The blood-brain barrier (BBB), while indispensable for maintaining central nervous system (CNS) homeostasis, constitutes the principal impediment to effective therapeutic delivery for neurodegenerative disorders, particularly hindering spatially resolved modulation of extracellular ions and reactive oxygen species (ROS) within the neural microenvironment. Contemporary electrochemical methodologies have emerged as a paradigm shift for dynamically reconciling these dual parameters, thereby enabling targeted neuroregulation. Critical review of this field reveals a distinct evolution from passive physiological interventions to active electrochemical engineering approaches. Current research, however, encounters persistent translational barriers including insufficient spatiotemporal resolution in neural interfaces, incomplete mechanistic understanding of ROS-ionic crosstalk, and scalability limitations of nanoscale delivery systems. To transcend these limitations, the synergistic convergence of electrochemical platforms with machine learning (ML)-guided predictive analytics, near-infrared (NIR) phototherapy, and biocompatible nanocarrier-mediated delivery systems constitutes a strategic imperative in next-generation neurotherapeutic development. Such interdisciplinary convergence is not merely incremental but rather a fundamental prerequisite for realizing clinically translatable neural microenvironment modulation.

The accurate delineation of ischemic stroke lesions in magnetic resonance imaging (MRI) is impeded by heterogeneous lesion morphology and the computational expense of modeling global context in three‑dimensional data. In cerebral infarction assessment, diffusion‑weighted imaging, apparent diffusion coefficient, T2‑weighted imaging and T2star sequences (including susceptibility weighted image processing) each offer complementary information, yet existing fusion strategies often fail to adapt to missing modalities or capture long‑range dependencies efficiently. Here we present GPMNet, a lightweight convolutional framework that integrates an adaptive multimodal feature fusion module-employing dynamic cross‑attention to spatially weight and merge signals from all four MRI sequences-and a gated parallel state‑space module that models global voxel interactions in linear time via dual gated branches. We trained the network end-to-end on the ATLAS R2.0 dataset and our own dataset collected at HuanHu Hospital (Tianjin, China), labeled as HHD. The training used a combined Dice-binary cross-entropy and TOPK10 loss, and the outputs were refined using ensemble inference and connected-domain filtering. GPMNet achieved Dice coefficients of 0.6604 and 0.7171 on the two cohorts respectively, achieving superior results compared to other state-of-the-art algorithms. Moreover, the Grad-CAM-based interpretability analysis confirms that the model's attention corresponds to true ischemic areas across modalities, offering visual evidence of its diagnostic reliability and enhancing the transparency of the segmentation process. Our approach delivered rapid, high‑precision stroke segmentation and establishes a scalable paradigm for resource‑efficient clinical imaging applications.

The existing strategies for spinal cord injury (SCI) repair are restricted by the limitations of experimental models and intervention measures. Although the potential of stem cells, biomaterials, and electromagnetic stimulation has been demonstrated in animal models, their clinical translation has been relatively ineffective. In the present study, using the concepts of biomimetic neural tissue engineering to address the complex spatiotemporal characteristics of injured spinal cord repair, we created a programmable controlled-release bionic spinal cord graft with a topological scaffold composed of silk fibroin and collagen. By employing microfluidic drug-loaded microsphere technology and dual regulation by exosomes and extracellular matrix derived from human stem cells, the graft exhibited sustained release of neurotrophic factors, providing a beneficial regenerative microenvironment for SCI repair. In a mouse T10 3-mm hemisection SCI model, the graft facilitated tissue repair of the injured spinal cord, vascular remodeling, sensory and motor functional reconstruction, bladder function recovery, and the reversal of muscle atrophy. This study presents a new strategy for effective injured spinal cord repair using a programmable controlled-release bionic spinal cord graft, and the results suggest potential for application in humans for spinal cord repair and functional reconstruction.

Transcranial acoustoelectric brain imaging (tABI) is a potential brain activity imaging technique with high spatiotemporal resolution. Precise focus of transcranial ultrasound is critical for realizing millimeter-level spatial resolution in tABI. In this study, a precise focusing simulation platform is proposed by constructing a high-precision 3D skull model from Bama pig computed tomography data and a mathematical model equation by considering the skull’s heterogeneous properties. Then, the delay parameters derived from the simulation platform improve the precision of transcranial ultrasound focusing, enabling precise localization of brain activation sources with tABI. Phantom experimental results demonstrate that the transcranial ultrasound field is precisely focused at the target with a 0.20 mm deviation when delay parameters are obtained from the proposed simulation platform, whereas it exhibits divergence when using delay parameters derived from pure water or homogeneous skull models. Furthermore, using the proposed simulation platform, tABI can accurately identify intracranial electrical signals of distinct frequencies and precisely locate the corresponding activation sources with a spatial deviation of 0.50 mm. These results demonstrate that the proposed simulation platform is a powerful tool for the precise focusing of tABI.

Accurate sleep staging is essential for the diagnosis and evaluation of sleep disorders. However, convolutional neural network (CNN)-based methods are limited in capturing stage-specific sleep waveforms, and classification errors often occur in transitional sleep regions. In addition, existing models do not adequately account for inter-individual variability related to physiological factors such as age and sex. To address these issues, we propose ViSTA-SleepNet, a two-stage, multi-view, multimodal framework for sleep staging.Specifically, data augmentation is applied to sleep stage transition segments to improve boundary learning. For EEG signals, a multi-view representation learning strategy and a cross-view Transformer are used to jointly model raw waveforms, amplitude variations, and temporally structured events, thereby enhancing the detection of key sleep events. EOG and EMG are modeled as complementary modalities and adaptively fused with EEG features. In addition, a feature-wise linear modulation (FiLM) mechanism based on demographic and physiological information is introduced to support individualized representation learning and joint detection of sleep stages, spindles, and slow waves. Finally, a bidirectional gated recurrent unit (Bi-GRU) and conditional random field (CRF) are combined to improve the physiological consistency of sleep stage transitions.Experiments on Sleep-EDF-20, Sleep-EDF-78, and SHHS using strict 20-fold subject-independent cross-validation show that ViSTA-SleepNet achieves accuracies of 88.5%, 84.5%, and 89.6% for sleep staging, respectively, while exceeding 95% accuracy in spindle and slow-wave detection. Visualization results further confirm its advantages in discriminative performance and physiological plausibility.

Background Idiopathic normal pressure hydrocephalus (iNPH) is a reversible disorder characterized by cognitive, gait, and urinary dysfunction. While the cerebrospinal fluid (CSF) tap test can predict surgical response, screening-based cognitive assessment protocols remain undefined, limiting prognostic accuracy. Objectives To delineate temporal patterns of recovery for individual cognitive subdomains after the CSF tap test in patients with iNPH, and to establish high-sensitivity, time-stratified cognitive subdomains to improve preoperative prognostication and surgical decision-making. Design Single-centre retrospective observational study with rigorous inclusion/exclusion criteria. All patients underwent a full series of multidimensional cognitive assessments before and at 24, 48, and 72 hours after the CSF tap test. Cognitive functions were stratified into six standardized subdomains using validated weighted composite scores. All enrolled patients underwent ventriculoperitoneal (VP) shunt surgery and completed 3-month postoperative cognitive follow-up. Longitudinal changes were analysed using repeated-measures ANOVA, linear mixed models, and receiver operating characteristic curve analysis. Methods In this study, 53 patients with iNPH were included. Cognitive assessments were performed consistently across all time points using the Mini-Mental State Examination (MMSE), Animal Fluency Test (AFT), Trail Making Test (TMT and TMT-A), and Clock Drawing Test (CDT). Quantitative composite scores for each cognitive subdomain were computed and analysed longitudinally. Results Stratified analyses showed that language ability at 24 hours (P < 0.01), orientation ability at 48 hours (P < 0.01), and recall combined with attention and calculation at 72 hours (recall: P < 0.001; attention and calculation: P < 0.01) were the most sensitive and predictive for assessing the effects of the CSF tap test across time points. All enrolled patients underwent VP shunt surgery after multi-index assessment. These improvements correlated with 3-month postoperative cognitive benefits. Conclusions A time-stratified, subdomain-specific cognitive assessment protocol enhances tap test sensitivity, reduces misclassification, and supports personalized surgical decision-making for patients with iNPH.

Abstract Spatial cognition is a key ability of human cognition and intelligence. In this study, we validated the feasibility and effectiveness of origami training in enhancing spatial cognition and elucidated the underlying neural mechanisms. We assigned participants to either an origami group or a control group, with the origami group completing a training program. We collected electroencephalography (EEG) signals and eye movement data during the spatial tasks pre‐, during‐, and post‐training. A cognitive questionnaire was also collected. We then compared event‐related synchronization and event‐related desynchronization in different bands and constructed weighted Phase Lag Index brain network maps. We also analyzed eye‐tracking metrics. Origami training enhanced cognitive performance, improving accuracy and reducing response time. The origami training increased the frontal midline θ power and decreased the parietal α power. The origami training modulated brain connectivity differently across tasks. Eye‐tracking data revealed a reduction in cognitive load, increased focus, and more efficient cognitive processing following the training. The frontal, parieto‐occipital, and frontal‐occipital regions actively contribute to spatial cognition. Origami training enhances spatial cognition by re‐shaping the brain networks and functional connectivity. These findings support the development of a portable and cost‐effective digital therapy for neurodegenerative disorders.

Stereotactic electroencephalography (sEEG) provides temporally precise intracranial recordings but is inherently constrained by sparse and irregular spatial sampling due to clinical limitations on electrode implantation. Signal reconstruction under this setting aims to infer neural activity at unmonitored locations, potentially expanding the coverage of neural recordings without increasing the number of implanted electrodes. However, most existing sEEG reconstruction methods underutilize the spatial information of electrode contacts in both encoding and modeling, and rely on deterministic objectives that favor average patterns, leading to over-smoothed reconstructions. We propose EpiTwin, a conditional spatial graph transformer for sEEG signal reconstruction, comprising three key components. Hybrid Spatial Positional Encoding (HSPE) constructs explicit spatial identities from electrode coordinates, graph topology, and anatomical priors. Geometry–Functional Biased Attention (GFBA) incorporates geometric distance and data-driven functional similarity biases into attention computation. Adversarial Refinement Training employs a multi-scale discriminator to counter reconstruction over-smoothing. Experiments on real-world clinical sEEG data demonstrate that EpiTwin consistently achieves lower reconstruction error under electrode series-level masking, outperforming recent foundation models such as LaBraM with a 16.8\% relative reduction in RMSE. Furthermore, EpiTwin effectively mitigates spectral over-smoothing and improves reconstruction fidelity.

Timely identification of harmful brain activities via electroencephalography (EEG) is critical for brain disease diagnosis and treatment, which remains limited in application due to inter-rater variability, resource constraints, and poor generalizability of existing artificial intelligence models. In this study, we describe an automated classifier, VIPEEGNet, which leverages the advantage of transfer learning from ImageNet-pretrained models to distinguish six types of brain activities. For the development cohort, the recall of VIPEEGNet ranges from 36.8% to 88.2%, and the precision ranges from 55.6% to 80.4%, with performance comparable to that of human experts. Notably, the external testing showed Kullback-Leibler divergence (KLD) values of 0.223 (public) and 0.273 (private), ranking second among the existing 2767 competing algorithms, while using only 0.7% of the parameters of the top-ranked algorithm. Its minimal parameter requirements and modular design offer a deployable solution for real-time brain monitoring, potentially expanding access to expert-level EEG interpretation in resource-limited settings.

Brain–computer interfaces (BCIs) have advanced at a rapid pace in recent years, particularly in the medical domain. This review provides a comprehensive summary of the progress made in medical BCIs during the 2023–2024 period, covering a wide range of topics from invasive to non‐invasive techniques, and from fundamental mechanisms to clinical applications. The 2023–2024 period saw numerous research breakthroughs and clinical applications of BCI technology. As BCI hardware and software continue to evolve, and as the understanding of basic medical principles deepens, the expectation is that innovative BCI inventions will increasingly be introduced in clinical practice. Both invasive and non‐invasive BCI technologies are paving the way for broader clinical applications. It is anticipated that BCI technologies will offer greater hope for disease treatment, provide additional methods of enhancing human bodily functions, and ultimately improve the quality of life.

INTRODUCTION:Melanogenesis, the process responsible for melanin production, is a critical determinant of skin pigmentation. Dysregulation of this process can lead to hyperpigmentation disorders. METHODS:In this study, we identified a novel Reed Rhizome extract, (1'S, 2'S)-syringyl glycerol 3'-O-β-D-glucopyranoside (compound 5), and evaluated its anti-melanogenic potential in zebrafish models and in vitro assays. Compound 5 inhibited melanin synthesis by 36.66% ± 14.00% and tyrosinase in vivo by 48.26% ± 6.94%, surpassing the inhibitory effects of arbutin. Network pharmacological analysis revealed key targets, including HSP90AA1, HRAS, and PIK3R1, potentially involved in the anti-melanogenic effects of compound 5. RESULTS:Molecular docking studies supported the interactions between compound 5 and these targets. Further, gene expression analysis in zebrafish indicated that compound 5 up-regulates hsp90aa1.1, hrasa, and pik3r1, and subsequently down-regulating mitfa, tyr, and tyrp1, critical genes in melanogenesis. CONCLUSION:These findings suggest that compound 5 inhibits melanin production via PI3K-Akt and Ras-Raf-MEK-ERK signaling pathways, positioning it as a promising candidate for the treatment of hyperpigmentation.

The brain is a complex system comprising neurons, local circuits, and functional regions. Neural pathways link various regions and collaborate to accomplish intricate cognitive and behavioral functions. To investigate brain activity within and between functional regions, we propose an adaptive local-global graph representation network (ALGGNet). This network suits brain-computer interface (BCI) applications, including the decoding of motor imagery electroencephalogram (EEG) signals and emotion recognition. ALGGNet consists of three essential components: the adaptive local region partitioning block, the temporal learning block, and the graph learning block. First, we adopt the concept of brain functional regions in neuroscience and employ the double clustering algorithm to generate brain-like functional regions for specific data sets adaptively. The EEG channels are then reordered according to the clustering results. Subsequently, the EEG signal passes through the time information processing stage and the graph learning stage. The time information processing stage mainly includes a multi-scale temporal convolution block and a feature mapping block with a coordinate attention (CA) mechanism. The graph learning stage includes graph filters that aggregate information about local regions and global graph convolutional networks (GCNs) that learn complex relationships between local regions. We conduct experiments on the motor imagery dataset and the emotion recognition dataset, respectively, and the results show that the proposed network obtains state-of-the-art performance. Additionally, we perform many ablation experiments, parameter tuning experiments, and visualization experiments to demonstrate the proposed ALGGNet better.

Systemic complications are common after acute brain injury (ABI) and may trigger coagulation cascades, systemic inflammation, as well as dysfunction of the cardiovascular, respiratory, and gastrointestinal systems, etc. The pathogenesis of these systemic manifestations is multifactorial but not yet fully elucidated. This paper introduces the novel term neurogenic organ dysfunction syndrome (NODS) to characterize systemic instability arising from internal and external perturbations of the neuronal center following ABI. Elucidating the central neurogenic mechanisms of NODS is critical for early detection and prevention of complications, thereby reducing mortality and improving patient outcomes following ABI. In this paper, we explore the potential central neurogenic mechanisms of NODS from the perspective of complex brain network theory, focusing on the structural network of the central autonomic system (CAS) that maintains systemic stability, and the functional network governed by the central stress system (CSS). The CAS can be divided into the cortical autonomic network, which involves higher cortical regions, and the subcortical autonomic network, which is relatively conserved, with its main connections located in deep brain structures. The CSS is a large-scale complex network characterized by hierarchy, hubs, and modularity, which together enable the competitive optimization of functional segregation and integration. Under physiological conditions, modules (mediating functional segregation) and hubs (functional integration) within the CSS dynamically trade-off with each other to maintain the overall homeostasis. However, this balance is disrupted following pathological insults or injury, resulting in weakened functional integrity of the CSS following ABI, impaired module activity, and disturbed hub integration. This paper also demonstrates the distinct pathological manifestations arising from disturbances at different levels of the homeostatic system. Finally, this study proposes potential clinical interventions, including analgesia and sedation, neuromodulation, and receptor regulation, for early interventions and potential treatment of NODS, aiming to improve patient outcomes.

Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, and applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited applications such as motor rehabilitation or communication. This paper aims to offer a comprehensive review of recent electroencephalogram (EEG)-based BCI applications in the medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, and emotion recognition. Moreover, the current challenges and future trends of BCIs were also discussed, including personal privacy and ethical concerns, network security vulnerabilities, safety issues, and biocompatibility.

BACKGROUND:The world has witnessed a steady rise in neurological diseases, which represent a heterogeneous group of disorders characterized by complex pathogenesis involving disruptions at multiple molecular levels, including genomic, transcriptomic, proteomic, and metabolomic levels. These disorders, often caused by genetic mutations, metabolic imbalances, immune dysregulation, and environmental factors, pose significant challenges to global public health due to their high prevalence, mortality, and disability burden. RESULTS:The advent of high-throughput technologies, such as next-generation sequencing and mass spectrometry, has provided valuable insights into the underlying mechanisms of disease, especially the development of multi- and high-spatial-resolution omics technologies, enabling the interaction of multiple levels of biology and analysis of the complex molecular networks and pathophysiological processes. CONCLUSIONS:This review provides a comprehensive analysis of the latest advancements in multi- and high-spatial-resolution omics, with a focus on their applications in precision diagnostics, biomarker discovery, and therapeutic target identification in brain diseases. The study also highlights the current challenges in the clinical implementation and discusses the future directions, with artificial intelligence being anticipated to enhance clinical translation and diagnostic accuracy significantly.

Subject-independent seizure detection algorithms are typically grounded in scalp electroencephalogram (EEG) databases, due to standardized channels and locations of EEG electrodes. Intracranial EEG (iEEG) has the characteristics of low noise and high temporal resolution compared with scalp EEG. However, it is still a big challenge for seizure detection using iEEG, because of the inconsistent number and locations of implanted electrodes in different patients, which results in a lack of unified algorithms. This study introduces an innovative approach for subject-independent seizure detection using iEEG, combining channel-wise mixup, transformer networks, and multi-task learning. Channel-wise mixup enhances data utilization by effectively leveraging information from different subjects, while multi-task learning improves the generalization of the model by concurrently optimizing both the seizure detection and the subject recognition tasks. 2983 files from two well-known epilepsy databases, i.e. SWEC-ETHZ and HUP were used in our study and the result showed that our approach surpasses currently existing methods. In terms of accuracy and generalization of seizure detection, our method achieved an area under the receiver operating characteristic curve (AUC) of 0.97 and 0.95 on the two databases respectively, which are significantly higher than the result of the currently existing methods. This study proposed a new method with great potential for surgery planning of epilepsy patients.

Transcranial acoustoelectric (AE) brain imaging (tABI) is a potential neuroimaging technique with high spatial (similar to mm) and temporal (similar to ms) resolution. Decoding precision is critical for decoded signal quality and imaging performance. However, the traditional method is based on the fundamental component of the AE signal, overlooking the valuable information of its harmonics. Here, we present a signal decoding framework combining fundamental and harmonic components. Adopting filter bank analysis to efficiently integrate the fundamental and harmonic components, a weighted filter bank Hilbert transform (wFBHT) decoding method is implemented. With steady-state visual stimulation, both brain electric signal steady-state visual evoked potential (SSVEP) and AE signal of live rats were recorded using tABI. First, the signal-to-noise ratios (SNRs) characteristics of harmonic were analyzed. The decoded AE (decAE) signals of the second-sixth harmonic exhibited a highly consistent spectral response with SSVEP, which provided a theoretical basis for the framework. Then, using the grid search and nonlinear fitting, the impacts of harmonic number N and weight w on the change rate of correlation coefficient (CRCC) between the decAE signal and SSVEP were further explored. Results demonstrated that CRCC increased with harmonic number N significantly superior to the traditional method ( p = 0.0004 ). With equal weight of first-fourth harmonics, the highest correlation coefficient increased by 47%. What is more, the feasibility and performance were validated in live rats. Compared with that of the traditional method, the SNR of the decAE signal with wFBHT was 20.20 dB and increased by 72.1%.

BACKGROUND:Ventriculoperitoneal (VP) shunt surgery is the primary treatment for patients with idiopathic normal pressure hydrocephalus (iNPH). This study compared the outcomes of VP shunt placement using electromagnetic (EM) navigation versus standard methods in patients with iNPH, focusing on catheter accuracy and postoperative complication rates. METHODS:This retrospective study included 31 patients with iNPH who underwent standard shunt placement using anatomical landmarks and 50 patients who underwent EM-guided shunt placement. Parameters assessed included shunt placement grade, catheter tip position, catheter angle, puncture attempts, operative duration, postoperative infection rates, intraparenchymal hemorrhage rates, and shunt malfunction rates. Patients had follow-ups at 3, 6, 12, and 24 months after surgery or until shunt failure. RESULTS:In the EM-guided group, a higher percentage of grade 1 shunt placements (92% vs. 71%, P = 0.03) and fewer grade 3 placements (2% vs. 13%, P = 0.068) were observed. The catheter tip position at the foramen of Monro was significantly more accurate (P < 0.001), with smaller lateral catheter deviation angles in both coronal (19.69° vs. 24.2°, P < 0.0001) and sagittal (21.75° vs. 39.3°, P < 0.01) sections. The EM-guided group had fewer puncture attempts, shorter operative durations, lower incidence of intraparenchymal hemorrhage (P < 0.01), and fewer shunt malfunctions over the 2-year follow-up period (2% vs. 26%, P = 0.0003). CONCLUSIONS:The use of EM navigation in VP shunt placement for patients with iNPH improves catheter placement accuracy, reduces postoperative complications and shunt malfunction rates, and provides precise and individualized surgical treatment.