Search Results (95)

Schizophrenia (SZ) is a complex neuropsychiatric disorder characterized by heterogeneous symptoms, relatively poor clinical outcome, and widespread disruptions in neural connectivity and oscillatory dynamics. This article attempts to review current evidence linking genomic and proteomic alterations with aberrant neural oscillations observed in SZ, including aberrations in all oscillatory frequency bands obtained via human EEG. The numerous genes discussed are mainly involved in modulating synaptic transmission, synaptic function, interneuron excitability, and excitation/inhibition balance, thereby influencing the generation and synchronization of neural oscillations at specific frequency bands (e.g., gamma frequency band) critical for different cognitive, emotional, and perceptual processes in humans. The review highlights how polygenic influences and gene–circuit interactions underlie the neural oscillatory and connectivity abnormalities central to SZ pathophysiology, providing a framework for future research on common genetic-neural function interactions and on potential therapeutic interventions targeting local and global network-level neural dysfunction in SZ patients. As will be discussed, many of these genes affecting neural oscillations in SZ also affect other neurological disorders, ranging from autism to epilepsy. In time, it is hoped that future research will show why the same genetic anomaly leads to one illness in one person and to another illness in a different person. Full article

The increasingly severe ecological and environmental problems in rural areas pose a serious threat to agricultural sustainability and human well-being. Protecting the ecological environment of cultivated land is fundamental to ensuring food security and achieving sustainable development goals. The effective integration of digital technology into farmers’ production and daily life is a key driver for transforming farming practices and advancing the ecological protection of cultivated land. This study draws on data from the 2020 China Rural Revitalization Survey (CRRS) to systematically examine the impact of farmers’ digital participation on the ecological protection of cultivated land. The main findings are as follows: (1) Digital participation significantly promotes ecological conservation of cultivated land, with each unit increase associated with a 7.8% reduction in fertilizer use intensity; (2) the results are robust across various empirical strategies, including instrumental variable estimation, the ERM approach, residual analysis, and alternative indicator specifications; (3) mechanism analysis indicates that digital participation reduces fertilizer use through three main channels: expansion of social networks (accounting for 7.10%), enhancement of subjective cognition (29.66%), and adoption of agricultural technologies (10.18%); and (4) heterogeneity analysis shows that the protective effects on cultivated land are more pronounced among households with off-farm employment experience, in villages where leaders have higher educational attainment, and in regions with more advanced digital environments. Based on these findings, the following policy recommendations are proposed: enhancing digital infrastructure in rural areas, strengthening the training of agricultural practitioners, and developing localized digital environments tailored to local conditions. Full article

Intracranial tumors such as gliomas, meningiomas, and brain metastases induce complex alterations in brain function beyond their focal presence. Modern connectomic and neuroimaging approaches, including resting-state functional MRI (rs-fMRI) and diffusion MRI, have revealed that these tumors disrupt and reorganize large-scale brain networks in heterogeneous ways. In adult patients, diffuse gliomas infiltrate neural circuits, causing both local disconnections and widespread functional changes that often extend into structurally intact regions. Meningiomas and metastases, though typically well-circumscribed, can perturb networks via mass effect, edema, and diaschisis, sometimes provoking global “dysconnectivity” related to cognitive deficits. Therefore, this review synthesizes interdisciplinary evidence from neuroscience, oncology, and neuroimaging on how intracranial tumors disrupt functional brain connectivity pre- and post-surgery. We discuss how functional heterogeneity (i.e., differences in network involvement due to tumor type, location, and histo-molecular profile) manifests in connectomic analyses, from altered default mode and salience network activity to changes in structural–functional coupling. The clinical relevance of these network effects is examined, highlighting implications for pre-surgical planning, prognostication of neurocognitive outcomes, and post-operative recovery. Gliomas demonstrate remarkable functional plasticity, with network remodeling that may correlate with tumor genotype (e.g., IDH mutation), while meningioma-related edema and metastasis location modulate the extent of network disturbance. Finally, we explore future directions, including imaging-guided therapies and “network-aware” neurosurgical strategies that aim to preserve and restore brain connectivity. Understanding functional heterogeneity in brain tumors through a connectomic lens not only provides insights into the neuroscience of cancer but also informs more effective, personalized approaches to neuro-oncologic care. Full article

Background: Olfactory dysfunction (OD)—including anosmia and hyposmia—is a common and often persistent outcome of viral infections. This systematic review consolidates findings from structural and functional MRI studies to explore how COVID-19 SARS-CoV-2-induced smell loss alters the brain. Considerable heterogeneity was observed across studies, influenced by differences in methodology, population characteristics, imaging timelines, and OD classification. Methods: Following PRISMA guidelines, we conducted a systematic search of PubMed/MEDLINE, Scopus, and Web of Science to identify MRI-based studies examining COVID-19’s SARS-CoV-2 OD. Twenty-four studies were included and categorized based on imaging focus: (1) olfactory bulb (OB), (2) olfactory sulcus (OS), (3) grey and white matter changes, (4) task-based brain activation, and (5) resting-state functional connectivity. Demographic and imaging data were extracted and analyzed accordingly. Results: Structural imaging revealed consistent reductions in olfactory bulb volume (OBV) and olfactory sulcus depth (OSD), especially among individuals with OD persisting beyond three months, suggestive of inflammation and neurodegeneration in olfactory-associated regions like the orbitofrontal cortex and thalamus. Functional MRI studies showed increased connectivity in early-stage OD within regions such as the piriform and orbitofrontal cortices, possibly reflecting compensatory activity. In contrast, prolonged OD was associated with reduced activation and diminished connectivity, indicating a decline in olfactory processing capacity. Disruptions in the default mode network (DMN) and limbic areas further point to secondary cognitive and emotional effects. Diffusion tensor imaging (DTI) findings—such as decreased fractional anisotropy (FA) and increased mean diffusivity (MD)—highlight white matter microstructural compromise in individuals with long-term OD. Conclusions: COVID-19’s SARS-CoV-2 olfactory dysfunction is associated with a range of cerebral alterations that evolve with the duration and severity of smell loss. Persistent dysfunction correlates with greater neural damage, underscoring the need for longitudinal neuroimaging studies to better understand recovery dynamics and guide therapeutic strategies. Full article

Introduction: Combat sport athletes are exposed to repetitive head impacts yet also develop distinct performance-related brain adaptations. Electroencephalography (EEG) provides millisecond-level insight into both processes; however, findings are dispersed across decades of heterogeneous studies. This mechanistic review consolidates and interprets EEG evidence to elucidate how participation in combat sports shapes brain function and to identify research gaps that impede clinical translation. Methods: A structured search was conducted in March 2025 across PubMed/MEDLINE, Scopus, Cochrane Library, ResearchGate, Google Scholar, and related databases for English-language clinical studies published between January 1980 and March 2025. Eligible studies recorded raw resting or task-related EEG in athletes engaged in boxing, wrestling, judo, karate, taekwondo, kickboxing, or mixed martial arts. Titles, abstracts, and full texts were independently screened by two reviewers. Twenty-three studies, encompassing approximately 650 combat sport athletes and 430 controls, met the inclusion criteria and were included in the qualitative synthesis. Results: Early visual EEG and perfusion studies linked prolonged competitive exposure in professional boxers to focal hypoperfusion and low-frequency slowing. More recent quantitative studies refined these findings: across boxing, wrestling, and kickboxing cohorts, chronic participation was associated with reduced alpha and theta power, excess slow-wave activity, and disrupted small-world network topology—alterations that often preceded cognitive or structural impairments. In contrast, elite athletes in karate, fencing, and kickboxing consistently demonstrated neural efficiency patterns, including elevated resting alpha power, reduced task-related event-related desynchronization (ERD), and streamlined cortico-muscular coupling during cognitive and motor tasks. Acute bouts elicited transient increases in frontal–occipital delta and high beta power proportional to head impact count and cortisol elevation, while brief judo chokes triggered short-lived slow-wave bursts without lasting dysfunction. Methodological heterogeneity—including variations in channel count (1 to 64), reference schemes, and frequency band definitions—limited cross-study comparability. Conclusions: EEG effectively captures both the adverse effects of repetitive head trauma and the cortical adaptations associated with high-level combat sport training, underscoring its potential as a rapid, portable tool for brain monitoring. Standardizing acquisition protocols, integrating EEG into longitudinal multimodal studies, and establishing sex- and age-specific normative data are essential for translating these insights into practical applications in concussion management, performance monitoring, and regulatory policy. Full article

Endocannabinoids, acting primarily through CB1 receptors, are critical modulators of neuronal activity, influencing cognitive functions and emotional processing. CB1 receptors are highly expressed in the hippocampus, primarily on GABAergic interneurons, modulating the excitation/inhibition balance. Previous evidence suggests the functional heterogeneity of CB1 receptors along the dorsoventral axis of the hippocampus. However, it is not known whether CB1 receptors differentially modulate basic aspects of the local neuronal network along the hippocampus. This study investigated how CB1 receptor activation modulates excitability, paired-pulse inhibition (PPI), and short-term neuronal dynamics (STND) in the dorsal and ventral CA1 hippocampus under physiologically relevant conditions. Using extracellular recordings from hippocampal slices of male Wistar rats, we compared the effects of two CB1 receptor agonists, ACEA and WIN55,212-2, on network activity in the dorsal and ventral hippocampus. We found that both agonists significantly increased excitability and reduced PPI in the dorsal, but not the ventral, hippocampus. Similarly, CB1 receptor activation modulated STND more prominently in the dorsal hippocampus, reducing facilitation at low frequencies and reversing depression at high frequencies, whereas effects on the ventral region were minimal. These dorsoventral differences in the actions of cannabinoid receptor agonists occurred despite similar CB1 receptor expression levels in both regions, suggesting that functional differences arise from downstream mechanisms rather than receptor density. Pre-application of the GIRK channel blocker Tertiapin-Q occluded the effects of WIN55,212-2 on STND, indicating a significant role of GIRK channel-mediated signaling in CB1 receptor actions. These findings demonstrate that CB1 receptors modulate hippocampal circuitry in a region-specific manner, with the dorsal hippocampus being more sensitive to cannabinoid signaling, likely through differential engagement of intracellular signaling pathways such as GIRK channel activation. These results provide novel insights into how endocannabinoid signaling differentially regulates neuronal dynamics along the dorsoventral axis of the hippocampus. They also have important implications for understanding the role of cannabinoids in hippocampus-dependent behaviors. Full article

Introduction: Basketball performance requires not only intermittent high-intensity movements—such as sprinting, jumping, and rapid directional changes—but also rapid decision-making under cognitive and psychological stress. Transcranial direct current stimulation (tDCS) has emerged as a potential modality to enhance both physical and mental performance due to its capacity to modulate cortical excitability and promote synaptic plasticity. Although the broader literature suggests that tDCS can benefit motor performance and endurance across various sports, its specific impact on basketball remains underexplored. Methods: This scoping review aimed to summarize current evidence on the effects of tDCS in basketball. A comprehensive literature search was conducted across databases including PubMed/Medline, Google Scholar, and Cochrane, identifying studies published between January 2008 and February 2025. Only clinical trials investigating tDCS interventions in basketball players were included. Eleven articles met the inclusion criteria and were synthesized narratively, with a focus on stimulation parameters (site, duration, intensity) and performance outcomes (shooting accuracy, dribbling, sprinting, decision-making, fatigue). Results: The reviewed studies indicated that tDCS—particularly when applied over the motor cortex—was associated with moderate improvements in shooting accuracy, dribbling time, repeated-sprint performance, and decision-making under fatigue. Some studies reported delayed rather than immediate benefits, suggesting that tDCS may prime neural networks for enhanced learning and retention. However, not all findings were consistent; certain interventions produced minimal or no significant effects, especially regarding subjective mental fatigue and cognitive workload. The variability in electrode placements and stimulation protocols highlights the need for methodological standardization. Conclusions: Current evidence partially supports the potential of tDCS to improve specific performance domains in basketball, particularly in skill acquisition, neuromuscular efficiency, and decision-making. Nevertheless, the findings are limited by small sample sizes, heterogeneous protocols, and a lack of long-term follow-up. Future research should prioritize larger, multisite studies with standardized tDCS parameters and ecologically valid outcome measures to confirm the efficacy and practical relevance of tDCS in competitive basketball settings. Full article

Background/Objectives: The severity of autism spectrum disorder (ASD) is clinically assessed through a comprehensive evaluation of social communication deficits, restricted interests, repetitive behaviors, and the level of support required (ranging from level 1 to level 3) according to DSM-5 criteria. Along with its varied clinical manifestations, the neuroanatomy of ASD is characterized by heterogeneous abnormalities. Notably, brain MRI of children with ASD often reveals an increased number of perivascular spaces (PVSs) compared to typically developing children. Our recent findings indicate that enlarged PVSs (ePVSs) are more common in younger male patients with severe ASD and that specific ePVS locations are significantly associated with ASD symptoms. Methods: In this study, we mapped ePVSs across key regions of three major cognitive networks—the Default Mode Network (DMN), the combined Central Executive/Frontoparietal Network (CEN/FPN), and the Salience Network (SN)—in 36 individuals with different symptom severities and rehabilitation needs due to ASD. We explored how the number, size, and location of PVSs in these networks are related to specific ASD symptoms and the overall need for rehabilitation and support. Results: Our results suggest that ePVSs in the DMN, CEN/FPN, and SN are strongly correlated with the severity of certain ASD symptoms, including verbal deficits, stereotypies, and sensory disturbances. We found a mild association between ePVSs and the level of support needed for daily living and quality of life. Conclusions: Dysfunction in cognitive networks associated with the presence of ePVSs has a significant impact on the severity of ASD symptoms. However, the need for assistance may also be influenced by other comorbid conditions and dysfunctions in smaller, overlapping brain networks. Full article

Parkinson’s disease involves widespread neurodegeneration that extends far beyond the basal ganglia, giving rise to a diverse range of non-motor symptoms that frequently emerge before motor onset. These include autonomic dysfunction, cognitive decline, neuropsychiatric disturbances, sleep-related disorders, and sensory deficits. Here, we synthesize current evidence on the anatomical, neurochemical, and network-level mechanisms that drive these symptoms, and we examine how they shape disease progression and clinical heterogeneity. We highlight the limitations of dopamine-centric models and advocate for a framework that treats non-motor symptoms as the disorder’s primary, mechanistically distinct features. We also discuss how emerging technologies—such as multi-omic profiling, artificial intelligence, and network neuroscience—enable earlier identification, stratification of non-motor phenotypes, and the development of precision-based therapeutic strategies. Recognizing non-motor symptoms as central to Parkinson’s disease redefines how the disorder should be diagnosed, studied, and treated. Full article

This paper presents an optimization model for wireless channel allocation in cellular networks, specifically designed for the transmission of smart meter (SM) data through a mobile virtual network operator (MVNO). The model efficiently allocates transmission channels, minimizing smart grid (SG) costs. The MVNO manages fixed and random channels through a shared access scheme, optimizing meter connectivity. Channel allocation is based on a Markovian approach and optimized through the Hungarian algorithm that minimizes the weight in a bipartite network between meters and channels. In addition, cumulative tokens are introduced that weight transmissions according to channel availability and network congestion. Simulations show that dynamic allocation in virtual networks improves transmission performance, contributing to sustainability and cost reduction in cellular networks. This study highlights the importance of inefficient resource management by cognitive mobile virtual network and cognitive radio virtual network operators (C-MVNOs), laying a solid foundation for future applications in intelligent networks. This work is motivated by the increasing demand for efficient and scalable data transmission in smart metering systems. The novelty lies in integrating cumulative tokens and a Markovian-based bipartite graph matching algorithm, which jointly optimize channel allocation and transmission reliability under heterogeneous wireless conditions. Full article

Background: The Glasgow Coma Scale (GCS) is a cornerstone in neurological assessment, providing critical insights into consciousness levels in patients with traumatic brain injuries and other neurological conditions. Despite its clinical importance, traditional methods for predicting GCS scores often fail to capture the complex, multi-dimensional nature of patient data. This study aims to address this gap by leveraging machine learning (ML) techniques to develop accurate, interpretable models for GCS prediction, enhancing decision making in critical care. Methods: A comprehensive dataset of 759 patients, encompassing 25 features spanning pre-, intra-, and post-operative stages, was used to develop predictive models. The dataset included key variables such as cognitive impairments, Hunt and Hess scores, and aneurysm dimensions. Six ML algorithms, including random forest (RF), XGBoost, and artificial neural networks (ANN), were trained and rigorously evaluated. Data preprocessing involved numerical encoding, standardization, and stratified splitting into training and validation subsets. Model performance was assessed using accuracy and receiver operating characteristic area under the curve (ROC AUC) metrics. Results: The RF model achieved the highest accuracy (86.4%) and mean ROC AUC (0.9592 ± 0.0386, standard deviation), highlighting its robustness and reliability in handling heterogeneous clinical datasets. XGBoost and SVM models also demonstrated strong performance (ROC AUC = 0.9502 and 0.9462, respectively). Key predictors identified included the Hunt and Hess score, aneurysm dimensions, and post-operative factors such as prolonged intubation. Ensemble methods outperformed simpler models, such as K-nearest neighbors (KNN), which struggled with high-dimensional data. Conclusions: This study demonstrates the transformative potential of ML in GCS prediction, offering accurate and interpretable tools that go beyond traditional methods. By integrating advanced algorithms with clinically relevant features, this work provides a dynamic, data-driven framework for critical care decision making. The findings lay the groundwork for future advancements, including multi-modal data integration and broader validation, positioning ML as a vital tool in personalized neurological care. Full article

This study examines the role of board experience diversity in fostering corporate green technological innovation (CGTI), focusing on the moderating effects of absorptive capacity and director network location. Integrating upper echelons theory with absorptive capacity theory, we explore how board experience diversity enhances strategic decision-making and innovation. We hypothesize that board experience diversity improves CGTI by broadening cognitive perspectives. We also examine the moderating effect of absorptive capacity on the relationship between board experience diversity and innovation. We examine Chinese A-share listing firms, finding that board experience diversity positively affects CGTI, and absorptive capacity strengthens this effect. Additionally, we show that director network location, proxied by centrality in inter-board networks, not only strengthens the association between board experience diversity and innovation but also affects innovation. Furthermore, we conducted heterogeneity and mechanism tests, confirming the robustness of these relationships. These findings contribute to the literature on corporate governance and sustainability by emphasizing the roles of board experience diversity, absorptive capacity, and network position in driving CGTI. Full article

Depression remains one of the most widespread and costly mental disorders, with the current first-line treatment efficacy of about a third, possibly due to its heterogeneous nature. Consequently, there is a need to identify reliable biomarkers for specific subtypes of depression, particularly neurological signatures that may help with targeted treatments. This study aimed to explore the connectivity between two important networks in the brain: the dorsal and ventral attention networks and the salience network, to determine their potential as biomarkers of depression subtypes. From resting electroencephalogram (EEG) data collected on 54 males and 46 females aged between 18 and 75 yr (M = 33 yr), functional network connectivity data were examined for their relationships with four depression subtypes. Beta and gamma wave connectivity was significantly associated with Anhedonia and Cognitive depression subtypes across and within all three networks while no significant results were found for alpha wave activity connectivity, and only one result was found for either the Mood or Somatic depression subtypes. In conclusion, these results provide further support for the concept of depression as heterogeneous rather than homogeneous and identify the novel neurophysiological signatures of two depression subtypes. Full article

Background/Objectives: Multiple sclerosis (MS) is the most prevalent incurable nontraumatic neurological disability in young individuals. It causes numerous symptoms, including tingling, fatigue, muscle spasms, cognitive deficits, and neuropsychiatric disorders. This disease significantly worsens quality of life (QoL), and this dimension of general functioning provides valuable information about the effectiveness of treatment and well-being. There are psychological interventions that can improve QoL, but their number is limited. Therefore, searching for new methods that are as effective and safe as possible is ongoing. Methods: This review examines the potential effectiveness of transcranial direct current stimulation (tDCS) in improving the quality of life in patients with MS. Searches were conducted in the PubMed/Medline, Research Gate, and Cochrane databases. Results: The search yielded seven studies in which QoL was a primary or secondary outcome. Stimulation protocols displayed heterogeneity, especially concerning the choice of the stimulation site. Four studies demonstrated the effectiveness of tDCS in improving QoL, all of which (two) used anodal stimulation of the left DLPFC. Stimulation of the motor cortex has produced mixed results. The potential mechanisms of action of tDCS in improving QoL in MS are explained. These include improved synaptic plasticity, increased cerebral blood flow, salience network engagement through tDCS, and reduction of beta-amyloid deposition. The limitations are also detailed, and recommendations for future research are made. Conclusions: While the evidence is limited, tDCS has shown potential to improve QoL in MS patients in some studies. Prefrontal stimulation appears promising, and further research is recommended to explore this approach. Full article

This study explores the potential of heterogeneous hybrid Free Space Optical (FSO) and Radio Frequency (RF) cognitive networks, which feature both cooperative and economic systems. The cooperative system is defined as a heterogeneous network where the hybrid FSO/RF node possesses dedicated RF resources and shares these resources to create additional transmission opportunities. In contrast, the low-cost economic system consists of a heterogeneous network where only an RF node has RF resources, and the hybrid node shares these resources. We provide a comprehensive analysis for each system, employing stay-and-switch (SAS) and simultaneous multipacket transmission (SMT) methods to ensure a thorough understanding of its performance. As a performance measure, we investigate the stability region of the proposed cognitive and economic systems and devise a reference system without cognitive capability for comparison. Numerical evaluations indicate that the cooperative system using SMT typically outperforms the reference system, increasing stability throughput by up to 52%. However, this advantage diminishes when SAS is used or in rainy conditions. The economic model shows performance levels comparable to the reference model, particularly when incoming traffic is low and when SAS is implemented in clear or hazy environments. Full article