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.

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.

sTBI-GAN: An adversarial learning approach for data synthesis on traumatic brain segmentation.

Automatic brain segmentation of magnetic resonance images (MRIs) from severe traumatic brain injury (sTBI) patients is critical for brain abnormality assessments and brain network analysis. Construction of sTBI brain segmentation model requires manually annotated MR scans of sTBI patients, which becomes a challenging problem as it is quite impractical to implement sufficient annotations for sTBI images with large deformations and lesion erosion. Data augmentation techniques can be applied to alleviate the issue of limited training samples. However, conventional data augmentation strategies such as spatial and intensity transformation are unable to synthesize the deformation and lesions in traumatic brains, which limits the performance of the subsequent segmentation task. To address these issues, we propose a novel medical image inpainting model named sTBI-GAN to synthesize labeled sTBI MR scans by adversarial inpainting. The main strength of our sTBI-GAN method is that it can generate sTBI images and corresponding labels simultaneously, which has not been achieved in previous inpainting methods for medical images. We first generate the inpainted image under the guidance of edge information following a coarse-to-fine manner, and then the synthesized MR image is used as the prior for label inpainting. Furthermore, we introduce a registration-based template augmentation pipeline to increase the diversity of the synthesized image pairs and enhance the capacity of data augmentation. Experimental results show that the proposed sTBI-GAN method can synthesize high-quality labeled sTBI images, which greatly improves the 2D and 3D traumatic brain segmentation performance compared with the alternatives. Code is available at .

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.

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.

Clinical application of neuromodulation therapy in patients with disorder of consciousness: A pooled analysis of 544 participants.

BACKGROUND: The number of patients with disorders of consciousness (DoC) has increased dramatically with the advancement of intensive care and emergency medicine, which brings tremendous economic burdens and even ethical issues to families and society. OBJECTIVE: To evaluate the effectiveness of neuromodulation therapy for patients with DoC. METHODS: First, we conducted a literature review of individual patient data (IPD) on PubMed, EMBASE, and Cochrane-controlled trials following PRISMA guidelines. Then, we collected neuromodulation cases from our institution. Finally, we conducted a pooled analysis using the participants from the medical literature (n = 522) and our local institutions (n = 22). RESULTS: In this pooled analysis of 544 patients with DoC with a mean age of 46.33 years, our results revealed that patients have improved CRS-R scores [1.0 points (95% CI, 0.57-1.42)] after neuromodulation. Among them, patients have better effectiveness in traumatic than non-traumatic etiology (P < 0.05). The effectiveness of consciousness improvement could be affected by the age, baseline consciousness state, and duration of stimulation. Compared with non-invasive intervention, an invasive intervention can bring more behavioral improvement (P < 0.0001) to MCS rather than UWS/VS patients. Importantly, neuromodulation is a valuable therapy even years after the onset of DoC. CONCLUSION: This pooled analysis spotlights that the application of neuromodulation can improve the behavioral performance of patients with DoC. A preliminary trend is that age, etiology, baseline consciousness state, and stimulation duration could impact its effectiveness.

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation.

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which requires a significant amount of expert annotated samples that are high-cost and laborious. Semi-supervised image segmentation can alleviate the problem by utilizing a large number of unlabeled images along with limited labeled images. However, learning a robust representation from numerous unlabeled images remains challenging due to potential noise in pseudo labels and insufficient class separability in feature space, which undermines the performance of current semi-supervised segmentation approaches. To address the issues above, we propose a novel semi-supervised segmentation method named as Rectified Contrastive Pseudo Supervision (RCPS), which combines a rectified pseudo supervision and voxel-level contrastive learning to improve the effectiveness of semi-supervised segmentation. Particularly, we design a novel rectification strategy for the pseudo supervision method based on uncertainty estimation and consistency regularization to reduce the noise influence in pseudo labels. Furthermore, we introduce a bidirectional voxel contrastive loss in the network to ensure intra-class consistency and inter-class contrast in feature space, which increases class separability in the segmentation. The proposed RCPS segmentation method has been validated on two public datasets and an in-house clinical dataset. Experimental results reveal that the proposed method yields better segmentation performance compared with the state-of-the-art methods in semi-supervised medical image segmentation. The source code is available at https://github.com/hsiangyuzhao/RCPS.

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.

Clinical Characteristics of and Treatment Strategy for Hydrocephalus in Patients With Severe Disorders of Consciousness.

Abstract Making an appropriate diagnosis and administering effective treatment for hydrocephalus in patients with severe disorders of consciousness (DOC) remains controversial and difficult. Given that the typical symptoms are usually concealed by the limited behavioral responsiveness of patients with severe DOC, hydrocephalus diagnosis is likely to be missed in the clinic. Even if not, the presence of hydrocephalus may reduce the likelihood of DOC recovery, posing a conundrum for clinicians. From December 2013 to January 2023, the clinical data and therapeutic schedule of hydrocephalus in patients with severe DOC at Huashan Hospital's Neurosurgical Emergency Center were studied retrospectively. Sixty-eight patients (mean age [± SD] 52.53 ± 17.03 years, 35 males and 33 females) with severe DOC were included. The hydrocephalus was discovered after computed tomography (CT) or magnetic resonance imaging (MRI) revealed enlarged ventricles in the patients. During hospitalization, patients underwent a surgical treatment that included a ventriculoperitoneal (V-P) shunt and/or cranioplasty (CP) implantation. Following the surgery, an individualized V-P pressure was established based on the patient's ventricle size and neurological function variation. To account for the improvement in consciousness in patients with severe DOC, Glasgow Coma Scale (GCS) and Coma Recovery Scale-Revised (CRS-R) assessments were performed before and after hydrocephalus treatment. All patients with severe DOC had varying degrees of ventricular enlargement, deformation, and poor brain compliance. Approximately 60.3% (41/68) of them had low- or negative-pressure hydrocephalus (LPH or NegPH). Of the patients, 45.5% (31/68) had a one-stage V-P shunt and CP operation performed concurrently, whereas the remaining 37 patients had a single V-P shunt operation performed independently. Besides two patients with DOC who developed surgical complications, 92.4% (61/66) of the survivors showed an improvement in consciousness after hydrocephalus treatment. In patients with severe DOC, LPH or NegPH was common. Secondary hydrocephalus in patients with DOC had been largely ignored, hampering their neurological rehabilitation. Even months or years after the onset of severe DOC, active treatment of hydrocephalus can significantly improve patients' consciousness and neurological function. This study summarized several evidence-based treatment experiences of hydrocephalus in patients with DOC.

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.

Enhanced brain parcellation via abnormality inpainting for neuroimage-based consciousness evaluation of hydrocephalus patients by lumbar drainage.

Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient's consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus.

Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury.

AIMS: Hypokalemia is a common complication following traumatic brain injury, which may complicate treatment and lead to unfavorable outcomes. Identifying patients at risk of hypokalemia on the first day of admission helps to implement prophylactic treatment, reduce complications, and improve prognosis. METHODS: This multicenter retrospective study was performed between January 2017 and December 2020 using the electronic medical records of patients admitted due to traumatic brain injury. A propensity score matching approach was adopted with a ratio of 1:1 to overcome overfitting and data imbalance during subgroup analyses. Five machine learning algorithms were applied to generate a best-performed prediction model for in-hospital hypokalemia. The internal fivefold cross-validation and external validation were performed to demonstrate the interpretability and generalizability. RESULTS: A total of 4445 TBI patients were recruited for analysis and model generation. Hypokalemia occurred in 46.55% of recruited patients and the incidences of mild, moderate, and severe hypokalemia were 32.06%, 12.69%, and 1.80%, respectively. Hypokalemia was associated with increased mortality, while severe hypokalemia cast greater impacts. The logistic regression algorithm had the best performance in predicting decreased serum potassium and moderate-to-severe hypokalemia, with an AUC of 0.73 ± 0.011 and 0.74 ± 0.019, respectively. The prediction model was further verified using two external datasets, including our previous published data and the open-assessed Medical Information Mart for Intensive Care database. Linearized calibration curves showed no statistical difference (p > 0.05) with perfect predictions. CONCLUSIONS: The occurrence of hypokalemia following traumatic brain injury can be predicted by first hospitalization day records and machine learning algorithms. The logistic regression algorithm showed an optimal predicting performance verified by both internal and external validation.

Clinical Decision on Disorders of Consciousness After Acquired Brain Injury: Stepping Forward.

Major advances have been made over the past few decades in identifying and managing disorders of consciousness (DOC) in patients with acquired brain injury (ABI), bringing the transformation from a conceptualized definition to a complex clinical scenario worthy of scientific exploration. Given the continuously-evolving framework of precision medicine that integrates valuable behavioral assessment tools, sophisticated neuroimaging, and electrophysiological techniques, a considerably higher diagnostic accuracy rate of DOC may now be reached. During the treatment of patients with DOC, a variety of intervention methods are available, including amantadine and transcranial direct current stimulation, which have both provided class II evidence, zolpidem, which is also of high quality, and non-invasive stimulation, which appears to be more encouraging than pharmacological therapy. However, heterogeneity is profoundly ingrained in study designs, and only rare schemes have been recommended by authoritative institutions. There is still a lack of an effective clinical protocol for managing patients with DOC following ABI. To advance future clinical studies on DOC, we present a comprehensive review of the progress in clinical identification and management as well as some challenges in the pathophysiology of DOC. We propose a preliminary clinical decision protocol, which could serve as an ideal reference tool for many medical institutions.

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.

Outcome Prediction of Unconscious Patients Based on Weighted Sparse Brain Network Construction.

It is quite challenging to establish a prompt and reliable prognosis assessment for acquired brain injury (ABI) patients with persistent severe disorders of consciousness (DOC) like unconscious comatose and unresponsive wakefulness syndrome (a.k.a., vegetative state). Recent advances in brain functional imaging and functional net-work analysis have demonstrated its potential in determining the consciousness level and prognostic outcome for ABI patients with DOC. However, the diagnostic and prognostic usefulness of the whole-brain functional connectome based on advanced machine learning techniques has not been fully evaluated. The first aim of this study is to predict the outcome of individual unconscious ABI patients during a three-month follow-up. The second aim is to conduct precise individualized differentiation among different consciousness levels for exploring the neurobiological mechanisms underlying DOC. Based on resting-state fMRI, we construct large-scale functional networks by using a weighted sparse model, which ensures sparsity and interpretability by preserving strong functional connections. The functional connection strengths are exploited as features for outcome prediction and consciousness level differentiation. We achieve significantly improved consciousness level classification (accuracy: 84.78%) and recovery outcome prediction (accuracy: 89.74%) compared to other network construction methods. More importantly, we reveal the contributive connections across the entire brain in both tasks. These connections could serve as the potential biomarkers for better understanding of consciousness and further provide new insight into the development of diagnostic, prognostic, and effective therapeutic guidelines for ABI patients with DOC.

Behavioral effects in disorders of consciousness following transcranial direct current stimulation: A systematic review and individual patient data meta-analysis of randomized clinical trials.

Background: In patients with Disorders of Consciousness (DoC), recent evidence suggests that transcranial direct current stimulation (tDCS) can be a promising intervention for them. However, there has been little agreement on the treatment effect and the optimal treatment strategy for the tDCS in patients with DoC. Objective: In this meta-analysis of individual patient data (IPD), we assess whether tDCS could improve DoC patients' behavioral performance. We also determine whether these treatment effects could be modified by patient characteristics or tDCS protocol. Methods: We searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials until 7 April 2022 using the terms "persistent vegetative state," "minimally conscious state," "disorder of consciousness," or "unresponsive wakefulness syndrome," and "transcranial direct current stimulation" to identify Randomized Controlled Trials (RCTs) in English-language publications. Studies were eligible for inclusion if they reported pre- and post-tDCS Coma Recovery Scale-Revised (CRS-R) scores. From the included studies, patients who had incomplete data were excluded. We performed a meta-analysis to assess the treatment effect of the tDCS compared with sham control. Additionally, various subgroup analyses were performed to determine whether specific patient characteristics could modify the treatment effect and to find out the optimal tDCS protocol. Results: We identified 145 papers, but eventually eight trials (including 181 patients) were included in the analysis, and one individual data were excluded because of incomplete data. Our meta-analysis demonstrated a mean difference change in the CRS-R score of 0.89 (95% CI, 0.17-1.61) between tDCS and sham-control, favoring tDCS. The subgroup analysis showed that patients who were male or with a minimally conscious state (MCS) diagnosis were associated with a greater improvement in CRS-R score. We also found that patients who underwent five or more sessions of tDCS protocol had a better treatment effect than just one session. Conclusion: The result shows that tDCS can improve the behavioral performance of DoC patients. The heterogeneity of the treatment effect existed within the patients' baseline conditions and the stimulation protocol. More explorative studies on the optimal tDCS protocol and the most beneficial patient group based on the mechanism of tDCS are required in the future. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42022331241.

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.

Neuroinflammation Following Traumatic Brain Injury: Take It Seriously or Not.

Traumatic brain injury (TBI) is associated with high mortality and disability, with a substantial socioeconomic burden. With the standardization of the treatment process, there is increasing interest in the role that the secondary insult of TBI plays in outcome heterogeneity. The secondary insult is neither detrimental nor beneficial in an absolute sense, among which the inflammatory response was a complex cascade of events and can thus be regarded as a double-edged sword. Therefore, clinicians should take the generation and balance of neuroinflammation following TBI seriously. In this review, we summarize the current human and animal model studies of neuroinflammation and provide a better understanding of the inflammatory response in the different stages of TBI. In particular, advances in neuroinflammation using proteomic and transcriptomic techniques have enabled us to identify a functional specific delineation of the immune cell in TBI patients. Based on recent advances in our understanding of immune cell activation, we present the difference between diffuse axonal injury and focal brain injury. In addition, we give a figurative profiling of the general paradigm in the pre- and post-injury inflammatory settings employing a bow-tie framework.

Non-invasive intracranial pressure assessment using shear-wave elastography in neuro-critical care patients.

OBJECTIVE: To determine if Young's modulus of the optic nerve (ON) structure as measured by shear-wave elastography can suggest changes in intracranial pressure (ICP) in neuro-critical care patients. MATERIALS AND METHODS: Thirty-one healthy volunteers and twenty-two neuro-critical care patients were enrolled. ON sheath (ONS) diameter (ONSD) values and Young's modulus measurements of volunteers were collected in a calm state and during a Valsalva maneuver (VM). Ultrasound measurements and ICP values of patients were collected on operation day and at 24 and 48 h after the operation; measurements were thereafter assigned to three groups: severely elevated (ICP greater than 22 mmHg), mildly elevated (ICP = 14-22 mmHg), and normal (ICP ≤ 13 mmHg). RESULTS: ONSD and Young's modulus for the ON and ONS of volunteers during VM were higher than those in the calm state (all P < 0.001). In contrast to ONSD, Young's modulus for ON and ONS did not correlate with age, body mass index, or sex. The best cutoff values of Young's modulus for ON for predicting elevated and severely elevated ICP were 16.67 kPa and 22.74 kPa, respectively. Accordingly, the sensitivity values were 96.7% and 88.9%, and the specificity values were 86.1% and 73.7%, which had the same diagnostic performance as ONSD. CONCLUSION: Young's modulus of the ON accurately reflects changes in ICP. It is not confounded by age, sex, or body mass index compared to ONSD.

Anterior precuneus related to the recovery of consciousness.

The neural mechanism that enables the recovery of consciousness in patients with unresponsive wakefulness syndrome (UWS) remains unclear. The aim of the current study is to characterize the cortical hub regions related to the recovery of consciousness. In the current fMRI study, voxel-wise degree centrality analysis was adopted to identify the cortical hubs related to the recovery of consciousness, for which a total of 27 UWS patients were recruited, including 13 patients who emerged from UWS (UWS-E), and 14 patients who remained in UWS (UWS-R) at least three months after the experiment performance. Furthermore, other recoverable unconscious states were adopted as validation groups, including three independent N3 sleep datasets (n = 12, 9, 9 respectively) and three independent anesthesia datasets (n = 27, 14, 6 respectively). Spatial similarity of the hub characteristic with the validation groups between the UWS-E and UWS-R was compared using the dice coefficient. Finally, with the cortical regions persistently shown as hubs across UWS-E and validation states, functional connectivity analysis was further performed to explore the connectivity patterns underlying the recovery of consciousness. The results identified four cortical hubs in the UWS-E, which showed significantly higher degree centrality for UWS-E than UWS-R, including the anterior precuneus, left inferior parietal lobule, left inferior frontal gyrus, and left middle frontal gyrus, of which the degree centrality value also positively correlated with the patients' Glasgow Outcome Scale (GOS) score that assessed global brain functioning outcome after a brain injury. Furthermore, the anterior precuneus was found with significantly higher similarity of hub characteristics as well as functional connectivity patterns between UWS-E and the validation groups. The results suggest that the recovery of consciousness may be relevant to the integrity of cortical hubs in the recoverable unconscious states, especially the anterior precuneus. The identified cortical hub regions could serve as potential treatment targets for patients with UWS.