Search Results (921)

Background/Objectives: Accumulating evidence suggests that cognitive training can induce functional reorganization of intrinsic connectivity networks involved in higher-order cognitive processes. However, few interventions have specifically targeted language, an essential domain tightly interwoven with memory, attention, and executive functions. Given their foundational role in communication, reasoning, and knowledge acquisition, enhancing language-related abilities may yield widespread cognitive benefits. This study investigated the neural impact of a new structured, language-based cognitive training program on neurotypical older adults. Methods: Twenty Brazilian Portuguese-speaking women (aged 63–77 years; schooling 9–20 years; low-to-medium socioeconomic status) participated in linguistic activities designed to engage language and general cognitive processing. Behavioral testing and resting-state functional Magnetic Resonance Imaging (fMRI) were conducted before and after the intervention. Results: Functional connectivity analyses revealed significant post-intervention increases in connectivity within the frontoparietal network, critical for language processing, and the ventral attentional network, associated with attentional control. Conclusions: The observed neural enhancements indicate substantial plasticity in cognitive networks among older adults, highlighting the effectiveness of linguistic interventions in modulating critical cognitive functions. These findings provide a foundation for future research on targeted cognitive interventions to promote healthy aging and sustain cognitive vitality. Full article

Background: Depression and anxiety disorders impact millions globally. In recent years, low-intensity focused ultrasound (LIFU), characterized by its high precision, deep penetration, and non-invasive nature, has garnered significant interest in neuroscience and clinical practice. To enhance understanding of its effects on mood, therapeutic availability in treatment of depression/anxiety disorders, and potential mechanisms, a systematic review of studies investigating the emotional impact of LIFU on depressive/anxious-like animal models, healthy volunteers, and patients with depression or anxiety disorders has been undertaken. Methods: Relevant papers published before 15 July 2025 were searched across four databases: Web of Science, PubMed, Science Direct, and Embase. A total of 28 papers which met the inclusion and exclusion criteria are included in this review. Results: Our findings indicate that LIFU reversed the depressive/anxious-like behaviors in the animal models and showed antidepressant/anti-anxiety effects among the state-of-art clinical studies. For example, immobility time in FST or TST is reduced in depressive animal models, and HRSD/BAI scales are improved in human studies. Key molecules such as BDNF/5-HT are found restored in animal models, and FC between key brain areas related to depression/anxiety is modulated after LIFU treatment. Notably, no brain tissue damage was observed in animal studies, and only mild adverse effects (such as dizziness and vomiting) were noted in a few human studies. Conclusions: The studies using LIFU to treat depression and anxiety remain in the preliminary stage. The mechanisms underlying LIFU’s mood effects—such as activation or inhibition of specific brain regions or neural circuits, anti-inflammatory effects, alterations in functional connectivity, synaptic plasticity, neurotransmitter levels, and BDNF—remain incompletely understood and warrant further investigation. Nevertheless, the LIFU technique holds promise for regulating both cortical and subcortical brain areas implicated in depression/anxiety disorders as a precise neuromodulation tool. Full article

Tenascin-C (TNC) is a complex extracellular matrix (ECM) protein that plays a critical role in regulating cellular adhesion, motility, proliferation, and inflammation through its interaction with Toll-like receptor 4 (TLR4) and other receptors. The upregulation of TNC is associated with inflammatory responses, autoimmune disorders, and neoplastic conditions during both physiological and pathological tissue remodeling. In the central nervous system (CNS), TNC contributes to neuroinflammatory processes by modulating the function of immune cells and the secretion of pro-inflammatory mediators, thereby playing a pivotal role in the initiation and progression of neuroinflammatory diseases. TNC is expressed in astrocytes, neural progenitor cells, and various neuronal populations within both developing and mature CNS regions. It regulates neuronal migration and axonal guidance during neurogenesis, facilitating synaptic plasticity and CNS regeneration. Furthermore, TNC enhances neuroplasticity through interactions with receptor families, such as integrins, to establish the molecular connections necessary for cell communication and signal transduction. This review investigates the mechanistic properties of TNC, focusing on its spatiotemporal expression, molecular interactions with receptors, and its role in neurological disorders, in addition to its modulatory capacity in neuroplastic processes. Additionally, this review delves into recent research advancements with respect to neuroinflammation involving TNC, along with therapeutic strategies targeting TNC. Full article

Computation modeling for large thermoplastic deformation of plastic solids is critical for industrial applications like non-invasive assessment of engineering components. While deep learning-based methods have emerged as promising alternatives to traditional numerical simulations, they often suffer from systematic errors caused by geometric mismatches between predicted and ground truth meshes. To overcome this limitation, we propose a novel boundary geometry-constrained neural framework that establishes direct point-wise mappings between spatial coordinates and full-field physical quantities within the deformed domain. The key contributions of this work are as follows: (1) a two-stage strategy that separates geometric prediction from physics-field resolution by constructing direct, point-wise mappings between coordinates and physical quantities, inherently avoiding errors from mesh misalignment; (2) a boundary-condition-aware encoding mechanism that ensures physical consistency under complex loading conditions; and (3) a fully mesh-free approach that operates on point clouds without structured discretization. Experimental results demonstrate that our method achieves a 36–98% improvement in prediction accuracy over deep learning baselines, offering a efficient alternative for high-fidelity simulation of large thermoplastic deformations. Full article

Wood–plastic composite (WPC) is being increasingly adopted in construction and furniture applications due to its durability and recyclability. This study investigates face-milling responses—resultant cutting force and cutting temperature—under systematically varied cutting parameters, and develops a radial basis function neural network for predictive modeling. Experiments were conducted on a computer numerical control machining center using a polycrystalline diamond end-milling cutter for face milling with fixed axial depth of cut. Feed speed, radial depth of cut, and spindle speed were selected as input factors. The results indicate that feed speed and radial depth of cut generally increase all force components, whereas higher spindle speed tends to reduce force magnitudes while elevating temperature. The radial basis function neural network yields acceptable accuracy for resultant cutting force (coefficient of determination R² ≈ 0.91) and acceptable accuracy for cutting temperature (R² ≈ 0.81). These findings demonstrate the feasibility of radial basis function neural network based prediction for WPC face milling and provide guidance for parameter selection. Full article

Autism spectrum disorder (ASD) and Fragile X syndrome (FXS) are neurodevelopmental disorders marked by deficits in communication and social interaction, often accompanied by anxiety, seizures, and intellectual disability. FXS, the most common monogenic cause of ASD, results from silencing of the FMR1 gene and consequent loss of FMRP, a regulator of synaptic protein synthesis. Disruptions in cyclic nucleotide (cAMP and cGMP) signaling underlie both ASD and FXS contributing to impaired neurodevelopment, synaptic plasticity, learning, and memory. Notably, reduced cAMP levels have been observed in platelets, lymphoblastoid cell lines and neural cells from FXS patients as well as Fmr1 KO and dfmr1 Drosophila models, linking FMRP deficiency to impaired cAMP regulation. Phosphodiesterase (PDE) inhibitors, which prevent the breakdown of cAMP and cGMP, have emerged as promising therapeutic candidates due to their ability to modulate neuronal signaling. Several PDE isoforms—including PDE2A, PDE4D, and PDE10A—have been implicated in ASD, and FXS, as they regulate pathways involved in synaptic plasticity, cognition, and social behavior. Preclinical and clinical studies show that PDE inhibition modulates neuroplasticity, neurogenesis, and neuroinflammation, thereby ameliorating autism-related behaviors. BPN14770 (a PDE4 inhibitor) has shown promising efficacy in FXS patients while cilostazol, pentoxifylline, resveratrol, and luteolin have showed improvements in children with ASD. However, challenges such as isoform-specific targeting, optimal therapeutic window, and timing of intervention remain. Collectively, these findings highlight PDE inhibition as a novel therapeutic avenue with the potential to restore cognitive and socio-behavioral functions in ASD and FXS, for which effective targeted treatments remain unavailable. Full article

Humans learn new tasks without forgetting, but neural networks suffer from catastrophic forgetting when trained sequentially. Dynamic expandable networks attempt to address this by assigning each task its own feature extractor and freezing previous ones to preserve past knowledge. While effective for retaining old tasks, this design leads to rapid parameter growth, and frozen extractors never adapt to future data, often producing irrelevant features that degrade later performance. To overcome these limitations, we propose Task-Sharing Distillation (TSD), which reduces the number of extractors by allowing multiple tasks to share one extractor and consolidating them through distillation. We study two strategies: (1) grouped rolling consolidation, which groups consecutive tasks and consolidates them into a shared extractor, and (2) a fixed-size pooling with similarity-based consolidation, where new tasks are merged into the most compatible extractor in a fixed pool according to prototype similarity. Experiments on the CIFAR-100 and ImageNet-100 datasets show that TSD maintains strong performance across tasks, demonstrating that careful feature sharing is more effective than simply adding more extractors. On ImageNet-100, our method achieves 2.5% higher average accuracy than DER while using fewer feature extractors. Full article

Background: Primary hepatocellular carcinoma (HCC) and liver metastases differ in origin, progression, and therapeutic response, yet a direct high-resolution spatial comparison of their tumor microenvironments (TMEs) within the liver has not previously been performed. Methods: We applied high-definition spatial transcriptomics to fresh-frozen specimens of one HCC and one liver metastasis (>16,000 genes per sample, >97% mapping rates) as a proof-of-principle two-specimen study, cross-validated in human proteomics and patients’ survival datasets. Transcriptional clustering revealed spatially distinct compartments, rare cell states, and pathway alterations, which were further compared against an independent systemic dataset. Results: HCC displayed an ordered lineage architecture, with transformed hepatocyte-like tumor cells broadly dispersed across the tissue and more differentiated hepatocyte-derived cells restricted to localized zones. By contrast, liver metastases showed two sharply compartmentalized domains: an invasion zone, where proliferative stem-like tumor cells occupied TAM-rich boundaries adjacent to hypoxia-adapted tumor-core cells, and a plasticity zone, which formed a heterogeneous niche of cancer–testis antigen–positive germline-like cells. Across both tumor types, we detected a conserved metabolic program of “porphyrin overdrive,” defined by reduced cytochrome P450 expression, enhanced oxidative phosphorylation gene expression, and upregulation of FLVCR1 and ALOX5, reflecting coordinated rewiring of heme and lipid metabolism. Conclusions: In this pilot study, HCC and liver metastases demonstrated fundamentally different spatial architectures, with metastases uniquely harboring a germline/neural-like plasticity hub. Despite these organizational contrasts, both tumor types converged on a shared program of metabolic rewiring, highlighting potential therapeutic targets that link local tumor niches to systemic host–tumor interactions. Full article

Traumatic brain injury (TBI) remains a major global health problem, contributing significantly to morbidity and mortality worldwide. Despite advances in understanding its complex pathophysiology, current therapeutic strategies are insufficient in addressing the long-term cognitive, emotional, and neurological impairments. While the primary mechanical injury is immediate and unavoidable, the secondary phase involves a cascade of biological processes leading to neuroinflammation, blood–brain barrier (BBB) disruption, and systemic immune activation. The heterogeneity of patient responses underscores the urgent need for reliable biomarkers and targeted interventions. Emerging evidence highlights the gut–brain axis as a critical modulator of the secondary phase, with microbiota-derived extracellular vesicles (MEVs) representing a promising avenue for both diagnosis and therapy. MEVs can cross the intestinal barrier and BBB, carrying biomolecules that influence neuronal survival, synaptic plasticity, and inflammatory signaling. These properties make MEVs promising biomarkers for early detection, severity classification, and prognosis in TBI, while also offering therapeutic potential through modulation of neuroinflammation and promotion of neural repair. MEV-based strategies could enable tailored interventions based on the individual’s microbiome profile, immune status, and injury characteristics. The integration of multi-omics with artificial intelligence is expected to fully unlock the diagnostic and therapeutic potential of MEVs. These approaches can identify molecular subtypes, predict outcomes, and facilitate real-time clinical decision-making. By bridging microbiology, neuroscience, and precision medicine, MEVs hold transformative potential to advance TBI diagnosis, monitoring, and treatment. This review also identifies key research gaps and proposes future directions for MEVs in precision diagnostics and gut microbiota-based therapeutics in neurotrauma care. Full article

Background: Treatment with androgen receptor (AR) signaling inhibitors, such as enzalutamide, can induce neural lineage plasticity in prostate cancer, potentially progressing to t-NEPC. However, the molecular mechanisms underlying this enzalutamide-driven plasticity, particularly the contribution of immune signaling pathways, remain poorly understood. Methods: We analyzed transcriptomic profiles of patient samples and prostate cancer cell lines to investigate changes in immune signaling pathways. Interferon gamma (IFNγ), interferon alpha (IFNα), and interleukin 6 (IL6)-Janus kinase (JAK)-signal transducer and activator of transcription 3 (STAT3) signaling were assessed in enzalutamide-sensitive and -resistant prostate cancer cells. Functional assays were conducted to examine cell responsiveness to cytokine stimulation and susceptibility to STAT1 inhibition using fludarabine. Results: Immune-related pathways, including IFNγ, IFNα, IL6-JAK-STAT3, and inflammatory responses, were significantly suppressed in NEPC patient samples compared to those with castration-resistant prostate cancer (CRPC). Enzalutamide-resistant and NEPC cells exhibited markedly impaired IFNγ and IL6 signaling. In contrast, early-stage enzalutamide treatment paradoxically enhanced IFNγ and IL6 responsiveness. Transcriptomic profiling revealed coordinated upregulation of E2F target genes and activation of IFNα/IFNγ and JAK/STAT signaling pathways during early treatment. Importantly, these early-stage cells remained highly sensitive to IFNγ and IL6 stimulation and showed increased susceptibility to STAT1 inhibition by fludarabine, a sensitivity that was lost in resistant cells. Conclusions: Early enzalutamide treatment enhances immune responsiveness, while the development of resistance is associated with suppressed immune signaling and increased lineage plasticity. These results suggest a therapeutic window where combining enzalutamide with STAT inhibitors may delay or prevent lineage plasticity and resistance. Full article

Microgravity exposure during spaceflight has been linked to cognitive impairments, including deficits in attention, executive function, and spatial memory. Both space missions and ground-based analogs—such as head-down bed rest, dry immersion, and hindlimb unloading—consistently demonstrate that altered gravity disrupts brain structure and neural plasticity. Neuroimaging data reveal significant changes in brain morphology, functional connectivity, and cerebrospinal fluid dynamics. At the cellular level, simulated microgravity impairs synaptic plasticity, alters dendritic spine architecture, and compromises neurotransmitter release. These changes are accompanied by dysregulation of neuroendocrine signaling, decreased expression of neurotrophic factors, and activation of oxidative stress and neuroinflammatory pathways. Molecular and omics-level analyses further point to mitochondrial dysfunction and disruptions in key signaling cascades governing synaptic integrity, energy metabolism, and neuronal survival. Despite these advances, discrepancies across studies—due to differences in models, durations, and endpoints—limit mechanistic clarity and translational relevance. Human data remain scarce, emphasizing the need for standardized, longitudinal, and multimodal investigations. This review provides an integrated synthesis of current evidence on the cognitive and neurobiological effects of microgravity, spanning behavioral, structural, cellular, and molecular domains. By identifying consistent patterns and unresolved questions, we highlight critical targets for future research and the development of effective neuroprotective strategies for long-duration space missions. Full article

Neuromorphic computing, inspired by the human brain’s architecture, offers a transformative approach to overcoming the limitations of traditional von Neumann systems by enabling highly parallel, energy-efficient information processing. Among emerging materials, MXenes—a class of two-dimensional transition metal carbides and nitrides—have garnered significant attention due to their exceptional electrical conductivity, tunable surface chemistry, and mechanical flexibility. This review comprehensively examines recent advancements in MXene-based optoelectronic synapses and neurons, focusing on their structural properties, device architectures, and operational mechanisms. We emphasize synergistic electrical–optical modulation in memristive and transistor-based synaptic devices, enabling improved energy efficiency, multilevel plasticity, and fast response times. In parallel, MXene-enabled optoelectronic neurons demonstrate integrate-and-fire dynamics and spatiotemporal information integration crucial for biologically inspired neural computations. Furthermore, this review explores innovative neuromorphic hardware platforms that leverage multifunctional MXene devices to achieve programmable synaptic–neuronal switching, enhancing computational flexibility and scalability. Despite these promising developments, challenges remain in device stability, reproducibility, and large-scale integration. Addressing these gaps through advanced synthesis, defect engineering, and architectural innovation will be pivotal for realizing practical, low-power optoelectronic neuromorphic systems. This review thus provides a critical roadmap for advancing MXene-based materials and devices toward next-generation intelligent computing and adaptive sensory applications. Full article

Background/Objectives: Understanding the diverse neuron types within the hippocampal formation is essential for advancing our knowledge of its fundamental roles in learning and memory. Hippocampome.org serves as a comprehensive, evidence-based knowledge repository that integrates morphological, electrophysiological, and molecular features of neurons across the rodent dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex. In addition to these core properties, this open access resource includes detailed information on synaptic connectivity, signal propagation, and plasticity, facilitating sophisticated modeling of hippocampal circuits. A distinguishing feature of Hippocampome.org is its emphasis on quantitative, literature-backed data that can help constrain and validate spiking neural network simulations via an interactive web interface. Methods: To assess and enhance its utility to the neuroscience community, we integrated Google Analytics (GA) into the platform to monitor user behavior, identify high-impact content, and evaluate geographic reach. Results: GA data provided valuable page view metrics, revealing usage trends, frequently accessed neuron properties, and the progressive adoption of new functionalities. Conclusions: These insights directly inform iterative development, particularly in the design of a robust Application Programming Interface (API) to support programmatic access. Ultimately, the integration of GA empowers data-driven optimization of this public resource to better serve the global neuroscience community. Full article

The growing demand for energy efficiency and sustainability in smart buildings necessitates advanced AI-driven methods for adaptive control and predictive maintenance. This study explores the application of Spiking Neural Networks (SNNs) to event-driven processing, real-time anomaly detection, and edge computing-based optimization in building automation. In contrast to conventional deep learning models, SNNs provide low-power, high-efficiency computation by mimicking biological neural processes, making them particularly suitable for real-time, edge-deployed decision-making. The proposed SNN based on Reward-Modulated Spike-Timing-Dependent Plasticity (STDP) and Bayesian Optimization (BO) integrates occupancy and ambient condition monitoring to dynamically manage assets such as appliances while simultaneously identifying anomalies for predictive maintenance. Experimental evaluations show that our BO-STDP-SNN framework achieves notable reductions in both energy consumption by 27.8% and power requirements by 70%, while delivering superior accuracy in anomaly detection compared with CNN, RNN, and LSTM based baselines. These results demonstrate the potential of SNNs to enhance the efficiency and resilience of smart building systems, reduce operational costs, and support long-term sustainability through low-latency, event-driven intelligence. Full article

Background/Objectives: Synaptic plasticity is fundamental to learning and memory, yet classical models such as Hebbian learning and spike-timing-dependent plasticity often overlook the distributed and wave-like nature of neural activity. We present a computational framework grounded in Scale Relativity Theory (SRT), which describes neural propagation along fractal geodesics in a non-differentiable space-time. The objective is to link nonlinear wave dynamics with the emergence of structured memory representations in a biologically plausible manner. Methods: Neural activity was modeled using nonlinear Schrödinger-type equations derived from SRT, yielding complex wave solutions. Synaptic plasticity was coupled through a reaction–diffusion rule driven by local activity intensity. Simulations were performed in one- and two-dimensional domains using finite difference schemes. Analyses included spectral entropy, cross-correlation, and Fourier methods to evaluate the organization and complexity of the resulting synaptic fields. Results: The model reproduced core neurobiological features: localized potentiation resembling CA1 place fields, periodic plasticity akin to entorhinal grid cells, and modular tiling patterns consistent with V1 orientation maps. Interacting waveforms generated interference-dependent plasticity, modeling memory competition and contextual modulation. The system displayed robustness to noise, gradual potentiation with saturation, and hysteresis under reversal, reflecting empirical learning and reconsolidation dynamics. Cross-frequency coupling of theta and gamma inputs further enriched trace complexity, yielding multi-scale memory structures. Conclusions: Wave-driven dynamics in fractal space-time provide a hypothesis-generating framework for distributed memory formation. The current approach is theoretical and simulation-based, relying on a simplified plasticity rule that omits neuromodulatory and glial influences. While encouraging in its ability to reproduce biological motifs, the framework remains preliminary; future work must benchmark against established models such as STDP and attractor networks and propose empirical tests to validate or falsify its predictions. Full article