Search Results (3,121)

Sharp wave–ripples (SWRs) are transient hippocampal population events that coordinate neuronal ensemble activity and play a central role in memory consolidation and affective processing. Although SWRs exhibit marked functional specialization along the dorsoventral axis of the hippocampus, and several cellular mechanisms underlying SWRs are sex-sensitive, systematic comparisons of SWR properties between females and males are lacking. Here, we examined sex- and region-dependent differences in SWRs and associated multiunit activity (MUA) in acute hippocampal slices from adult female and male rats. Spontaneous SWRs were recorded from the CA1 stratum pyramidale of the dorsal and ventral hippocampus, and SWR occurrence rate, amplitude, ripple oscillation properties, and SWR-locked neuronal firing were quantified. Linear mixed-effects analysis revealed robust region-dependent differences across multiple SWR parameters. In contrast, sex effects were selective. SWR occurrence rate and amplitude did not differ significantly between females and males, whereas SWR-associated MUA showed a significant main effect of sex, with higher values in males. Ripple power was also influenced by sex, with higher values in females, together with a significant effect of region, suggesting differences in oscillatory structure. Baseline MUA did not differ between sexes, indicating that sex-related effects are specific to the SWR state. These findings suggest that sex does not substantially alter the generation of SWRs per se but influences neuronal recruitment and oscillatory properties during these events. Our results reveal previously underappreciated dimensions of hippocampal network organization and provide a descriptive framework for future studies investigating how sex-dependent circuit properties may shape hippocampal contributions to cognition and affective regulation. They further highlight the importance of incorporating sex as a fundamental biological variable in studies of hippocampal network dynamics. Full article

This contribution examines the role of artificial intelligence technologies in the co-construction of social reality, with specific attention to AI-generated data as emergent agents of knowledge production. Building on perspectives from science and technology studies and recent debates on algomorphic sociology, the contribution conceptualizes generative AI systems not as research instruments, but as active participants in epistemic processes. The analysis argues that AI-generated data exhibit a performative character: they do not simply represent social phenomena but actively contribute to their stabilization, classification, and circulation. This performativity fosters a shift from researcher-centered interpretation toward hybrid configurations in which meaning emerges through human–machine assemblages. Through a theoretical synthesis of recent methodological and epistemological reflections, the contribution highlights a transition from anthropocentric models of knowledge production to post-anthropocentric, relational frameworks in which agency, cognition, and sense-making are distributed across sociotechnical networks. The contribution concludes by outlining the implications of this shift for the future of digital social research and also for reflexivity, methodological design, and the ethics of social research, advocating a critical and adaptive stance toward AI as a co-producer of knowledge rather than a subordinate analytical tool. Full article

Traditional Network Intrusion Detection Systems (NIDSs) face persistent challenges in detecting zero-day attacks due to concept drift, high false-positive rates, and limited adaptability. This research introduces a Cognitive Network Intrusion Detection System (CNIDS) whose central novelty is that effective zero-day handling does not arise from any single mechanism but from the interaction between continual representation learning, persistent vector memory, and human-aligned feedback. By reframing zero-day resilience as a continuous learning process rather than a static detection task, CNIDS emphasizes adaptive operational behavior over raw automated accuracy. The proposed framework integrates Continual Pre-Training (CPT) to align representations with evolving traffic, Supervised Fine-Tuning (SFT) to preserve precision on known attacks, and a Human-in-the-Loop Reinforcement Signal (HRS) that converts low-confidence alerts into structured learning updates. These components are unified through a vector database that functions as long-term episodic memory, enabling similarity-based reasoning and cross-dataset generalization. Ablation results show that disabling any component degrades zero-day adaptation: removing CPT increases drift sensitivity, removing vector memory prevents knowledge retention, and removing human feedback collapses learning to static inference. Using a class-exclusion zero-day protocol on NSL-KDD, UNSW-NB15, and CICIDS2017, CNIDS raises zero-day detection from 0% to 18.2% while maintaining precision above 80% and stabilizing false positives. Full article

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

The convergence of artificial intelligence (AI), explainable AI (XAI), and neuroscience is fostering new opportunities for understanding both machine and biological intelligence through interpretable and human-centered learning paradigms. In this Perspective, we introduce XAI2Brain as a conceptual framework for brain–AI alignment, positioning mechanistic interpretability as an intermediate layer connecting neural network representations, human understanding, and neuroscience-inspired AI design. Rather than viewing XAI solely as a post hoc transparency tool, we emphasize its emerging role in enabling mechanistic analysis of internal model representations, concept-level reasoning, and interactive human–AI alignment. We define XAI2Brain as a multi-level conceptual framework rather than a deployable system, explicitly aimed at structuring brain–AI alignment across representation-level, mechanism-level, and interaction-level perspectives. We survey the evolution of XAI methodologies—from feature attribution and concept-based explanations to mechanistic and human-centric interpretability approaches—and discuss how these methods may support bidirectional knowledge transfer between AI systems and cognitive neuroscience. Importantly, we adopt a cautious stance on brain–AI analogy, explicitly recognizing that artificial neural representations are not equivalent to biological neural representations, and instead focusing on functional and informational correspondences rather than structural equivalence. Unlike conventional human-in-the-loop or reinforcement learning from human feedback paradigms that primarily optimize behavioral outputs, XAI2Brain focuses on cognitively interpretable and mechanistically grounded alignment between AI systems and human reasoning processes. This alignment promotes interactive human-in-the-loop intelligence, empowering humans to comprehend, guide, and refine AI systems, while enabling AI systems to better interpret human instructions, intentions, and contextual reasoning. We further discuss the challenges of scaling explainability to large generative and multimodal models, including issues of interpretability robustness, cognitive compatibility, evaluation, and ethical accountability. We also highlight key limitations of current mechanistic interpretability methods, including explanation instability, representation superposition, and lack of causal guarantees, underscoring that these challenges remain open research problems. Rather than proposing a complete artificial brain architecture, this Perspective outlines a research roadmap toward more interpretable, adaptive, and neuroscience-inspired AI systems capable of supporting future brain–AI integration and collaborative intelligence. We additionally clarify that this work follows a narrative perspective review methodology with structured thematic synthesis of the literature. By framing explainability as a bridge between mechanistic AI understanding, cognitive science, and human-centered interaction, XAI2Brain highlights the importance of interpretable alignment for the next generation of brain-inspired AI systems. Full article

Background/Objectives: Alzheimer’s disease (AD) disproportionately affects women, who account for nearly two-thirds of affected individuals worldwide. This sex imbalance cannot be explained by longevity alone and likely reflects complex interactions among biological sex, endocrine aging, genetic susceptibility, and brain-specific mechanisms of vulnerability. Neuroimaging has played a pivotal role in characterizing these sex-related differences in vivo, enabling the assessment of amyloid-β deposition, tau propagation, neurodegeneration, cerebral glucose metabolism, and network reorganization. This invited review examines AD through a rigorously sex-specific neuroimaging perspective, with particular emphasis on implications for women’s brain health. Methods: We integrated evidence from structural MRI, FDG-PET, amyloid-PET, tau-PET, estrogen receptor PET, diffusion MRI, and fluid biomarkers, together with epidemiological, molecular, genetic, and endocrine studies. The review focuses on female-specific trajectories of AD initiation and progression, highlighting the contribution of neuroendocrine aging, menopause, metabolic dysfunction, and sex-modulated genetic risk factors. Results: Available evidence indicates that women exhibit distinct biological and neuroimaging signatures across the AD continuum. Menopause emerges as a critical neuroendocrine transition associated with metabolic decline, altered brain connectivity, increased amyloid and tau vulnerability, and progressive neurodegeneration. Female-specific patterns of tau propagation and sex-dependent interactions with genetic risk factors further contribute to differential disease trajectories. Advanced multimodal neuroimaging approaches have substantially improved the characterization of these mechanisms and their relationship with cognitive decline and clinical progression. Conclusions: A sex-specific neuroimaging framework is essential to improve understanding of AD pathophysiology and to advance precision medicine approaches tailored to women’s brain health. Recognition of endocrine aging and female-specific biological vulnerability may inform earlier identification of at-risk individuals and the development of targeted prevention and treatment strategies. Future research should prioritize sex-aware longitudinal studies and multimodal biomarker integration to optimize personalized interventions in AD. Full article

Accumulation of aggregated amyloid beta (Aβ) species is a defining pathological hallmark of Alzheimer’s disease and is associated with extensive neuronal structural abnormalities. Mild cognitive impairment (MCI), a transitional stage between normal aging and the onset of dementia, is thought to represent an early phase of this pathological continuum. Studies at the cellular level suggest that the conditions impair the maintenance of established neuronal processes/networks and restrict their capacity for elongation or re-elongation. They may also attenuate the activation and process extension of quiescent neural progenitor or stem-like cells. These early cellular changes precede overt neurodegeneration in neural tissue and are likely to contribute to cognitive decline. They highlight the importance of in vitro models for identifying molecular targets involved in recovery from disease. In this study, we investigated the effects of aggregated Aβ (25–35) on neuronal process elongation and associated intracellular events in the N1E-115 cell line, a widely used model of neuronal differentiation. Addition of aggregated Aβ to cultured N1E-115 cells attenuated process elongation in a concentration-dependent manner. This morphological impairment was accompanied by decreased expression of neuronal differentiation markers. In contrast, at the half-maximal inhibitory concentration for process elongation, long-term cultured cells did not exhibit apparent process retraction or degenerative morphology. This mild but progressive impairment, without extensive cell death, is consistent with the cellular features of early-stage conditions rather than advanced Alzheimer’s pathologies. Similar results were observed in primary cortical neurons. Aβ also decreased the level of GTP-bound Ras and phosphorylation of the downstream mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK). Furthermore, treatment with hesperetin, a bioactive flavonoid compound, recovered the Aβ-induced inhibition of neuronal process elongation. Hesperetin also restored Ras and MAPK/ERK states, suggesting that its effects are associated, at least in part, with modulation of signaling through Ras and MAPK/ERK. Our findings suggest that hesperetin may serve as a useful molecular probe for modulating early cellular responses associated with Alzheimer’s disease-related pathology. This in vitro model might serve as a useful platform for investigating the molecular target candidates involved in recovery from nervous system disorders. Full article

Patients with neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) frequently lose the ability to communicate through speech or writing. However, their cognitive and sensory functions are often relatively preserved. In Japan, the traditional method known as kuchimoji (mouth-based character communication) enables character-by-character communication using mouth shapes. This method relies heavily on caregiver skill and is challenging to implement consistently. This study introduces KUCHIMOJI, a Japanese text input system that uses mouth-shape recognition to support independent augmentative and alternative communication (AAC) without caregiver assistance. The system employs a lightweight convolutional neural network (MobileNetV2) to classify six mouth shapes. These shapes correspond to five vowels and a closed-lip state. To accommodate diverse user conditions, a multimodal input framework is designed. It supports three operation modes: facial-image-based signal input, button-based input, and key-based direct input. As an initial feasibility study, experiments with ten healthy participants were conducted to evaluate text entry performance in terms of text entry speed (TES) and miss entry rate (MER). Results indicate that the system achieves average input speeds of 3.86, 5.32, and 11.35 characters per minute (cpm) for the facial-image, button, and key-based modes, respectively. It maintains low error rates (2.96–5.05%). These findings suggest that the system offers a flexible trade-off between speed and accuracy depending on the input modality. The proposed approach provides a practical, low-cost, non-contact communication solution. This underscores its potential forpractical assistive communication applications. Full article

Alzheimer’s disease (AD) and related tauopathies are marked by progressive cognitive decline, synaptic dysfunction, and neuronal loss. The microtubule (MT)-associated protein tau, encoded by the MAPT gene, plays a central role in neurodegenerative pathology. Although the dissociation of hyperphosphorylated tau from MTs and their subsequent aggregation has been extensively studied, the broader landscape of other MT-associated proteins remains largely underexplored. Among these, the end-binding protein (EBP) family, which comprises MT plus-end-tracking proteins, has emerged as a critical regulator of MT dynamics and stability. EBPs modulate MT polymerization, interact with various MT-associated proteins, and influence cytoskeletal organization. Recent studies suggest that pathological tau impairs end-binding protein 3 (EB3) function by limiting its localization to MT plus-ends and inhibiting EB3-mediated MT elongation and stability. In addition, EB1 appears to interfere with tau aggregation in an in vitro study involving biomolecular condensates. Dysregulation of dynamic tau-MT-EBP interactions may result in structural and functional consequences throughout the entire network, potentially increasing MT instability under neurodegenerative conditions. Hence, the tau-MT-EBP network is an emerging mechanistic axis for advancing the understanding of physiological processes, disease pathology, and therapeutic interventions. In this review, we summarize recent advances in understanding the tau-MT-EBP axis and highlight the molecular mechanisms underlying key pathological interactions within this network. Finally, we discuss current therapeutic strategies and future directions for targeting this dynamic axis to mitigate AD and related tauopathies. Full article

I present a quantum-tunnelling oscillator model as a universal dynamical engine for two paradigmatic problems in quantum cognition theory—optical illusion perception and group decision making—where individuals are treated as quantum-mechanical agents whose choices shift through context-dependent transitions rather than simple probabilities. I show that, when networked together, these units form a quantum-cognitive neural system that reproduces familiar collective and perceptual phenomena while naturally accommodating counterintuitive processes that challenge classical models. Bridging ideas from quantum cognition theory and neural networks, this approach offers a compact, physically grounded way to describe how real individuals and groups think, perceive and decide. Full article

Background/Objectives: Assessing inhibitory function and error detection is crucial for the early detection of age-related cognitive decline. This study aimed to investigate the neural network dynamics underlying these functions in younger and older adults to better understand age-related changes in cognitive control. Methods: We recorded electroencephalograms (EEGs) during an inhibitory control task in 17 older and 15 younger healthy adults. Behavioral performance was assessed, and directional functional connectivity was analyzed using Low-Resolution Electromagnetic Tomography (LORETA), isolated effective coherence (iCoh), and Full Vector Field analysis across the theta, alpha, and beta frequency bands. Results: Older adults showed significantly fewer correct responses than younger adults. During incorrect responses, older adults exhibited strong beta-band directionality from the ventral anterior cingulate cortex (ACC) to the left frontal polar cortex (FPC), alongside strong intra-ACC connectivity. During correct responses, they demonstrated alpha- and beta-band directionality from the left dorsolateral prefrontal cortex (DLPFC) to the right FPC. Conversely, compared with older adults, younger adults demonstrated significantly stronger mutual directionality within the ACC and widespread robust connectivity among the ACC, bilateral DLPFC, and FPC during correct responses. Conclusions: Efficient inhibitory control in older adults appears to rely on higher-order error-monitoring and error detection networks. The altered network dynamics in older adults suggest an age-related decline in immediate cognitive control. Evaluating these neural networks via EEGs provides a potential non-invasive biomarker for early cognitive decline and highlights higher-order executive control as a promising target for preventive interventions. Full article

Visual working memory (VWM) declines with age and has been explained by multiple mechanisms, including reduced precision, capacity limitations, binding deficits, and altered attentional control. However, these accounts are typically framed within a unitary processing architecture and do not fully capture the distributed nature of visual cognition. This review advances a coordination-based framework in which age-related differences in VWM are understood as partly reflecting reduced efficiency in integrating and regulating representations across the two cerebral hemispheres. Behavioural, electrophysiological, and neurophysiological evidence is synthesised to characterise the role of inter-hemispheric communication in VWM. Age-related changes in corpus callosum structure and function are then considered in relation to these coordination processes. Deficits in precision, capacity, binding, and attention are proposed to reflect different behavioural expressions of a common limitation in coordinating distributed representations, providing a unifying account of multiple behavioural signatures, particularly under conditions that place high demands on inter-hemispheric coordination. The framework offers a mechanistic explanation of the task-dependent nature of ageing effects and generates testable predictions for future research, highlighting the role of network-level coordination mechanisms in cognitive ageing. Full article

Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, making it difficult to provide architects with understandable and traceable grounds for judgment. This limits their practical value in the architectural field, as designers require not only accurate outputs but also interpretable explanatory evidence regarding the basis of decision-making. This issue is particularly critical in architectural interpretation, where judgments are rarely made solely on the basis of isolated visual features, but are instead often formed through comparison and negotiation with precedents, spatial logic, and domain knowledge. To address this challenge, this paper proposes ArchiExplain, a multi-level interpretability framework for architectural image understanding, aiming to enable a deeper understanding of architectural images. The main contributions of this study are threefold: (1) We construct two architectural datasets for interpretability evaluation: a facade dataset composed of streetscape images from Harbin, China, and Greece, and a floor-plan dataset consisting of Real-plan drawings from real design cases and standardized generated R-plan drawings. Unlike existing datasets that primarily serve style recognition, semantic parsing, or image generation tasks, the datasets in this paper focus on evaluating the correspondence among model explanations, precedent associations, visual evidence, and predictive judgments. (2) Based on the above datasets, we propose the ArchiExplain framework. Unlike attribution methods such as Grad-CAM, Saliency Maps, and Integrated Gradients, which mainly reveal local discriminative regions, or influence-based methods that only trace the influence of training samples, this framework integrates training-sample influence tracing, Saliency Maps, and Integrated Gradients. It establishes a unified evidential chain among precedent samples, discriminative image regions, and final predictions, thereby transforming neural network decisions into an interpretable reasoning process with architectural significance. (3) Experimental results show that ArchiExplain performs stably on 100 randomly selected test samples, achieving an accuracy of 98.41% in the facade classification task and 98.34% in the floor-plan classification task. Further deletion/occlusion faithfulness analysis shows that the main attribution methods outperform the random baseline. Meanwhile, a questionnaire study involving 28 architects further verifies the consistency between model explanations and human architectural cognition. These findings indicate that ArchiExplain can enhance the transparency of architectural deep learning models and has practical application potential in architectural design analysis, model diagnosis, and precedent-based learning. Full article

Background/Objectives: Chimeric antigen receptor T-cell (CAR-T) therapy has transformed outcomes in relapsed or refractory hematologic malignancies, but long-term cognitive outcomes remain poorly understood. We compared the incidence and time course of cognitive impairment and associated neurological complications after CAR-T therapy compared with autologous stem cell transplantation (ASCT). Methods: This retrospective, propensity-matched cohort study utilized the TriNetX US Collaborative Network (January 2014–April 2025). To ensure concurrent comparisons, ASCT recipients were restricted to an index date beginning in August 2017 or later. CAR-T recipients were matched 1:1 to ASCT recipients for demographics, disease, comorbidities, prior and concomitant treatments, and laboratory parameters. The primary endpoint was time to cognitive impairment, as defined by ICD-10 codes. Results: After comparing 3067 CAR-T patients (median follow-up 634 days) with 3067 ASCT patients (median follow-up 713 days), CAR-T recipients had a higher risk of cognitive impairment (HR 1.58; 95% CI 1.39–1.80; p < 0.001). Because the risks were not proportional (Schaenfeld p < 0.001), the difference was also expressed as restricted median survival time (RMST): CAR-T recipients spent approximately 25 and 53 days fewer days without cognitive impairment at 1 and 2 years, respectively (both p < 0.001). The risk was greatest at 30 days (HR 4.22; 95% CI 3.23–5.53), but remained elevated in control analyses at 30 and 90 days that excluded the acute ICANS window (HR 1.30 and 1.25, respectively; both p < 0.05). Neurological dysfunction, particularly encephalopathy (HR 2.04; 95% CI 1.73–2.40), was more common after CAR-T. Conversely, CAR-T was associated with a reduced risk of secondary acute myeloid leukemia (HR 0.46; 95% CI 0.38–0.55; p < 0.001). Conclusions: CAR-T therapy is associated with a higher risk of cognitive impairment that persists beyond the acute phase. As these are observational, code-based data, they should be interpreted as associations rather than evidence of a specific mechanism, and they highlight the need for informed consent discussions, long-term neurocognitive monitoring, and the development of neuroprotective strategies. Full article

Probabilistic risk assessment of complex offshore oil and gas systems is often challenged by scarce statistical data and multiple uncertainties. Traditional point-value probability and standard Bayesian networks cannot fully represent and propagate these uncertainties, which may mislead high-risk security decision-making. To address this issue, this paper proposes a new hybrid risk assessment framework that combines interval-valued T-spherical fuzzy sets (IVTSFS) with credal networks (CN). First, IVTSFS is used to quantify the subjective risk perception of multiple experts, effectively capturing hesitancy, fuzziness, and group disagreement. An improved probability mapping mechanism is introduced to align linguistic evaluations with objective failure frequency spaces, thereby avoiding systemic transformation biases. Subsequently, the interval conditional probability table is constructed using the imprecise leakage noise-OR model, which alleviates the problem of parameter dimension explosion in complex causal structure and explicitly retains the parameter uncertainty. The 2U algorithm is then applied to perform accurate interval inference in CN. The feasibility and comparative advantages of the method are illustrated in the actual case of the single-point mooring system. The results clearly output the upper and lower bounds of the system failure risk, and identify the key vulnerable nodes through diagnostic reasoning and sensitivity analysis. This study has theoretical contributions in fuzzy decision-making and uncertainty modeling. By unifying advanced fuzzy cognitive quantification and imprecise probability propagation, it provides a structured uncertainty representation tool for expert-informed risk screening under data scarcity. Full article