Search Results (289)

Major depressive disorder (MDD) accounts for a substantial share of global disability-adjusted life years and remains inadequately treated despite pharmacological advances. Growing evidence implicates endocrine-disrupting chemicals (EDCs)—particularly phthalates and bisphenols—as environmental contributors to the onset of these conditions. This narrative review examines evidence from PubMed, Scopus, and Web of Science (January 2000–March 2025) on the relationship between early-life exposure to these compounds and the development of mood disorders, with emphasis on molecular and neurobiological mechanisms. Phthalates such as di(2-ethylhexyl) phthalate (DEHP), and bisphenols such as bisphenol A (BPA), are detected ubiquitously in human urine, blood, placenta, and umbilical cord blood. Key mechanisms identified include Nrf2/HO-1-driven oxidative stress and neuronal apoptosis, disruption of calcium signalling and synaptic plasticity via CREB phosphorylation deficits, epigenetic suppression of brain-derived neurotrophic factor (BDNF) via promoter hypermethylation, NF-κB/NLRP3/IL-1β neuroinflammatory cascades, interference with thyroid hormone bioavailability through transthyretin competition, and PPAR-mediated disruption of brain lipid metabolism. Prenatal and early-life exposure has been associated with ADHD, cognitive impairment, autism spectrum disorder, and elevated risk of depressive and anxiety phenotypes in epidemiological cohorts. Psychological vulnerability factors—perceived stress, deficient emotion regulation, and adverse childhood experiences—likely amplify this biological susceptibility through HPA axis sensitisation. Methodological limitations of current evidence, including reliance on single-spot urine samples and residual confounding, are critically appraised. Future research priorities include longitudinal biomonitoring cohorts, brain organoid mechanistic models, and integration of validated psychiatric assessments into environmental health study designs. Full article

Redundant channels, sparse electrode topology, and insufficient cross-layer feature fusion limit electroencephalography (EEG)-based emotion recognition. This study proposes the Topology-Aware Dual-Bridge Fusion Network (TDBF-Net), a compact framework that integrates adaptive channel selection, topology-aware sparse convolution, and bidirectional bridge fusion. First, sample entropy, dispersion entropy, and fuzzy entropy are fused to estimate channel importance, while particle swarm optimization (PSO) learns the entropy weights and an elbow-based criterion determines the retained channel subset. Second, differential entropy (DE) features from the $\theta$, $\alpha$, $\beta$, and $\gamma$ bands are mapped to an $8\times 9$ sparse topological tensor according to electrode locations. A fixed spatial validity mask is applied before and after convolution to suppress invalid responses from zero-padded regions and preserve real electrode topology. Third, a dual-bridge fusion module recalibrates shallow and deep features in both directions through channel attention and gated fusion, and a bidirectional long short-term memory network (BiLSTM) further captures short-term temporal dependencies. Subject-dependent experiments on the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP) show that TDBF-Net achieves $97.62\%$ ± $1.59\%$ accuracy on SEED and $98.46\%$ ± $0.94\%$ and $98.14\%$ ± $0.77\%$ on DEAP valence and arousal, respectively. Paired DEAP ablations support topology and bridge contributions for valence, whereas the corresponding arousal differences are not significant. Selector controls, robustness tests, computational profiling, and held-out visualizations further characterize the method’s compression, cost, and interpretability. The evidence supports TDBF-Net as an effective subject-dependent framework while leaving subject-independent and cross-dataset generalization for future validation. Full article

Why do humans produce dysphoric dreams and nightmares at all? This paper develops a cautious evolutionary theory of unpleasant dreaming by extending the logic of evolutionary psychiatry to dream life. Research on dream continuity suggests that dreams are not detached from waking mental life, but instead reflect current concerns, emotional preoccupations, memory fragments, and bodily states. Work on threat simulation, emotional processing during sleep, and the neurocognitive structure of nightmares further suggests that some disturbing dreams may not be mere malfunctions. Rather, they may be costly outputs of systems involved in salience tracking, threat rehearsal, emotional adaptation, or flexible model updating. At the same time, nightmare research shows that recurrent and trauma-linked nightmares can become clinically serious, producing sleep disruption, daytime distress, and functional impairment. This paper argues that the proper distinction is not between “useful dreams” and “pathological nightmares” in any simple sense, but between unpleasant dream processes that remain self-limiting and those that cross a threshold into maladaptive feedback loops. On that basis, dream engineering is defended not as routine suppression of unpleasant dreaming, but as a selective and limited intervention appropriate when recurrence, rigidity, carryover, and impairment indicate dysfunction. A structured research agenda is proposed to test this threshold model and to distinguish adaptive bad dreams from pathological nightmares. Full article

Hypnotic hypoalgesia effectively reduces various types of pain, but its neurophysiological mechanisms remain unclear. EEG and somatosensory event-related potentials (SERPs) can help elucidate how hypnotic suggestions influence pain processing across sensory, emotional, and cognitive domains. This review synthesizes evidence on EEG oscillatory activity and SERP correlates in hypnotic pain modulation and interprets the findings through predictive coding and active inference frameworks. The review integrated relevant studies on EEG frequency-band oscillations, SERPs, and the modulation of pain by hypnosis, focusing on a convergent neurophysiological account of hypnotic pain modulation rather than on individual studies. Research indicates that hypnotic hypoalgesia is associated with concerted changes across multiple EEG frequency bands. Theta activity is linked to focused internal attention and the maintenance of suggestion-consistent representations, while increased alpha activity may indicate inhibitory sensory gating and reduced nociceptive gain. Beta oscillations enhance pain relief in cognitive and motor functions, while decreased gamma activity indicates reduced significance of pain signals. SERP findings show that hypnotic suggestions primarily affect late evaluative components (e.g., P200/P250, P300) rather than early sensory elements, suggesting changes in salience assignment and affective-cognitive appraisal rather than uniform suppression of nociceptive input. Predictive coding and active inference explain how hypnotic suggestions change pain-related expectations and bodily perceptions. This insight is key to enhancing personalized pain management and guiding future neuroscience research. Full article

Children undergoing cancer treatment face repeated, prolonged hospitalisations carrying substantial risks of psychological distress, social isolation, and impaired development. Volunteers offering structured non-medical activities are widely employed to complement professional psychosocial care in paediatric oncology settings, yet empirical evidence examining their role specifically—as distinct from professionally delivered psychosocial interventions—remains sparse. This narrative review, conducted in accordance with SANRA criteria and drawing on a structured search of PubMed, PsycINFO, and Google Scholar (2000–2025), supplemented by a targeted verification search in July 2026, synthesises the theoretical and preliminary empirical basis for volunteer involvement, critically evaluates associated clinical risks, and proposes evidence-informed recommendations for paediatric oncology clinical practice. Three theoretical frameworks provide the conceptual grounding for volunteer activities: developmental psychological models (Piaget; Erikson), stress and coping theory (Lazarus and Folkman), and the biopsychosocial model (Engel). Indirect empirical evidence—derived predominantly from professionally delivered interventions—supports short-term benefits of structured distraction, play, and social activities on procedural anxiety, pain experience, and sense of social inclusion; the sole study examining volunteer-facilitated activities specifically in paediatric oncology reported meaningful reductions in distress and high family satisfaction. Eight categories of clinical risk are identified, including physical and psychological overload, erosion of child autonomy, suppression of authentic emotional expression, and inadequate coordination with healthcare staff. Six practice recommendations are proposed, centred on a working definition of the volunteer role, mandatory pre-access training, genuine individualisation including cultural adaptation, multidisciplinary-team-led coordination, and structured programme evaluation. Volunteer involvement can meaningfully complement professional psychosocial care when delivered within a structured, supervised, and multidisciplinary-team-coordinated framework. The paediatric oncology team occupies a pivotal role in realising the potential benefits while safeguarding children from associated risks. Prospective research employing standardised volunteer programme definitions and validated child-reported outcome measures is urgently needed to build an evidence base commensurate with the clinical importance of this practice. Full article

Personal growth often requires people to cope with setbacks and the negative emotions they evoke. Romantic partners can serve as important sources of support in these situations. In this research, we examined which interpersonal emotion regulation (IER) strategies are most effective for supporting two common goals in such contexts: improving emotional well-being and facilitating goal pursuit. Across three studies, we investigated the effectiveness of seven IER strategies (i.e., interpersonal acceptance, reappraisal, problem-solving, co-rumination, distraction, suppression and ignoring) for helping one’s partner feel better (hedonic goal) vs. achieve their goal (instrumental goal). Study 1 assessed whether the perceived effectiveness of these IER strategies was dependent on people’s hedonic vs. instrumental regulatory goal. Study 2 examined naturally occurring IER in romantic relationships, testing which IER strategies were related to perceived efficacy of the partner in fostering hedonic and instrumental goal achievement. Study 3 used a one-month longitudinal design to examine changes in negative affect and goal progress over time. Results showed that while people perceive IER strategies as differentially effective for hedonic versus instrumental goals, strategies that address obstacles (i.e., problem-solving) may be especially effective in helping romantic partners support both emotional well-being and goal pursuit, two processes central to personal growth. Full article

For decades, visual perception was treated as a linear, feature-extracting mechanism driven almost exclusively by bottom-up sensory inputs. Emerging insights from affective neuroscience and cognitive psychology have systematically dismantled this view, revealing that vision operates within a continuous, bidirectional dialog with emotional systems. This review synthesizes the multi-layered neurobiological architectures underpinning this relationship. The pathways through which top–down emotional states recalibrate sensory processing are analyzed. Mechanisms including amygdalocortical feedback, frontoparietal attentional networks, and insular interoceptive monitoring are examined. These systems prioritize survival-driven motivational salience over objective accuracy. In the opposite direction, the text charts how ambient environmental features, such as lighting dynamics, spatial geometry, and structural ambiguity, immediately register along rapid subcortical and detailed cortical streams to instantiate emotional states. By situating these reciprocal dynamics within predictive coding and active inference frameworks, this paper illustrates how affective states function as precision weights that dynamically adjust internal perceptual priors. Finally, the clinical utility of these interconnected systems is evaluated, demonstrating how subtle visual aberrations like disrupted contrast suppression serve as diagnostic signatures for mood disorders, while structural retinal decay offers an accessible window into neurodegenerative pathology. Ultimately, the evidence indicates that conscious vision is fundamentally an affective construction, carrying transformative implications for early biomathematical and ocular screening in psychopathology. Full article

Automatic modulation classification (AMC) is a critical enabler of cognitive radio (CR) systems, allowing secondary users to identify primary user modulation schemes and adapt transmission parameters in real time. Traditional AMC approaches, based on likelihood functions or hand-crafted features, suffer from degraded performance under low signal-to-noise ratio (SNR) conditions and realistic channel impairments. In this paper, we propose MS-SENet (Multi-Scale Squeeze–Excitation Network), a novel deep-learning architecture that integrates multi-scale convolutional feature extraction, squeeze-and-excitation channel attention, residual learning, bidirectional long short-term memory (BiLSTM) temporal modelling, and global attention pooling into a unified framework for robust AMC. The multi-scale convolution module employs parallel branches with kernel sizes of 3, 5, and 7 to capture both fine-grained phase transitions and coarse envelope patterns from raw in-phase/quadrature (I/Q) signal samples. Squeeze–excitation residual blocks perform channel-wise feature recalibration, enabling the network to emphasize informative feature maps while suppressing less relevant ones. A bidirectional LSTM layer models temporal dependencies across the signal sequence, and a global attention pooling mechanism performs weighted temporal aggregation prior to classification. We present a comprehensive taxonomy of deep-learning architectures for AMC organised along five axes—input representation, feature extraction, temporal modelling, regularization strategy, and architectural complexity—and conduct a rigorous comparative evaluation against ten baseline architectures on a RadioML-style synthetic dataset (110,000 samples, 11 modulation classes, and 20 SNR levels from −20 to +18 dB). The experimental results demonstrate that MS-SENet achieves a mean classification accuracy of 87.9% at SNR ≥ 0 dB (the average of the medium and high SNR regime averages: 86.06% for 0 ≤ SNR < 10 dB and 89.68% for SNR ≥ 10 dB) while maintaining a compact footprint of approximately 406 K parameters, making it suitable for deployment on resource-constrained edge devices. We further analyze the robustness of the proposed architecture to multipath fading, carrier frequency offset, and sample rate offset, confirming its resilience under practical operating conditions. MS-SENet is an architecture designed for automatic modulation classification of I/Q signals and is not related to the homonymous architecture for speech emotion recognition. Full article

Fragrant olive (Osmanthus fragrans var. aurantiacus (O. fragrans)) extract is known to influence neurophysiological responses through inhalation, yet research on concentration-dependent effects and sex-specific variations remains insufficient. This study utilized an electronic nose (E-nose), electroencephalography (EEG), and standardized low-resolution electromagnetic tomography (sLORETA) to characterize the volatile profiles and neurophysiological impacts of O. fragrans at 3% and 5% concentrations. E-nose analysis identified 48 volatile compounds, with chemometric modeling (PCA, HCA) showing clear discrimination between concentrations. EEG results demonstrated that inhalation induced significant concentration-dependent changes—specifically increasing sedation-related alpha waves and decreasing tension-related gamma waves—with 5% O. fragrans eliciting more widespread cortical responses than the 3% concentration. Notably, no significant sex-related differences were observed in general EEG patterns; however, sLORETA revealed that 5% inhalation specifically suppressed high beta and gamma activities in male participants within Brodmann areas 13, 21, 22, and 44, regions associated with emotional and multisensory processing. In conclusion, this study successfully quantified the relationship between volatile profiles and human brain responses using an integrated biomimetic and neurophysiological approach. These findings provide objective evidence that O. fragrans inhalation, particularly at 5%, modulates neural oscillations toward a relaxed state, offering valuable data for olfactory perception and potential applications as functional volatile compounds. Full article

While the influence of the urban built environment on public emotions has garnered extensive attention, existing studies predominantly focus on temperate climates or warmer seasons. As a result, they rarely extend their scope to winter-specific emotions in cold-region cities, thereby overlooking the complex human–environment emotional interactions under extreme climates. To bridge this seasonal research gap, this study develops an innovative analytical framework integrating Large Language Models (LLMs) with Multiscale Geographically Weighted Regression (MGWR). Drawing on social media data, this framework leverages the powerful zero-shot reasoning capabilities of LLMs to precisely quantify the two-dimensional emotional characteristics of Valence and Arousal. Concurrently, by incorporating the multi-scale spatial modeling strengths of MGWR, it thoroughly investigates the spatial patterns and driving mechanisms of public emotions within the winter context of typical cold-region cities. The results indicate that, first, extreme climates do not lead to urban emotional suppression; instead, frozen rivers transform into vibrant emotional corridors, with the public demonstrating a high degree of thermal-psychological adaptability. Second, by incorporating winter-specific environmental variables, the research reveals a cold-region paradox of emotional valence. Specifically, under snow cover, lower winter Land Surface Temperature (LST) and winter Normalized Difference Vegetation Index (NDVI) paradoxically evoke positive emotions by reconstructing the aesthetic experience of ice-snow landscapes. Furthermore, the impact of urban service facilities on emotional arousal exhibits a significant pattern of diminishing marginal utility. Overall, the LLMs-MGWR framework achieves a closed loop of high-throughput, multi-dimensional semantic decoding and multi-scale spatial interpretation, demonstrating exceptional cross-regional generalizability. Ultimately, this study not only provides a novel paradigm for understanding human–environment interactions in complex environments but also offers transferable planning guidelines for microclimate design, facility decentralization, and the reshaping of winter blue-green infrastructure in global cold-region cities. Full article

Background/Objectives: Emotion recognition is a fundamental component of mammalian social cognition, enabling animals such as rodents to detect the emotional states of conspecifics and guide prosocial or avoidance behaviors. However, natural social landscapes are highly complex and multi-targeted. Furthermore, it remains unknown how an observer’s own internal emotional state dictates social target prioritization in these dynamic environments. Methods: We used a novel multi-target social paradigm wherein observer mice subjected to naïve, positive (relieved), or negative (acute stress) experiences, interacting simultaneously with demonstrator mice in neutral, relieved, or stressed states. We utilized in vivo fiber photometry to record anterior cingulate cortex (ACC) calcium dynamics during these complex interactions, and employed bidirectional optogenetics to establish causal neural mechanisms. Results: Naïve and relieved observer mice exhibited a rapid, innate behavioral prioritization of acutely stressed conspecifics. Conversely, an internal state of acute stress experience in the observer completely abolished this early-stage discrimination. Fiber photometry revealed that ACC excitatory neuronal activity robustly encodes the prioritization of stressed conspecifics, a neural signature that is fundamentally suppressed in stressed observers. Optogenetic inhibition of the ACC abolished innate social preference in naïve and relieved mice, whereas targeted ACC activation successfully overrode internal stress to restore social discrimination in stressed observers. Conclusions: Acute negative internal states profoundly suppress social discrimination ability. The ACC acts as a state-dependent gatekeeper to dictate social prioritization. Full article

Self-silencing, the suppression of self-expression to preserve relationships, has been theorised as context-dependent, yet research has examined this construct primarily at the between-group level, leaving within-person variation underexplored. This qualitative study examined how self-silencing varies across relationship domains among 53 young adults (30 women, 23 men) at international universities in Thailand, using semi-structured interviews (n = 10) and written reflections (n = 43) analysed through reflexive thematic analysis. Findings showed that self-silencing was relationship-specific: participants distinguished relationships in which they expressed themselves from those in which they did not, with perceived security as the most consistent distinction. This pattern operated independently of family socialisation: supportive later relationships could undo learned silence, and fragile ones could introduce silence in participants from expressive families. Thai cultural values of kreng jai and jai yen gave participants a vocabulary for silence but did not dictate where it occurred. The meaning of self-silencing differed by gender, with women more clearly framing silence as care and men as emotional control. Self-silencing carried cumulative psychological costs and took varied behavioural forms. These findings suggest that self-silencing operates as a relationship-specific process rather than a stable personal characteristic. Full article

Parents of children and adolescents with type 1 diabetes (T1D) are responsible for intensive daily disease management and often experience high levels of emotional distress. This study examined whether fear of hypoglycemia mediates the association between parents’ emotion regulation strategies and diabetes-related distress. Participants were recruited through Facebook and WhatsApp groups for parents of children and adolescents with T1D, and data was collected via self-report online questionnaires. A total of 102 parents, 92.2% mothers (aged 32–58 years) of children with T1D aged 8–17 years, completed measures of fear of hypoglycemia (Hypoglycemia Fear Survey—Parent Version), diabetes distress (Problem Areas in Diabetes-Parent Revised) and emotion regulation strategies (Emotion Regulation Questionnaire), along with a sociodemographic questionnaire. Four mediation models were tested using PROCESS, including cognitive reappraisal and expressive suppression as predictors and the worry and behavior subscales of fear of hypoglycemia as mediators. Results revealed a significant indirect effect of worry on the relationship between cognitive reappraisal and diabetes distress (indirect effect = −0.15, 95% CI [−0.35, −0.02]), highlighting worry as a potential mediator between these variables, while the direct effect was negative but non-significant. No significant indirect effects were found for expressive suppression on the behavior subscale (indirect effect = 0.12; 95% IC [−0.07; 0.36]) or on the worry subscale (indirect effect = 0.07; 95% IC [−0.08; 0.24]). These findings suggest that cognitive reappraisal may be associated with lower diabetes-related distress through lower levels of excessive worry about hypoglycemia. Clinically, the results highlight fear-related cognition can be a relevant intervention target, alongside emotion regulation skills, in psychosocial support programs for parents of youth with T1D. Full article

Background: Robust perception involves processing heterogeneous sensory signals, such as facial expressions, vocal prosody, and language, particularly in noisy environments. In computational modeling, a key challenge is integrating these diverse inputs while actively filtering uninformative variations. While recent deep learning models address this integration through complex fusion architectures, they typically aggregate features without explicit filtering modules analogous to inhibitory control. In this study, we propose Multi-modal Information Disentanglement (MInD), a computational framework designed to test the hypothesis that algorithmic noise isolation facilitates robust multisensory integration. Methods: Drawing conceptual inspiration from cognitive theories of modularity, our model decomposes sensory inputs into amodal (modality-invariant) and modal-specific pathways. Furthermore, we introduce an adversarial noise isolation mechanism to serve as an algorithmic analog to cognitive inhibition. Given that our model operates on pre-extracted high-level features, this mechanism functions to isolate latent distributional variance—uninformative fluctuations that persist after initial feature extraction—guiding the network to separate task-relevant affective cues from irrelevant feature variance. Results: Empirical evaluations on standard emotion recognition benchmarks indicate that this purification-before-fusion strategy is associated with competitive performance and stability across multiple metrics. Notably, the framework attains these results using simple linear integration layers, suggesting that separating representations prior to fusion may reduce the computational complexity required for subsequent integration. Conclusions: These observations highlight the computational utility of algorithmic noise suppression, illustrating how cognitive inspiration can inform efficient machine learning architectures without claiming direct neurobiological validation. Full article

Athletes face a variety of pressures related to their sport participation, and these demands can contribute to persistent mental health challenges. The aim of this study was to examine the role of grit, growth mindset, emotion regulation, self-compassion, and psychological well-being in collegiate athletes’ sport satisfaction. Participants (N = 263) were recruited through CloudResearch and outreach emails to athletic programs. The sample included individuals from 43 U.S. states and represented rural, suburban, and urban communities. Among the 30 sports represented, basketball, football, and soccer had the highest participation. Results indicated significant differences in sport satisfaction (p = 0.007, η² = 0.04) and growth mindset (p = 0.017, η² = 0.03) across communities of upbringing, as well as differences in sport satisfaction across years in college (p = 0.008, η² = 0.06). Scholarship status was associated with significant differences in sport satisfaction (p < 0.001, d = 0.85) and expressive suppression (p = 0.019, d = 0.31). Cognitive reappraisal (r = 0.427) demonstrated the strongest association with psychological well-being, whereas growth mindset (r = 0.501) showed the strongest association with sport satisfaction. Additionally, a significant interaction effect emerged between growth mindset and psychological well-being (p = 0.033, ΔR² = 0.01) in predicting sport satisfaction. These findings highlight the importance of supporting student-athletes in effectively regulating their emotions and maintaining a belief in their capacity for growth, as both factors appear critical for promoting psychological well-being and enhancing satisfaction with the athletic experience. Full article