Search Results (1,898)

Cerebellar ataxia is clinically and genetically heterogeneous, and candidate variant prioritization is particularly difficult when only a single affected animal is available. We combined structural MRI of one affected Border Collie and six controls with whole-genome sequencing of the affected dog and 31 comparison dogs. MRI revealed widened cerebellar folial spaces and reduced bilateral cerebellar gray matter volume, without global brain atrophy or spinal cord lesioning. Parallel heterozygous and recessive variant screens, together with identity-by-descent, runs-of-homozygosity, structural-variant, and repeat-expansion analyses, narrowed the candidates to six non-MODIFIER variants. Among these, a heterozygous PACSIN1 splice-donor variant (chr12:3,985,222 T > G) was private to the affected dog in the analyzed cohort and affects a conserved residue in the F-BAR domain. No segregation, replication, or functional validation data are currently available. These results prioritize the PACSIN1 variant as a testable candidate and demonstrate the utility of imaging-guided genomic prioritization in underpowered veterinary cohorts. Full article

Background: Accurate presurgical localization of eloquent cortex and subcortical pathways is essential in neurosurgery, guiding the balance between maximal safe resection and preservation of neurological function. This narrative review compares the clinical utility of task-based functional magnetic resonance imaging (tb-fMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) in neurosurgical populations, with emphasis on brain tumors and epilepsy. Methods: This narrative review was based on a non-systematic literature search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar from database inception to March 2026. The review focused on tb-fMRI and rs-fMRI for presurgical functional mapping in neurosurgical populations, including clinical utility, feasibility, validation, limitations, and workflow integration. Results: Tb-fMRI remains the most established noninvasive modality for motor and language mapping and language lateralization because of its task-specific activation maps and established role in clinical workflows. However, its use is limited by dependence on patient cooperation, task performance, and intact neurovascular coupling; thus, aphasia, cognitive impairment, fatigue, paresis, pediatric age, sedation, and tumor-related neurovascular uncoupling may render tb-fMRI inconclusive or misleading. Rs-fMRI offers a task-free alternative based on intrinsic functional connectivity, enabling simultaneous mapping of multiple resting-state networks from a single acquisition and providing particular value in non-cooperative, cognitively impaired, aphasic, pediatric, or sedated patients. Evidence indicates that rs-fMRI is most robust for sensorimotor mapping, with reported agreement with tb-fMRI and intraoperative direct electrical stimulation, whereas language mapping remains less consistent and more dependent on analytical methodology. Neither modality replaces intraoperative stimulation, which remains the reference standard. Conclusions: Current evidence supports a multimodal presurgical strategy in which tb-fMRI is used first-line in cooperative patients; rs-fMRI is added when task-based mapping is limited or infeasible, and both are interpreted alongside tractography, neuronavigation, and intraoperative mapping. Full article

Background: The emergence of generative adversarial networks has laid the groundwork for data augmentation, addressing challenges of missing training data in various research scenarios. However, simulating functional magnetic resonance imaging (fMRI) data remains particularly challenging, especially for populations with varying degrees of mild cognitive impairment (MCI). Effectively characterizing and capturing the mechanisms of brain function variations poses a critical issue in cognitive neuroscience. This study aims to simulate and analyze synthetic fMRI blood-oxygen-level-dependent (BOLD) signals across four cognitive stages: healthy control (HC), early MCI (EMCI), late MCI (LMCI), and Alzheimer’s disease (AD). Methods: We propose WGTMM, an innovative method that integrates the Vision Transformer for fMRI (VTFF) into a generative adversarial network architecture. Crucially, WGTMM directly generates fMRI time-series data from pink noise rather than modeling in a latent space, thereby preserving rich temporal dynamics. The framework incorporates a Wasserstein GAN (WGAN) with feature matching to enhance generation quality and mitigate mode collapse. Results: demonstrate that WGTMM-generated fMRI data exhibit lower Kullback-Leibler (KL) divergence compared to traditional GAN and WGAN models, indicating a closer resemblance to real datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Furthermore, when applied to data augmentation, the synthetic data substantially improve multi-class classification performance. Conclusions: WGTMM not only enriches training datasets but also provides new insights into spatial biomarkers of cognitive decline. By leveraging VTFF to investigate class token attention patterns across 360 brain regions, this study reveals monotonic weight variations along disease stages in key cortical areas, including the rostral Area 6, the primary sensory cortex, and PFm near Wernicke’s area, offering a fine-grained exploration of disease progression. Full article

Prenatal alcohol exposure (PAE) is recognized as a major public health concern due to its profound and lasting effects on the central nervous system (CNS) and its ability to induce fetal alcohol spectrum disorders (FASD), which encompass a wide range of cognitive, behavioural, and neuropsychiatric disorders that persist throughout life. Experimental and clinical studies have identified several mechanisms underlying ethanol impairing brain development, including apoptosis, oxidative stress, disruption of morphogen and growth factor signalling pathways, impaired neuronal proliferation and migration, neurotransmitter systems’ dysfunction, glial cells damage associated with deficient myelination, vascular and blood–brain barrier (BBB) alterations, and lasting epigenetic reprogramming. However, to date no widely accepted integrative framework explaining how these impairments underline the heterogeneous phenotype observed in FASD is available. The present brings together developmental neurobiology and computational neuroscience to conceptualize PAE as a disorder of emerging neural and functional architecture. Here, we summarize the pharmacokinetics of ethanol in pregnancy, critical windows of vulnerability, and the classical pathways of alcohol teratogenesis affecting neuronal survival, migration, synaptogenesis, myelination, and gene regulation. We have also reviewed MRI, diffusion imaging, and EEG/MEG evidence showing altered brain volumes, white matter microstructure, functional connectivity, and network organization in individuals with PAE. Finally, we propose a systems-level model that conceptualizes PAE as a disorder of emerging neuro-computational architecture, in which ethanol-induced cellular and molecular perturbations collectively alter the building blocks and self-organization rules of brain network assembly. Full article

Blood-based biomarkers offer a promising “biochemical imaging” approach for acute ischemic stroke (AIS) management, providing objective and accessible tools to complement conventional neuroimaging. This narrative review synthesizes recent advances in biomarkers derived from multiple neurovascular unit (NVU) compartments, including glial fibrillary acidic protein (GFAP), S100 calcium-binding protein B (S100B), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), neuron-specific enolase (NSE), neurofilament light chain (NfL), matrix metalloproteinase-9 (MMP-9), Claudin-5, Occludin, brain-derived neurotrophic factor (BDNF), interleukin-33 (IL-33), tumor necrosis factor-alpha (TNF-alpha), PARK7/DJ-1, glycogen phosphorylase BB (GP-BB), and circulating microRNAs. We focus on their stage-specific clinical utility across three scenarios: (1) ultra-early differentiation between ischemic stroke and intracerebral hemorrhage in prehospital and emergency settings; (2) dynamic prediction and monitoring of hemorrhagic transformation after reperfusion therapies; and (3) assessment of infarct burden, neurorepair potential, and long-term functional outcomes. Despite their promise, clinical translation remains hindered by assay platform heterogeneity, lack of standardized cut-off values, limited cost-effectiveness data, and insufficient prospective validation adjusted for key covariates such as age and renal function. We further discuss multi-marker panel construction, including strategies to address biomarker collinearity and overfitting. Future directions emphasize stage-specific panels, point-of-care testing devices, and artificial intelligence algorithms to advance precision medicine in stroke care. Full article

Background: Mild Cognitive Impairment (MCI) is a clinical state between normal aging and dementia. It may involve impairment in one or several cognitive domains. MCI offers a key window for maintaining cognitive function and studying how deficits develop in the elderly, making it of great research value. Measurement tools for screening MCI are not yet standardized in China. The accuracy of diagnostic criteria and threshold values needs improvement. Previous studies on the neural mechanisms of MCI have examined various aspects, but the changes in the white matter microstructure in older adults with MCI remain unclear. Most past studies used Fractional Anisotropy (FA) analysis to examine changes in white matter fiber orientation, often ignoring fiber density. As a result, findings are often contradictory or difficult to interpret. Therefore, it is necessary to assess cognitive function in MCI populations using more comprehensive and standardized measurement tools. It is also important to explore the association between changes in white matter microstructure and cognitive function in MCI by analyzing FA and Mean Diffusivity (MD). Methods: First, we assessed cognitive function using the Cognitive Function Measurement Scale for the Elderly, developed by Beijing Normal University, with diagnoses based on the NIA-AA (National Institute on Aging—Alzheimer’s Association) criteria. Second, we employed Diffusion Tensor Imaging (DTI) combined with Tract-Based Spatial Statistics (TBSS) to investigate alterations in the white matter fiber tract integrity in individuals with MCI. Based on the metrics used, this study was divided into two analytical approaches: Analysis Mode 1 utilized FA to explore changes in white matter fiber orientation in the MCI group. Analysis Mode 2 utilized MD to examine changes in white matter fiber density in the MCI group. Third, we further explored the association between alterations in the white matter fiber tract integrity and cognitive function in individuals with MCI. Specifically, FA and MD values from brain regions showing significant differences between the MCI and normal control groups were extracted and correlated with cognitive test scores. Results: According to the results of the community measurement survey, the prevalence of MCI among the elderly in Shenzhen is approximately 21.54%. Individuals with MCI exhibited functional decline in memory, attention, language, executive function, and spatial processing. DTI results indicated that (1) FA values across the brain’s white matter fiber tracts showed a decreasing trend in the elderly with MCI, with no areas exhibiting significantly higher FA values. Specifically, FA values were significantly lower in the corpus callosum, internal capsule, corona radiata, thalamic radiation, external capsule, superior fronto-occipital fasciculus, and cingulum (cingulate gyrus). (2) White matter fiber tracts with significantly reduced FA values also demonstrated significantly increased MD values. Additionally, MD values in the cingulum (hippocampus), inferior cerebellar peduncle, and corticospinal tract were significantly reduced in the MCI group. (3) Correlation analysis revealed that the significant differences in FA and MD values within the white matter fiber tracts of older adults with MCI were correlated with scores on several cognitive tests. Conclusions: In the present study, older adults with MCI tended to exhibit functional decline across multiple cognitive domains and relatively extensive microstructural white matter damage. Observations suggested that white matter fiber density may be informative regarding these microstructural alterations, indicating that diffusion biomarkers in key regions such as the cingulum (hippocampus) warrant further investigation. Full article

Background/Objectives: COVID-19, caused by the SARS-CoV-2 virus, can lead to widespread neurological and cognitive complications, even in the absence of significant structural brain abnormalities. Understanding the evolving health concerns in the context of viral infections is critical to service member readiness, fitness, and mission completion. The potential neuroprotective effects of SARS-CoV-2 vaccination remain underexplored. Methods: Using a cross-sectional, non-human primate model (female cynomolgus macaques), we employed magnetoencephalography (MEG) to assess resting-state brain activity following vaccination with escalating doses of a novel psoralen-inactivated SARS-CoV-2 vaccine (PsIV) or a combination of PsIV and a DNA vaccine (prime boost), and subsequent challenge with the Delta variant (SARS-CoV-2 B.1.617.2). MEG scans were acquired 41 days after inoculation. Source series were constructed for 42 regions of interest for each subject, and band power was computed. Results: Band power demonstrated substantial preservation of neural activity across multiple brain regions in vaccinated subjects compared to unvaccinated controls following viral challenge. Significantly lower power was observed across the brain at all bandwidths in the unvaccinated group relative to the prime boost group. As PsIV concentration increased, spectral power increased, with the prime boost group having the greatest power. Conclusions: This approach not only underscores the role of vaccination in mitigating neuropathology but also highlights the capability of MEG to detect subtle yet significant changes in brain function that may be overlooked by other imaging modalities. These findings advance our understanding of vaccine-induced neuroprotection and establish MEG as a powerful tool for monitoring brain function in the context of viral infections. Full article

Spiking neural networks (SNNs) are well suited for modeling temporally evolving information due to their event-driven and dynamic neuronal mechanisms. Nevertheless, the majority of existing SNN topologies are constructed through algorithmic procedures rather than guided by constraints from biological brain connectivity, which weakens their biological plausibility. In our earlier work, we developed a spiking neural network (SNN) by incorporating topological information from functional brain networks extracted from functional magnetic resonance imaging (fMRI) data of healthy individuals, and named the resulting model fMRISNN. Nevertheless, the fMRI data used in previous work were resting-state fMRI. Compared with resting-state fMRI, task-state fMRI can capture brain-region coordination patterns induced by specific task stimuli, and the resulting functional brain network is therefore more closely related to the corresponding task. Motivated by this advantage, this study replaces the resting-state topology used in previous fMRISNN studies with a task-state, number/digit-related fMRI topology and validates the resulting Task-fMRISNN on handwritten digit recognition. The experimental results demonstrate that the proposed Task-fMRISNN outperforms the Rest-fMRISNN in terms of recognition accuracy, lesion robustness, and noise robustness. In addition, the Task-fMRISNN achieves significantly better performance than several baseline models constructed using algorithmically generated topologies. While deep convolutional neural networks (CNNs) may deliver superior absolute recognition performance, the proposed fMRISNN provides a more compact model structure and shows potential resource-efficiency advantages due to its sparse and event-driven computational characteristics. Full article

Background/Objectives: Rheumatoid arthritis (RA) has been associated with accelerated atherosclerosis and cerebrovascular alterations. Our 2017 study compared 60 RA patients to healthy controls, assessing vascular, neurological, and cognitive parameters. The present study is a follow-up of these RA patients to evaluate disease progression and vascular changes over time, using their 2017 results as baseline. Methods: In 2023, we reassessed 43 of the original 60 RA patients using laboratory testing, carotid ultrasound, functional transcranial Doppler (TCD) and brain magnetic resonance imaging (MRI) examinations. Changes over time were analyzed within the same individuals. Results: Inflammatory markers and lipid profiles showed a trend toward improvement, though changes were not statistically significant, except for a significant increase in vitamin D (p < 0.001) and a decrease in Disease Activity Score in 28 Joints (DAS28) scores (p < 0.001). Carotid ultrasound revealed a significant increase in plaque burden (p = 0.022 on the right side and p = 0.008 on the left), while carotid intima media thickness (cIMT) showed a non-significant rise. TCD measurements indicated significantly increased pulsatility (p < 0.001 on the right, p = 0.001 on the left side) and resistance (p = 0.001 on the right, p = 0.012 on the left side) indices and reduced flow velocities (p < 0.001 on the right and p = 0.001 on the left side) in bilateral middle cerebral arteries (MCAs). The cerebrovascular reserve capacity was significantly lower on the right side overall (p = 0.013), with further decline noted in the methotrexate (MTX)-treated subgroup on the left side (p = 0.043). MRI findings showed non-significant numerical trends toward worsening lacunar small-vessel disease (p = 0.405) and cerebral atrophy (p = 0.063), with higher but stable lacunar infarction scores among MTX users (p = 0.023). Conclusions: Despite improved inflammatory control, RA patients demonstrated progressive vascular and hemodynamic alterations over time, while MRI changes should be interpreted as trends. These findings support multimodal vascular monitoring in RA. Full article

Glioblastoma (GBM) is one of the most aggressive human cancers, with therapeutic failure driven by pronounced intratumoral heterogeneity, microenvironmental plasticity, immune suppression, blood–brain barrier (BBB)-related pharmacological constraints, and adaptive resistance mechanisms. A major limitation in GBM research is the lack of a human-relevant experimental system able to reproduce these dynamic features while generating interpretable, multimodal datasets. In this context, we propose a testable organ-on-chip (OoC)-extracellular vesicle (EV)-deep learning (DL) framework in which patient-derived GBM cells, endothelial cells, astrocytes, pericytes, stromal cells, and immune components are organized within perfused microphysiological systems. EVs are selectively and temporally harvested from defined compartments, and imaging, barrier-function, sensor, and EV-cargo data are integrated through modality-specific and multimodal DL architectures. This framework is intended not as an immediately validated clinical tool but as an experimental roadmap for linking EV-mediated communication to measurable phenotypes such as BBB disruption, invasion, immune reprogramming, and drug response. We critically discuss the technical requirements of BBB-on-chip systems, EV source attribution, immune-component integration, DL model selection, data scarcity, overfitting, batch effects, domain shift, regulatory barriers, cost, throughput, and reproducibility. By repositioning OoC-EV-DL integration as a staged translational strategy rather than a clinically established solution, this work aims to define a realistic and biologically grounded route for advancing precision oncology in GBM. Full article

Exosomes are small vesicles released by cells that have attracted growing interest as drug delivery vehicles, particularly for brain diseases, where getting therapeutics across the BBB remains a fundamental problem. While conventional platforms such as liposomes, polymeric nanoparticles, and viral vectors often suffer from immune clearance and poor brain accumulation, engineered exosomes leverage natural cellular transport mechanisms to cross the BBB, protect cargo from degradation, and enable biocompatible interactions with target cells. This review takes a mechanistic and translational look at how exosomes are being engineered for CNS disorders, with a particular focus on glioblastoma. We cover exosome biogenesis through ESCRT-dependent and ESCRT-independent pathways, and how the competition between Rab27-driven secretion and Rab7-driven lysosomal degradation determines how many exosomes a cell releases, which has direct consequences for therapeutic production. We then discuss cargo loading strategies, from genetic approaches where donor cells are engineered to package specific molecules during biogenesis to physical methods like electroporation and sonication applied to isolated vesicles, alongside surface modification techniques for directing exosomes toward specific cell types. In glioblastoma, engineered exosomes have shown real promise for delivering chemotherapeutics across the BBB, targeting glioma stem cells, enabling CRISPR-based gene editing, and functioning as combined treatment and imaging tools. Applications in stroke and neurodegenerative diseases, where engineered exosomes carrying microRNAs and neuroprotective cargo have produced encouraging preclinical results, are also discussed. Scalable manufacturing and consistent targeting remain the hardest unsolved problems, and we outline emerging approaches including bioreactor-based production, programmable cargo loading, and patient-specific exosome design that are beginning to address these gaps. Overall, the progress reviewed here suggests that engineered exosomes are moving from an interesting biological concept toward a practically viable platform for CNS drug delivery. Full article

Early diagnosis of post-traumatic epilepsy (PTE) is crucial for timely intervention. However, it is hampered by the lack of reliable biomarkers. In this review, we provide a comprehensive summary of current advances in PTE biomarker research, drawing primarily on evidence from human cohort studies, with selective support from experimental animal models where mechanistic insights are required. We cover (i) neuroimaging, including CT, MRI, and EEG/qEEG, which reveal structural and functional alterations associated with epileptogenesis; (ii) molecular biomarkers, including RNAs, proteins, metabolites, and extracellular vesicle (EV)-derived molecules that reflect neuroinflammation, blood–brain barrier (BBB) dysfunction, neuronal injury, and synaptic remodeling; and (iii) artificial intelligence (AI)-assisted approaches, which integrate multimodal datasets to identify complex predictive patterns. While individual modalities offer valuable but incomplete prognostic information, AI-driven analytics hold the greatest promise for early predictive power by combining multimodal data. Future progress will depend on the integration of high-resolution imaging, multi-omics profiling, and rigorous validation to deliver clinically actionable biomarker panels and ultimately reduce the burden of PTE. Full article

Physical unclonable function (PUF) based on intrinsic device randomness has emerged as promising hardware security primitives, yet combining secure encryption with neuromorphic recognition within a single device platform remains challenging. Here, we demonstrate a photosensing PUF based on an intrinsically random SnTe memristor capable of both image encryption and memristive neural network recognition. The SnTe memristor, fabricated with an In₂O₃:SnO₂/SnTe/Nb:SrTiO₃ structure, exhibits stable resistive switching and stable retention exceeding 4000 s. Synaptic biomimetic behaviors including learning-experience emulation, short-term plasticity and long-term plasticity are also realized. Notably, the device displays pronounced optical sensitivity that produces stochastic photocurrent fluctuations originating from unavoidable device-to-device variations under illumination. By quantizing these random photocurrents, an encryption key stream is generated and utilized for image scrambling and diffusion. A memristive neural network is constructed to classify the encrypted images, achieving a recognition accuracy of 95.1% with a loss of 0.15 after 300 training epochs. This work establishes a viable pathway from intrinsic optical randomness to secure neuromorphic computing, highlighting the multifunctional potential of SnTe memristors in integrated hardware security and brain-inspired computation. Full article

A brain–computer interface (BCI) is a complex system that allows humans to interact with physical devices by analysing and interpreting brain signals obtained from neuroimaging modalities (electroencephalography, electrocorticography, magnetoencephalography, intracortical neuron recording, functional magnetic resonance imaging, etc.). BCI applications in robotics and medicine have demonstrated invaluable benefits. The rise of BCI technology and neuroergonomics techniques could also provide promising solutions in transportation systems, particularly in smart roads, vehicles, and traffic regulation systems. This narrative literature review examines how, in the age of smart transportation systems and self-driving vehicles, different far-future applications of BCI systems could be integrated to enhance the safety and capacity of transportation systems. Full article

Background: Positron emission tomography (PET) is a molecular imaging technique that exploits the β⁺ decay of selected radionuclides to enable non-invasive in vivo investigation of biochemical and physiological processes, including early and subclinical disease alterations. Radiotracers are designed to bind specific molecular targets with high affinity and selectivity. Among the targets to which PET devotes increasing attention are G protein-coupled receptors (GPCRs)—the largest class of transmembrane receptors—which orchestrate a wide spectrum of biological outcomes and are widely implicated in human disease. Objectives: This review analyzes patents published between 2020 and 2025 focusing on GPCR-targeted PET radiotracers, highlighting design strategies, radionuclide selection, and translational perspectives across oncology, central nervous system (CNS) disorders, and inflammatory diseases. Results: Patent activity shows that most GPCR-targeted PET tracers are derived from validated ligands adapted for imaging while preserving affinity and selectivity. Oncology patents mainly favor peptide-based or modular metal–chelator platforms enabling radionuclide flexibility and theranostic extension, whereas CNS tracers rely on drug-like small molecules optimized under strict ADME and blood–brain barrier constraints. Increasing emphasis on non-orthosteric, function-sensitive, and dual-targeting approaches reflects a shift toward interrogating GPCR signaling states, while inflammatory indications remain comparatively underrepresented despite clear biological foundations. Conclusions: Current patent trends consolidate GPCR-targeted PET tracers as well-established diagnostic tools while progressively expanding their clinical utility, both as platforms supporting translational research—informing mechanistic insight and drug development—and as components of emerging theranostic strategies across multiple disease areas. Full article