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Ischemic stroke (IS) results in the interruption of blood flow to the brain, which can cause significant damage. The pathophysiological mechanisms of IS include ionic imbalances, oxidative stress, neuroinflammation, and impairment of brain barriers. Brain barriers, such as the blood-brain barrier (BBB) and the blood-cerebrospinal fluid (CSF) barrier (B-CSF), protect the brain from harmful substances by regulating the neurochemical environment. Although the BBB is widely recognized for its crucial role in protecting the brain and its involvement in conditions such as stroke, the B-CSF requires further study. The B-CSF plays a fundamental role in regulating the CSF environment and maintaining the homeostasis of the central nervous system (CNS). However, the impact of B-CSF impairment during pathological events such as IS is not yet fully understood. In conditions like IS and other neurological disorders, the B-CSF can become compromised, allowing the entry of inflammatory substances and increasing neuronal damage. Understanding and preserving the integrity of the B-CSF are crucial for mitigating damage and facilitating recovery after ischemic stroke, highlighting its fundamental role in regulating the CNS during adverse neurological conditions.

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BACKGROUND: Acute encephalopathy with biphasic seizures and late reduced diffusion (AESD) develops along with status epilepticus and widespread subcortical white matter edema. We aimed to evaluate the epileptic foci and networks in two patients with epilepsy after AESD using simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI). METHODS: Statistically significant blood oxygen level-dependent (BOLD) responses related to interictal epileptiform discharges (IEDs) were analyzed using an event-related design of hemodynamic response functions with multiple peaks. RESULTS: Patient 1 developed focal seizures at age 10 years, one year after AESD onset. Positive BOLD changes were observed in the bilateral frontotemporal lobes, left parietal lobe, and left insula. BOLD changes were also observed in the subcortical structures. Patient 2 developed epileptic spasms at age two years, one month after AESD onset. Following total corpus callosotomy (CC) at age three years, the epileptic spasms resolved, and neurodevelopmental improvement was observed. Before CC, positive BOLD changes were observed bilaterally in the frontotemporal lobes. BOLD changes were also observed in the subcortical structures. After CC, the positive BOLD changes were localized in the temporal lobe ipsilateral to the IEDs, and the negative BOLD changes were mainly in the cortex and subcortical structures of the hemisphere ipsilateral to IEDs. CONCLUSION: EEG-fMRI revealed multiple epileptic foci and extensive epileptic networks, including subcortical structures in two cases with post-AESD epilepsy. CC may be effective in disconnecting the bilaterally synchronous epileptic networks of epileptic spasms after AESD, and pre-and post-operative changes in EEG-fMRI may reflect improvements in epileptic symptoms.

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Our intricate social brain is implicated in a range of brain disorders, where social dysfunction emerges as a common neuropsychiatric feature cutting across diagnostic boundaries. Understanding the neurocircuitry underlying social dysfunction and exploring avenues for its restoration could present a transformative and transdiagnostic approach to overcoming therapeutic challenges in these disorders. The brain's default mode network (DMN) plays a crucial role in social functioning and is implicated in various neuropsychiatric conditions. By thoroughly examining the current understanding of DMN functionality, we propose that the DMN integrates diverse social processes, and disruptions in brain communication at regional and network levels due to disease hinder the seamless integration of these social functionalities. Consequently, this leads to an altered balance between self-referential and attentional processes, alongside a compromised ability to adapt to social contexts and anticipate future social interactions. Looking ahead, we explore how adopting an integrated neurocircuitry perspective on social dysfunction could pave the way for innovative therapeutic approaches to address brain disorders.

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STriatal-Enriched protein tyrosine Phosphatase (STEP) is a brain-specific tyrosine phosphatase that is associated with numerous neurological and neuropsychiatric disorders. STEP dephosphorylates and inactivates various kinases and phosphatases critical for neuronal function and health including Fyn, Pyk2, ERK1/2, p38, and PTPα. Importantly, STEP dephosphorylates NMDA and AMPA receptors, two major glutamate receptors that mediate fast excitatory synaptic transmission. This STEP-mediated dephosphorylation leads to their internalization and inhibits both Hebbian synaptic potentiation and homeostatic synaptic scaling. Hence, STEP has been widely accepted to weaken excitatory synaptic strength. However, emerging evidence implicates a novel role of STEP in neuronal hyperexcitability and seizure disorders. Genetic deletion and pharmacological blockade of STEP reduces seizure susceptibility in acute seizure mouse models and audiogenic seizures in a mouse model of Fragile X syndrome. Pharmacologic inhibition of STEP also decreases hippocampal activity and neuronal intrinsic excitability. Here, we will highlight the divergent roles of STEP in excitatory synaptic transmission and neuronal intrinsic excitability, present the potential underlying mechanisms, and discuss their impact on STEP-associated neurologic and neuropsychiatric disorders.

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SUMMARY: In the 2021 version of the Standardized Critical Care EEG Terminology, the American Clinical Neurophysiology Society introduced new definitions, including for the cyclic alternating pattern of encephalopathy (CAPE). CAPE refers to changes in background EEG activity, with two patterns alternating spontaneously in a regular manner. CAPE shares remarkable similarities with the cyclic alternating pattern, a natural EEG phenomenon occurring in normal non-rapid eye movement sleep, considered the main electrophysiological biomarker of sleep instability. This review explores similarities and differences between cyclic alternating pattern and CAPE and, leveraging the existing expertise on cyclic alternating pattern, aims to extend knowledge on CAPE. A standardized assessment of CAPE features is key to ascertain its prevalence and clinical significance among critically ill patients and to encompass the impact of confounding factors such as anesthetic and sedative agents. Although the preservation of non-rapid eye movement sleep-related elements has a well-known prognostic value in the critical care setting, the clinical importance of cyclic oscillating patterns and the prognostic significance of CAPE remain to be elucidated.

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Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. While brain connections are typically delineated by channel signal similarity, there lacks a consistent and reliable strategy for ascertaining node characteristics. Conventional node features such as temporal and frequency domain properties of EEG signals prove inadequate for capturing the extensive EEG information. In our investigation, we introduce a novel adaptive method for extracting node features from EEG signals utilizing a distinctive task-induced self-supervised learning technique. By amalgamating these extracted node features with fundamental edge features constructed using Pearson correlation coefficients, we showed that the proposed approach can function as a plug-in module that can be integrated to many common GNN networks (e.g., GCN, GraphSAGE, GAT) as a replacement of node feature selections module. Comprehensive experiments are then conducted to demonstrate the consistently superior performance and high generality of the proposed method over other feature selection methods in various of brain disorder prediction tasks, such as depression, schizophrenia, and Parkinson's disease. Furthermore, compared to other node features, our approach unveils profound spatial patterns through graph pooling and structural learning, shedding light on pivotal brain regions influencing various brain disorder prediction based on derived features.

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The integrated stress response (ISR) is a highly conserved biochemical pathway that regulates protein synthesis. The ISR is activated in response to diverse stressors to restore cellular homeostasis. As such, the ISR is implicated in a wide range of diseases, including brain disorders. However, in the brain, the ISR also has potent influence on processes beyond proteostasis, namely synaptic plasticity, learning and memory. Thus, in the setting of brain diseases, ISR activity may have dual effects on proteostasis and synaptic function. In this review, we consider the ISR's contribution to brain disorders through the lens of its potential effects on synaptic plasticity. From these examples, we illustrate that at times ISR activity may be a "double-edged sword". We also highlight its potential as a therapeutic target to improve circuit function in brain diseases independent of its role in disease pathogenesis.

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BACKGROUND: As physical signals, mechanical cues regulate the neural cells in the brain. The mechanosensitive channels (MSCs) perceive the mechanical cues and transduce them by permeating specific ions or molecules across the plasma membrane, and finally trigger a series of intracellular bioelectrical and biochemical signals. Emerging evidence supports that wide-distributed, high-expressed MSCs like Piezo1 play important roles in several neurophysiological processes and neurological disorders. AIMS: To systematically conclude the functions of MSCs in the brain and provide a novel mechanobiological perspective for brain diseases. METHOD: We summarized the mechanical cues and MSCs detected in the brain and the research progress on the functional roles of MSCs in physiological conditions. We then concluded the pathological activation and downstream pathways triggered by MSCs in two categories of brain diseases, neurodegenerative diseases and place-occupying damages. Finally, we outlined the methods for manipulating MSCs and discussed their medical potential with some crucial outstanding issues. RESULTS: The MSCs present underlying common mechanisms in different brain diseases by acting as the "transportation hubs" to transduce the distinct signal patterns: the upstream mechanical cues and the downstream intracellular pathways. Manipulating the MSCs is feasible to alter the complicated downstream processes, providing them promising targets for clinical treatment. CONCLUSIONS: Recent research on MSCs provides a novel insight into brain diseases. The common mechanisms mediated by MSCs inspire a wide range of therapeutic potentials targeted on MSCs in different brain diseases.

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BACKGROUND: To compare the amplitude-integrated electroencephalography (aEEG) monitoring (short-term versus prolonged-period) for neonatal seizure detection and outcome. METHODS: The aEEG monitoring in a historical cohort (n = 88, preterm:42, and term:46) with neonatal encephalopathy between 2010-2022 was re-evaluated for neonatal seizures (electrographic, electro-clinical, and clinical seizures) and EEG background scoring. The cohort was dichotomized: group I (short-period with 6-12 h, n = 36) and group II (prolonged-period with 24-48 h, n = 52). Both monitoring types were evaluated for the diagnostic accuracy of the "patients with seizures" and for outcome characteristics (early death as well as adverse outcomes at 12 months of age). RESULTS: A total of 67 (76 %) neonates of the cohort were diagnosed as "patients with seizures": electrographic-only seizures in 10 (15 %), electro-clinical seizures in 22 (33 %), and clinical-only seizures in 35 (52 %). The aEEG provides the "patients with seizures" in neonates with a 36.5 % rate with both types of monitoring: 17/36 (47.2 %) with short-term and 15/52 (28.8 %) with prolonged-period monitoring. The prolonged period aEEG had higher diagnostic values for seizure detection (sensitivity = 0.73 and negative predictivity value = 0.81). However, the aEEG background scores were similar for both types of aEEG monitoring, respectively (the mean ± SD: 4.73 ± 2.9 versus 4.4 ± 4. p = 0.837). The aEEG scoring was correlated with the magnitude of brain injury documented with MRI, the early death, and the adverse outcome at 12 months of age. CONCLUSIONS: Both aEEG types are valuable for monitoring the "patients with seizures" and outcome characteristics.

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Subiculum is a pivotal output component of the hippocampal formation, a structure often overlooked in neuroscientific research. Here, this review aims to explore the role of the subiculum in various brain disorders, shedding light on its significance within the functional-neuroanatomical perspective on neurological diseases. The subiculum's involvement in multiple brain disorders was thoroughly examined. In Alzheimer's disease, subiculum alterations precede cognitive decline, while in epilepsy, the subiculum plays a critical role in seizure initiation. Stress involves the subiculum's impact on the hypothalamic-pituitary-adrenocortical axis. Moreover, the subiculum exhibits structural and functional changes in anxiety, schizophrenia, and Parkinson's disease, contributing to cognitive deficits. Bipolar disorder is linked to subiculum structural abnormalities, while autism spectrum disorder reveals an alteration of inward deformation in the subiculum. Lastly, frontotemporal dementia shows volumetric differences in the subiculum, emphasizing its contribution to the disorder's complexity. Taken together, this review consolidates existing knowledge on the subiculum's role in brain disorders, and may facilitate future research, diagnostic strategies, and therapeutic interventions for various neurological conditions.

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The continually advancing landscape of neuroscientific and imaging research has broadened our comprehension of sex differences encoded in the human brain, expanding from the hypothalamus and sexual behaviour to encompass the entire brain, including its diverse lobes, structures, and functions. However, less is known about sex differences in the brains of neonates and infants, despite their relevance to various sex-linked diseases that develop early in life. In this review, we provide a synopsis of the literature evidence on sex differences in the brains of neonates and infants at the morphological, structural and network levels. We also briefly overview the present evidence on the sex bias in some brain disorders affecting infants and neonates.

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Fragile X messenger ribonucleoprotein 1 (FMRP) is a widely expressed RNA binding protein involved in several steps of mRNA metabolism. Mutations in the FMR1 gene encoding FMRP are responsible for fragile X syndrome (FXS), a leading genetic cause of intellectual disability and autism spectrum disorder, and fragile X-associated tremor-ataxia syndrome (FXTAS), a neurodegenerative disorder in aging men. Although FMRP is mainly expressed in neurons, it is also present in glial cells and its deficiency or altered expression can affect functions of glial cells with implications for the pathophysiology of brain disorders. The present review focuses on recent advances on the role of glial subtypes, astrocytes, oligodendrocytes and microglia, in the pathophysiology of FXS and FXTAS, and describes how the absence or reduced expression of FMRP in these cells can impact on glial and neuronal functions. We will also briefly address the role of FMRP in radial glial cells and its effects on neural development, and gliomas and will speculate on the role of glial FMRP in other brain disorders.

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OBJECTIVE: We investigated how electroencephalography (EEG) quantitative measures and dysglycemia relate to neurodevelopmental outcomes following neonatal encephalopathy (NE). METHODS: This retrospective study included 90 neonates with encephalopathy who received therapeutic hypothermia. EEG absolute spectral power was calculated during post-rewarming and 2-month follow-up. Measures of dysglycemia (hypoglycemia, hyperglycemia, and glycemic lability) and glucose variability were computed for the first 48 h of life. We evaluated the ability of EEG and glucose measures to predict neurodevelopmental outcomes at ≥ 18 months, using logistic regressions (with area under the receiver operating characteristic [AUROC] curves). RESULTS: The post-rewarming global delta power (average all electrodes), hyperglycemia and glycemic lability predicted moderate/severe neurodevelopmental outcome separately (AUROC = 0.8, 95%CI [0.7,0.9], p < .001) and even more so when combined (AUROC = 0.9, 95%CI [0.8,0.9], p < .001). After adjusting for NE severity and magnetic resonance imaging (MRI) brain injury, only global delta power remained significantly associated with moderate/severe neurodevelopmental outcome (odds ratio [OR] = 0.9, 95%CI [0.8,1.0], p = .04), gross motor delay (OR = 0.9, 95%CI [0.8,1.0], p = .04), global developmental delay (OR = 0.9, 95%CI [0.8,1.0], p = .04), and auditory deficits (OR = 0.9, 95%CI [0.8,1.0], p = .03). CONCLUSIONS: In NE, global delta power post-rewarming was predictive of outcomes at ≥ 18 months. SIGNIFICANCE: EEG markers post-rewarming can aid prediction of neurodevelopmental outcomes following NE.

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Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

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Electroencephalogram (EEG) is an important biomarker for neonatal encephalopathy (NE) and has significant predictive value for brain injury and neurodevelopmental outcomes. Quantitative analysis of EEG involves the representation of complex EEG data in an objective, reproducible and scalable manner. Quantitative EEG (qEEG) can be derived from both a limited channel EEG (as available during amplitude integrated EEG) and multi-channel conventional EEG. It has the potential to enable bedside clinicians to monitor and evaluate details of cortical function without the necessity of continuous expert input. This is particularly useful in NE, a dynamic and evolving condition. In these infants, continuous, detailed evaluation of cortical function at the bedside is a valuable aide to management especially in the current era of therapeutic hypothermia and possible upcoming neuroprotective therapies. This review discusses the role of qEEG in newborns with NE and its use in informing monitoring and therapy, along with its ability to predict imaging changes and short and long-term neurodevelopmental outcomes. IMPACT: Quantitative representation of EEG data brings the evaluation of continuous brain function, from the neurophysiology lab to the NICU bedside and has a potential role as a biomarker for neonatal encephalopathy. Clinical and research applications of quantitative EEG in the newborn are rapidly evolving and a wider understanding of its utility is valuable. This overview summarizes the role of quantitative EEG at different timepoints, its relevance to management and its predictive value for short- and long-term outcomes in neonatal encephalopathy.

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Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

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