Search Results (4,959)

The neural mechanisms of auditory and visual processing are not only a core research focus in cognitive neuroscience but also hold critical importance for the development of brain–computer interfaces, neurological disease diagnosis, and human–computer interaction technologies. However, EEG-based studies on classifying auditory and visual brain activities largely overlook the in-depth utilization of spatial distribution patterns and frequency-specific characteristics inherent in such activities. This paper proposes an analytical framework that constructs symmetrical spatio-temporal–frequency feature association vectors to represent brain activities by computing EEG microstates across multiple frequency bands and brain functional connectivity networks. Then we construct an Adaptive Tensor Fusion Network (ATFN) that leverages feature association vectors to recognize brain activities related to auditory, visual, and audiovisual processing. The ATFN includes a feature fusion and selection module based on differential feature enhancement, a feature encoding module enhanced with attention mechanisms, and a classifier based on a multilayer perceptron to achieve the efficient recognition of audiovisual brain activities. The feature association vectors are then processed by the Adaptive Tensor Fusion Network (ATFN) to efficiently recognize different types of audiovisual brain activities. The results show that the classification accuracy for auditory, visual, and audiovisual brain activity reaches 96.97% using the ATFN, demonstrating that the proposed symmetric spatio-temporal–frequency feature association vectors effectively characterize visual, auditory, and audiovisual brain activities. The symmetrical spatio-temporal–frequency feature association vectors establish a computable mapping that captures the intrinsic correlations among temporal, spatial, and frequency features, offering a more interpretable method to represent brain activities. The proposed ATFN provides an effective recognition framework for brain activity, with a potential application for brain–computer interfaces and neurological disease diagnosis. Full article

Female cancers, including breast and gynecological malignancies, are among the most prevalent oncological conditions worldwide. Advances in screening, diagnosis, and treatment have markedly improved survival, resulting in a growing population of female cancer survivors. Consequently, long-term health and quality of life have become essential aspects of comprehensive cancer care. Among survivorship issues, sleep disturbances—particularly insomnia—are highly prevalent and associated with adverse outcomes including mood and cognitive impairment, fatigue, immune and cardiometabolic dysregulation, and reduced adherence to therapy. Insomnia, defined as difficulty initiating or maintaining sleep or experiencing poor sleep quality with daytime impairment, affects 6–10% of the general population and is more common in women. In cancer survivors, poor sleep quality appears to be three times more frequent, reaching 62% in breast cancer survivors, although these data may be underestimated, especially for other cancer types, due to the small sample size and heterogeneity of the studies. The pathogenesis of insomnia in female cancer patients is multifactorial, involving cancer-related inflammation, hypothalamic–pituitary–adrenal axis dysregulation, neuroimmune alterations, treatment effects, psychological distress, and behavioral factors. Hormonal disruption plays a central role, as oncological treatments are often the cause of iatrogenic menopause, leading to vasomotor symptoms, mood and cognitive disturbances, sexual dysfunction, and genitourinary complaints, all contributing to sleep disruption. Importantly, estrogens and progesterone independently regulate sleep–wake pathways via central mechanisms, influencing sleep quality even in the absence of vasomotor symptoms. Management requires a multidisciplinary approach integrating oncology, gynecology, and sleep medicine. Cognitive Behavioral Therapy for Insomnia (CBT-I) is first-line, while pharmacologic options include benzodiazepines, Z-drugs, SSRIs/SNRIs, melatonin, or new medication like DORAs. Menopausal hormone therapy (MHT) should be considered for premature menopause management in selected women without contraindications, improving both vasomotor symptoms and sleep quality. Emerging neurokinin receptor (NK-R) antagonists show promise, and ongoing trials suggest significant potential even in breast cancer survivors. Full article

Alzheimer’s Disease (AD) is a neurodegenerative disorder characterised by Amyloid-beta 42 (Aβ42) plaque accumulation and cognitive decline, with current treatments focused on symptomatic relief. Emerging therapeutics, such as dietary interventions, can modulate cognitive decline and delay AD progression. Our previous work in Drosophila melanogaster identified cell competition as a key mechanism that eliminates unfit neurons in an AD model, improving locomotion by removing the unfit neurons expressing flower^LoseB and ahuizotl (azot). Here, we explored how diet influences azot-dependent cell competition and locomotion in the AD model. Flies were fed with either a yeast-based diet (YBD) or a synthetic (SAA) diet for up to 28 days. In contrast to YBD, SAA delayed cell competition activation until day 21, coinciding with locomotion improvement and delayed Aβ formation. The overexpression of the human Flower (hFWE) isoforms in a Drosophila neuronal context revealed functional conservation: hFWE1 acted as the sole loser isoform, and hFWE2 as a winner isoform. With the YBD, forcing cell competition by expressing hFWE2 in the AD model led to an accumulation of unfit cells and promoted worse locomotion phenotypes over time compared to with the SAA diet. Our data highlights the complex interaction between diet, cell competition, and Aβ toxicity, offering new therapeutic insights. Full article

Reading and mathematics are intricately linked through shared cognitive processes that underpin developmental relationships across domains. Despite extensive research on early-grade links between reading and basic arithmetic, gaps persist in understanding how reading comprehension (RC) supports applied math problem-solving (AMP) in older students and non-English contexts. The current study investigates the grade-level relationship between RC and AMP in typically developing Hebrew-speaking fourth (N = 41) and eleventh graders (N = 43), focusing on the contributions of working memory (WM), reading fluency, and arithmetic fluency. Results indicated significant positive associations between RC and AMP in both age groups. In fourth graders, arithmetic fluency partially statistically mediated the RC-AMP relationship in a cross-sectional mediation model. This indicates that students rely on computational proficiency to translate textual understanding into solutions. In contrast, eleventh graders exhibited a direct RC-AMP link, reflecting advanced comprehension and metacognitive strategies as computational skills are automatized. WM showed stronger correlations with RC and AMP among younger students, whereas these associations were weaker in older students. These findings support a Developmental Linguistic–Cognitive Scaffold Model, highlighting age-related shifts in cognitive and linguistic mechanisms supporting AMP. The results emphasize the need for integrated curricula incorporating RC strategies to enhance mathematical reasoning, particularly in morphologically rich languages like Hebrew. Full article

Early age of onset is a major predictor of poor disease course in Bipolar Disorder (BD) and Schizophrenia (SCZ), often associated with greater symptom severity, cognitive decline, and worse outcomes. However, the biological mechanisms that shape age- and sex-specific vulnerability remain unclear, limiting progress toward early identification and intervention. To address this gap, we conducted an integrative transcriptomic study of 369 postmortem dorsolateral prefrontal cortex samples from the CommonMind Consortium. Differential gene expression, Weighted Gene Co-Expression Network Analysis, and gene set enrichment analysis were applied to identify pathways associated with age of onset, complemented by sex-stratified models and cellular deconvolution. To assess predictive signals, we applied a rigorous two-stage machine-learning framework using nested cross-validation, with Lasso feature selection followed by L2-regularized logistic classification. Performance was evaluated solely on held-out test folds. Genes and modules linked to earlier onset showed consistent enrichment for calcium signaling, with downregulation of CACNA1C and multiple adenylate-cyclase-related transcripts, while female-specific analyses revealed selective dysregulation of cyclase-associated pathways. Network analysis identified a calcium-enriched module associated with onset and sex, and diagnosis-specific modeling highlighted MAP2K7 in early-onset BD. The predictive model achieved an AUC of 0.63, and the top 50 machine-learning features were significantly enriched in calcium signaling pathway. These findings converge on calcium–cAMP signaling networks as key drivers of early psychiatric vulnerability and suggest biomarkers for precision-targeted interventions. Full article

The COVID-19 pandemic has demonstrated that its effects go far beyond the initial respiratory illness, with many survivors experiencing lasting neurological problems. Some patients develop a condition known as Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), which includes current issues such as reduced cognitive function, chronic headaches, depression, neuropathic pain, and sensory disturbances. These symptoms can severely disrupt daily life and overall well-being. In this narrative review, we provide an overview of current understanding regarding the neurological effects of COVID-19, with a focus on Long COVID. We discuss possible underlying mechanisms, including direct viral invasion of the nervous system, immune-related damage, and vascular complications. We also summarize findings from cohort studies and meta-analyses that explore the causes, symptom patterns, and frequency of these neurological issues. Approximately one-third of people who have had COVID-19 report neurological symptoms, especially those who experienced severe illness or were infected with pre-Omicron variants. Emerging research has identified potential biomarkers such as neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP) that may help in diagnosis. Treatment approaches under investigation include antiviral medications, nutraceuticals, and comprehensive rehabilitation programs. Factors like older age, existing health conditions, and genetic differences in ACE2 and TMPRSS2 genes may affect an individual’s risk. To effectively address these challenges, current research is essential to improve diagnostic methods, develop targeted treatments, and enhance rehabilitation strategies. Ultimately, a coordinated, multidisciplinary effort is crucial to reduce the neurological impact of Long COVID and support better recovery for patients. Full article

Background: Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 ± 0.7 years; height 1.43 ± 0.08 m; body mass 38.6 ± 6.2 kg; BMI 18.7 ± 2.1 kg/m²) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical–cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)—were trained using a 70/30 train–test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Conclusions: Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. Full article

Memory constitutes a fundamental cognitive domain, and converging evidence suggests that its dysfunction represents a prominent, though not exclusive, transdiagnostic dimension across major psychiatric disorders. This review aimed to integrate neurobiological, cognitive, and clinical evidence on domain-specific memory impairments in mood, anxiety, obsessive–compulsive, post-traumatic stress, and psychotic disorders. A comprehensive search was conducted on PubMed, Scopus, and Web of Science up to November 2025 for peer-reviewed studies examining short-term, working, long-term, episodic, semantic, and prospective memory, prioritizing both landmark and recent contributions. Two recurrent transdiagnostic patterns emerged: (i) consistent impairments in working-memory control, and (ii) reduced episodic/autobiographical specificity, while procedural memory appeared relatively preserved. Disorder-specific profiles include overgeneral autobiographical memory in major depression, enduring working and episodic deficits in bipolar disorder, variable impairments in anxiety disorders, functional rather than structural memory inefficiencies in obsessive–compulsive disorder, broad mnemonic disorganization in post-traumatic stress disorder, and pervasive working and episodic deficits in schizophrenia and related psychoses. Across conditions, converging neurobiological data implicate fronto-hippocampal dysconnectivity, altered plasticity, and impaired consolidation processes. Unlike previous reviews, this work syntetisizes evidence across multiple memory systems and across major psychiatric categories, linking neurobiological mechanisms with cognitive and clinical manifestations to support a dimensional, transdiagnostic interpretation of memory dysfunction. These findings could suggest that memory dysfunction represents a recurrent and clinically relevant dimension across psychiatric conditions, warranting further mechanistic and longitudinal investigation. Full article

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by persistent social communication difficulties, restricted interests, repetitive behaviors, and frequent medical comorbidities. Although early brain development in ASD has been extensively investigated, its biological progression across adulthood and aging remains largely unexplored. Growing evidence suggests that perivascular space (PVS) abnormalities may indicate impaired neurovascular integrity and reduced glymphatic clearance in ASD. Enlarged perivascular spaces (ePVS) in children commonly present alongside increased extra-axial CSF accumulation and more severe clinical manifestations, consistent with early alterations in CSF homeostasis and neuroimmune signaling. However, whether these abnormalities persist or evolve with aging remains unknown. Given that glymphatic and vascular integrity decline with age, and adults with ASD show elevated rates of sleep, metabolic, and cardiovascular disorders, PVS alterations may represent a unifying mechanism linking early neurodevelopmental divergence with later neurovascular vulnerability and cognitive aging. Advances in ultra-high-field MRI and automated segmentation now enable precise in vivo quantification of PVS burden, offering new opportunities for lifespan studies. By combining structural and functional methodologies, researchers may determine whether PVS constitute enduring traits, dynamic indicators of disease, or actionable therapeutic targets. Understanding their trajectories could provide critical insights into the continuum between neurodevelopmental and neurodegenerative phenomena in autism. Full article

The mosaic brain evolution perspective states that the relative sizes and functions of brain regions adapt to living environments and behavioural motivation. Early life adversity brings changes to brain structure, function, and patterns of cognitive processing of food cues. Specific brain development patterns are associated with subsequent disordered eating, which, on the one hand, increases the risk of obesity and metabolic syndrome, and, on the other hand, leads to mental health problems, such as depression and anxiety. This review intends to synthesise aberrant brain development indices, describe aberrant brain developmental trajectories, summarise aberrant neural markers of cognitive processing of food cues, conclude how early life adversity affects disordered eating through aberrant brain development patterns, and provide neural implications for future disordered eating research and intervention. Full article

Post-Traumatic Stress Disorder (PTSD) is consistently linked to multidimensional working memory (WM) impairments, encompassing deficits in sustained attention, verbal and visuospatial processing, and executive control, with inhibitory dysfunction emerging as a key feature. This scoping review synthesizes evidence from 39 studies examining neurobiological mechanisms, trauma-related factors, genetic and hormonal influences, gender differences, and task-specific variability. Findings indicated that PTSD is associated with altered activation and connectivity in the prefrontal cortex, hippocampus, and related neural networks, often resulting in compensatory but inefficient recruitment patterns. Emotional distraction and comorbidities such as depression, alcohol use, and traumatic brain injury can exacerbate cognitive deficits. Performance impairments are evident across both emotional and neutral WM tasks, with visuospatial and updating processes being particularly vulnerable. Risk factors include chronic trauma exposure, older age, APOE ε4 allele, and the BDNF Val66Met (rs6265) polymorphism, while modulators such as oxytocin, cortisol, and physical activity show potential cognitive benefits under specific conditions. Methodological heterogeneity and limited longitudinal data restrict generalizability. These findings underscore the importance of early screening, targeted cognitive interventions, and inclusion of underrepresented populations to refine prevention and treatment strategies for PTSD-related WM deficits. Full article

Recent advancements in multimodal large language models (MLLMs) have demonstrated remarkable capabilities in processing diverse data types, yet significant disparities persist between human cognitive processes and computational approaches to multimodal information integration. This research presents a systematic investigation into the parallels between human cross-modal chunking mechanisms and token representation methodologies in MLLMs. Through empirical studies comparing human performance patterns with model behaviors across visual–linguistic tasks, we demonstrate that conventional static tokenization schemes fundamentally constrain current models’ capacity to simulate the dynamic, context-sensitive nature of human information processing. We propose a novel framework for dynamic cross-modal tokenization that incorporates adaptive boundaries, hierarchical representations, and alignment mechanisms grounded in cognitive science principles. Quantitative evaluations demonstrate that our approach yields statistically significant improvements over state-of-the-art models on benchmark tasks (+7.8% on Visual Question Answering (p < 0.001), 5.3% on Complex Scene Description) while exhibiting more human-aligned error patterns and attention distributions. These findings contribute to the theoretical understanding of the relationship between human cognition and artificial intelligence, while providing empirical evidence for developing more cognitively plausible AI systems. Full article

Material deprivation, defined as the inability to afford essential goods and services, is a key determinant of psychological well-being across Europe. While prior research links deprivation to lower well-being and diminished institutional trust, few or no studies to date have examined how trust itself might operate as a mechanism connecting these phenomena in an entrepreneurial context. The current study investigates whether institutional trust mediates the relationship between material deprivation and mental well-being among self-employed individuals across Europe. Drawing on data from the 2016 European Quality of Life Survey (N = 2373), the analysis focuses on the self-employed, a group particularly vulnerable to material insecurity due to limited access to welfare protections. Mental well-being is measured through positive emotions, energy levels, restfulness, and a sense of fulfilment, while institutional trust refers to confidence in government, parliament, the legal system, and local authorities. The results of structural equation modelling show that material deprivation is negatively associated with both institutional trust and mental well-being and that trust partially mediates this link. The findings suggest that when self-employed individuals face material deprivation, reduced trust in public institutions partly explains their lower well-being. This study contributes to entrepreneurial well-being research by highlighting the role of institutional trust as a cognitive belief-based mechanism through which economic insecurity affects mental well-being. Full article

Precision perioperative medicine connects mechanisms across inflammation, metabolism, and neuroimmunomodulation to predict risk and individualize therapy. This review aims to incorporate landmark concepts and recent studies (2017–2025) as well as outlining how multi-omics and clinical analytics translate biology into actionable pathways. Key opportunities include cytokine-guided risk stratification, metabolic conditioning, and autonomic neuromodulation targeting the cholinergic anti-inflammatory reflex. Implementation requires robust phenotyping, interoperable data pipelines, and trials focused on functional recovery and cognition. Full article

Background/Objectives: Tinnitus causes significant cognitive and emotional distress; however, its clinical assessment mostly relies on subjective measures without evaluation of objective indices. In this narrative review, we examined the potential of electroencephalography (EEG)-based neurophysiological markers as objective biomarkers in tinnitus assessment. Methods: The Web of Science, PubMed, EMBASE, and MEDLINE databases were searched to identify research articles on EEG-based analysis of individuals with tinnitus. Studies in which treatment and control groups were compared across four analytical domains (spectral power analysis, functional connectivity, microstate analysis, and entropy measures) were included. Qualitative synthesis was conducted to elucidate neurophysiological mechanisms, methodological characteristics, and clinical implications. Results: Analysis of 18 studies (n = 1188 participants) revealed that tinnitus is characterized by distributed neural dysfunction that extends beyond the auditory system. Spectral power analyses revealed sex-dependent, frequency-specific abnormalities across distributed brain regions. Connectivity analyses demonstrated elevated long-range coupling in high-frequency bands concurrent with diminished low-frequency synchronization. Microstate analyses revealed alterations in spatial configuration and transition probabilities. Entropy quantification indicated elevated complexity, particularly in the frontal and auditory cortices. Conclusions: EEG-derived neurophysiological markers demonstrate associations with tinnitus in group analyses and show potential for elucidating pathophysiological mechanisms. However, significant limitations, including low spatial resolution, small sample sizes, methodological heterogeneity, and lack of validation for individual-level diagnosis or treatment prediction, highlight the need for cautious interpretation. Standardized analytical protocols, larger validation studies, multimodal neuroimaging integration, and demonstration of clinical utility in prospective trials are required before EEG markers can be established as biomarkers for tinnitus diagnosis and management. Full article