area). Brainstem arousal nuclei were partially affected by microbleeds, with microbleed volume not exceeding 35% of brainstem nucleus volume on average. Compared to microbleed volume within nonarousal brainstem regions, the microbleed volume within arousal brainstem nuclei accounted for a larger proportion of variance in the duration of unresponsiveness and 6-month Glasgow Outcome Scale-Extended scores. CONCLUSION: These results suggest resilience of arousal mechanisms in the human brain after severe TBI.