Ameliorating parkinsonian motor dysfunction by targeting histamine receptors in entopeduncular nucleus-thalamus circuitry.

In Parkinson's disease (PD), reduced dopamine levels in the basal ganglia have been associated with altered neuronal firing and motor dysfunction. It remains unclear whether the altered firing rate or pattern of basal ganglia neurons leads to parkinsonism-associated motor dysfunction. In the present study, we show that increased histaminergic innervation of the entopeduncular nucleus (EPN) in the mouse model of PD leads to activation of EPN parvalbumin (PV) neurons projecting to the thalamic motor nucleus via hyperpolarization-activated cyclic nucleotide-gated (HCN) channels coupled to postsynaptic H2R. Simultaneously, this effect is negatively regulated by presynaptic H3R activation in subthalamic nucleus (STN) glutamatergic neurons projecting to the EPN. Notably, the activation of both types of receptors ameliorates parkinsonism-associated motor dysfunction. Pharmacological activation of H2R or genetic upregulation of HCN2 in EPNPV neurons, which reduce neuronal burst firing, ameliorates parkinsonism-associated motor dysfunction independent of changes in the neuronal firing rate. In addition, optogenetic inhibition of EPNPV neurons and pharmacological activation or genetic upregulation of H3R in EPN-projecting STNGlu neurons ameliorate parkinsonism-associated motor dysfunction by reducing the firing rate rather than altering the firing pattern of EPNPV neurons. Thus, although a reduced firing rate and more regular firing pattern of EPNPV neurons correlate with amelioration in parkinsonism-associated motor dysfunction, the firing pattern appears to be more critical in this context. These results also confirm that targeting H2R and its downstream HCN2 channel in EPNPV neurons and H3R in EPN-projecting STNGlu neurons may represent potential therapeutic strategies for the clinical treatment of parkinsonism-associated motor dysfunction.

Shared and malignancy-specific functional plasticity of dynamic brain properties for patients with left frontal glioma.

The time-varying brain activity may parallel the disease progression of cerebral glioma. Assessment of brain dynamics would better characterize the pathological profile of glioma and the relevant functional remodeling. This study aims to investigate the dynamic properties of functional networks based on sliding-window approach for patients with left frontal glioma. The generalized functional plasticity due to glioma was characterized by reduced dynamic amplitude of low-frequency fluctuation of somatosensory networks, reduced dynamic functional connectivity between homotopic regions mainly involving dorsal attention network and subcortical nuclei, and enhanced subcortical dynamic functional connectivity. Malignancy-specific functional remodeling featured a chaotic modification of dynamic amplitude of low-frequency fluctuation and dynamic functional connectivity for low-grade gliomas, and attenuated dynamic functional connectivity of the intrahemispheric cortico-subcortical connections and reduced dynamic amplitude of low-frequency fluctuation of the bilateral caudate for high-grade gliomas. Network dynamic activity was clustered into four distinct configuration states. The occurrence and dwell time of the weakly connected state were reduced in patients' brains. Support vector machine model combined with predictive dynamic features achieved an averaged accuracy of 87.9% in distinguishing low- and high-grade gliomas. In conclusion, dynamic network properties are highly predictive of the malignant grade of gliomas, thus could serve as new biomarkers for disease characterization.

Role of HCN channels in the functions of basal ganglia and Parkinson's disease.

Parkinson's disease (PD) is a motor disorder resulting from dopaminergic neuron degeneration in the substantia nigra caused by age, genetics, and environment. The disease severely impacts a patient's quality of life and can even be life-threatening. The hyperpolarization-activated cyclic nucleotide-gated (HCN) channel is a member of the HCN1-4 gene family and is widely expressed in basal ganglia nuclei. The hyperpolarization-activated current mediated by the HCN channel has a distinct impact on neuronal excitability and rhythmic activity associated with PD pathogenesis, as it affects the firing activity, including both firing rate and firing pattern, of neurons in the basal ganglia nuclei. This review aims to comprehensively understand the characteristics of HCN channels by summarizing their regulatory role in neuronal firing activity of the basal ganglia nuclei. Furthermore, the distribution and characteristics of HCN channels in each nucleus of the basal ganglia group and their effect on PD symptoms through modulating neuronal electrical activity are discussed. Since the roles of the substantia nigra pars compacta and reticulata, as well as globus pallidus externus and internus, are distinct in the basal ganglia circuit, they are individually described. Lastly, this investigation briefly highlights that the HCN channel expressed on microglia plays a role in the pathological process of PD by affecting the neuroinflammatory response.

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.

Increased number and domain of interlaminar astrocytes in layer I of the temporal cortex in epilepsy.

AIM: The functions of the interlaminar astrocytes in layer I of the human cortex are currently unknown. Here, we aimed to explore whether there is any morphological remodelling of interlaminar astrocytes in layer I of the temporal cortex in epilepsy. METHODS: Tissues were obtained from 17 epilepsy surgery patients and 17 post-mortem age-matched controls. In addition, 10 Alzheimer's disease (AD) patients and 10 age-matched controls were used as the disease control group. Paraffin sections (6 µm) and frozen sections (35 or 150 µm) of inferior temporal gyrus tissue were used for immunohistochemistry. With the use of tissue transparency, 3D reconstruction and hierarchical clustering, we performed a quantitative morphological analysis of astrocytes. RESULTS: Upper and lower zones were identified in layer I of the human cortex. Compared with the astrocytes in layers IV-V, layer I interlaminar astrocytes occupied a significantly smaller volume and exhibited shorter and fewer process intersections. Increased Chaslin's gliosis (consisting of types I and II subpial interlaminar astrocytes) and number of glial fibrillary acidic protein (GFAP)-immunoreactive interlaminar astrocytes in layer I of the temporal cortex were confirmed in patients with epilepsy. There was no difference in the number of interlaminar astrocytes in layer I between AD and age-matched control groups. Using tissue transparency and 3D reconstruction technology, the astrocyte domain in the human temporal cortex was classified into four clusters, among which the interlaminar astrocytes in cluster II were more abundant in epilepsy, showing specific topological structures in patients with epilepsy. Furthermore, there was a significant increase in the astrocyte domain of interlaminar cells in layer I of the temporal cortex in patients with epilepsy. CONCLUSION: The observed significant astrocytic structural remodelling in the temporal cortex of epilepsy patients showed that the astrocyte domain in layer I may play an important role in temporal lobe epilepsy.

Neuroprotective effect of a Keap1-Nrf2 Protein-Protein Inter-action inhibitor on cerebral Ischemia/Reperfusion injury.

Oxidative stress has been confirmed to be closely related to the occurrence and development of cerebral ischemic/reperfusion (I/R). The Keap1-Nrf2 pathway is widely recognized as a defensive system to maintain cellular redox homeostasis. Targeting Keap1-Nrf2 interaction by small molecules to release Nrf2 should be a promising strategy to treat cerebral I/R injury. The piperazinyl-naphthalenesulfonamide 6 K was reported to be a Keap1-Nrf2 protein-protein interaction inhibitor, showing promising antioxidative effect. Herein, this study is to investigate whether 6 K could prevent brain from I/R injury. The related mechanism of oxidative stress was also elucidated using in vivo mice middle cerebral artery occlusion (MCAO) model and in vitro SH-SY5Y oxygen-glucose deprivation/reperfusion (OGD/R) model. The results indicated that treatment of 6 K markedly decreased infarct volume, apoptotic neurons and oxidative damage and promoted neurologic recovery in vivo. The cell model revealed that the reactive oxygen species (ROS) was decreased, and cell viability was increased. Western blots and immunofluorescence staining demonstrated that compound treatment promoted Nrf2 release and nuclear translocation. The downstream protective enzymes were significantly enhanced at both in vivo and in vitro levels. Collectively, 6 K is a promising protective agent against cerebral I/R injury through activation of Nrf2 to suppress oxidative stress.

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.

A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.

Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratification of gliomas. Herein, we established three separate case cohorts, consisting of 655 glioma patients, and carried out a retrospective study. Image and clinical data of three cohorts were used for training (N = 188), cross-validation (N = 411), and independent testing (N = 56) of the IS model. All tumors were segmented from magnetic resonance (MR) images by the 3D U-net, followed by extraction of high-throughput network features, which were referred to as IS. IS was then used to perform noninvasive histopathological diagnosis and molecular subtyping. Moreover, a new IS-based clustering method was applied for prognostic stratification in IDH-wild-type lower-grade glioma (IDHwt LGG) and triple-negative glioblastoma (1p19q retain/IDH wild-type/TERTp-wild-type GBM). The average accuracies of histological diagnosis and molecular subtyping were 89.8 and 86.1% in the cross-validation cohort, while these numbers reached 83.9 and 80.4% in the independent testing cohort. IS-based clustering method was demonstrated to successfully divide IDHwt LGG into two subgroups with distinct median overall survival time (48.63 vs 38.27 months respectively, P = 0.023), and two subgroups in triple-negative GBM with different median OS time (36.8 vs 18.2 months respectively, P = 0.013). Our findings demonstrate that our novel IS-based radiomics model is an effective tool to achieve noninvasive histo-molecular pathological diagnosis and prognostic stratification of gliomas. This IS model shows potential for future routine use in clinical practice.

Abscisic Acid-Induced Autophagy Selectively via MAPK/JNK Signalling Pathway in Glioblastoma.

As a widely known plant hormone, Abscisic acid plays an important role in the progress of planting cell and their stress response. Recently, we reported that ABA might play an anti-cancer role in glioma tissues. In the present study, the molecular mechanism of ABA anti-cancer was further explored in glioblastoma cells. By measuring LC3 puncta formation and conversion in glioblastoma cells, inhibiting the autophagic pathway, targeting the essential autophagic modulator beclin 1 with RNA interference, and analysing cellular morphology via transmission electron microscopy, we found that ABA-treated glioblastoma cells exhibited the features of autophagy. Specifically, ABA-induced autophagy in glioblastoma cells was mediated by the MAPK/JNK signalling pathway rather than the PI3K/AKT/mTOR axis. Moreover, the inhibition or knockdown of JNK specifically blocked ABA-induced autophagic cell death. ABA-induced autophagy was further confirmed in tumour-bearing mice and was accompanied by the inhibition of glioma growth in vivo. This report is the first to describe autophagy induced by ABA and mediated by the MAPK/JNK pathway in human cancer cells and tumour-bearing mice. These results may shed some light in new therapeutic strategies of glioma.

Markov State Models and Molecular Dynamics Simulations Provide Understanding of the Nucleotide-Dependent Dimerization-Based Activation of LRRK2 ROC Domain.

Mutations in leucine-rich repeat kinase 2 (LRRK2) are recognized as the most frequent cause of Parkinson's disease (PD). As a multidomain ROCO protein, LRRK2 is characterized by the presence of both a Ras-of-complex (ROC) GTPase domain and a kinase domain connected through the C-terminal of an ROC domain (COR). The bienzymatic ROC-COR-kinase catalytic triad indicated the potential role of GTPase domain in regulating kinase activity. However, as a functional GTPase, the detailed intrinsic regulation of the ROC activation cycle remains poorly understood. Here, combining extensive molecular dynamics simulations and Markov state models, we disclosed the dynamic structural rearrangement of ROC's homodimer during nucleotide turnover. Our study revealed the coupling between dimerization extent and nucleotide-binding state, indicating a nucleotide-dependent dimerization-based activation scheme adopted by ROC GTPase. Furthermore, inspired by the well-known R1441C/G/H PD-relevant mutations within the ROC domain, we illuminated the potential allosteric molecular mechanism for its pathogenetic effects through enabling faster interconversion between inactive and active states, thus trapping ROC in a prolonged activated state, while the implicated allostery could provide further guidance for identification of regulatory allosteric pockets on the ROC complex. Our investigations illuminated the thermodynamics and kinetics of ROC homodimer during nucleotide-dependent activation for the first time and provided guidance for further exploiting ROC as therapeutic targets for controlling LRRK2 functionality in PD treatment.

TRIM28 as an independent prognostic marker plays critical roles in glioma progression.

Tripartite motif (TRIM) proteins are involved in tumorigenesis. Here, we examined the expression, biological function, and clinical significance of tripartite motif containing 28 (TRIM28) in glioma, a locally aggressive brain tumor. First, TRIM28 expression was significantly higher in glioma (n = 138) than in non-glioma controls (n = 6). TRIM28 expression was positively correlated with tumor malignancy, and associated with poor overall survival (OS) and progression-free survival (PFS). Notably, TRIM28 expression was negatively correlated with p21 expression in patients with glioblastoma multiforme (GBM). A multivariate analysis that included relevant measures indicated that high TRIM28 expression is an independent prognostic factor for poor OS and PFS in GBM patients. In experiments with cultured glioma cells, down-regulating TRIM28 with shRNA increased p21 expression, and induced cell cycle arrest at the G1 phase. In a xenograft model, down-regulating TRIM28 suppressed tumor growth. These results indicate that over-expression of TRIM28 is associated with poor outcome in glioma patients.

miR-491 regulates glioma cells proliferation by targeting TRIM28 in vitro.

BACKGROUND: MicroRNAs are significantly involved in tumorigenesis and progression of glioma. However, the critical part they play in glioma have not been fully elaborated. miR-491 and Tripartite motif containing 28 (TRIM28) are reported to aberrantly express in glioblastoma multiforme (GBM). Here, we detected miR-491 and TRIM28 expression and function in glioma cells. METHODS: We analyzed miR-491 expressions in 20 primary human GBM tissues and 6 control brain tissues by qRT-PCR assays and searched for The Cancer Genome Atlas (TCGA) database. Then we predicted possible mRNA target of miR-491 by TargetScan/MicroRNA and confirmed it via luciferase reporter assays. Knock-down of miR-491 and transfection of pLenti-TRIM28 were performed in U251 and U87 cells. Proliferation ability was examined by MTT and clone formation assays. RESULTS: miR-491 expression was obviously reduced in GBM cells and tissues. There was a positive correlation between the down-regulation of miR-491 and poor prognosis. Spearman's correlation analysis demonstrated that miR-491 expression was negatively correlated with TRIM28 protein level. Possible mRNA binding sites of miR-491 predicted by TargetScan/MicroRNA were proved by luciferase assays. Clone formation and MTT assays indicated that up-regulation of miR-491 inhibited the proliferation of glioma cells. CONCLUSIONS: miR-491 regulates glioma cells proliferation in vitro by targeting TRIM28.

Role of the globus pallidus in motor and non-motor symptoms of Parkinson's disease.

ABSTRACT: The globus pallidus plays a pivotal role In the basal ganglia circuit. Parkinson's disease Is characterized by degeneration of dopamine-producing cells in the substantia nigra, which leads to dopamine deficiency in the brain that subsequently manifests as various motor and non-motor symptoms. This review aims to summarize the involvement of the globus pallidus in both motor and non-motor manifestations of Parkinson's disease. The firing activities of parvalbumin neurons in the medial globus pallidus, including both the firing rate and pattern, exhibit strong correlations with the bradykinesia and rigidity associated with Parkinson's disease. Increased beta oscillations, which are highly correlated with bradykinesia and rigidity, are regulated by the lateral globus pallidus. Furthermore, bradykinesia and rigidity are strongly linked to the loss of dopaminergic projections within the cortical-basal ganglia-thalamocortical loop. Resting tremors are attributed to the transmission of pathological signals from the basal ganglia through the motor cortex to the cerebellum-ventral intermediate nucleus circuit. The cortico-striato-pallidal loop is responsible for mediating pallidi-associated sleep disorders. Medication and deep brain stimulation are the primary therapeutic strategies addressing the globus pallidus in Parkinson's disease. Medication is the primary treatment for motor symptoms in the early stages of Parkinson's disease, while deep brain stimulation has been clinically proven to be effective in alleviating symptoms in patients with advanced Parkinson's disease, particularly for the movement disorders caused by levodopa. Deep brain stimulation targeting the globus pallidus internus can improve motor function in patients with tremordominant and non-tremor-dominant Parkinson's disease, while deep brain stimulation targeting the globus pallidus externus can alter the temporal pattern of neural activity throughout the basal ganglia-thalamus network. Therefore, the composition of the globus pallidus neurons, the neurotransmitters that act on them, their electrical activity, and the neural circuits they form can guide the search for new multi-target drugs to treat Parkinson's disease in clinical practice. Examining the potential intra-nuclear and neural circuit mechanisms of deep brain stimulation associated with the globus pallidus can facilitate the management of both motor and non-motor symptoms while minimizing the side effects caused by deep brain stimulation.

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 .

Comprehensive analysis reveals that LTBR is a immune-related biomarker for glioma.

Glioma is a common malignant brain tumor with great heterogeneity and huge difference in clinical outcomes. Although lymphotoxin (LT) beta receptor (LTBR) has been linked to immune system and response development for decades, the expression and function in glioma have not been investigated. To confirm the expression profile of LTBR, integrated RNA-seq data from glioma and normal brain tissues were analyzed. Functional enrichment analysis, TMEscore analysis, immune infiltration, the correlation of LTBR with immune checkpoints and ferroptosis, and scRNAseq data analysis in gliomas were in turn performed, which pointed out that LTBR was pertinent to immune functions of macrophages in gliomas. In addition, after being trained and validated in the tissue samples of the integrated dataset, an LTBR DNA methylation-based prediction model succeeded to distinguish gliomas from non-gliomas, as well as the grades of glioma. Moreover, by virtue of the candidate LTBR CpG sites, a prognostic risk-score model was finally constructed to guide the chemotherapy, radiotherapy, and immunotherapy for glioma patients. Taken together, LTBR is closely correlated with immune functions in gliomas, and LTBR DNA methylation could serve as a biomarker for diagnosis and prognosis of gliomas.

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.

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.

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.