BackgroundPattern visual evoked potentials (pattern VEP) are widely used for functional assessment of the visual pathways. The P100 component represents the principal clinical parameter owing to its relative interindividual stability and diagnostic value. However, both latency and amplitude are modulated by multiple physiological and environmental factors, which complicates interpretation and the establishment of reliable reference standards. This scoping review aimed to systematically map determinants of P100 parameters in healthy individuals.Main textThe review was conducted in accordance with PRISMA-ScR and Joanna Briggs Institute methodology. Databases were searched for studies published between 2015 and 2025 that examined biological, refractive, anthropometric, metabolic, or environmental influences on pattern VEP parameters in healthy populations. Owing to methodological heterogeneity, findings were synthesized descriptively. Thirty-nine studies met the inclusion criteria. Age emerged as the most consistent determinant of P100 parameters. Latency followed a non-linear trajectory across the lifespan, with shortening during maturation, stabilization in early adulthood, and progressive prolongation after approximately 40 years of age, whereas amplitude generally declined with aging. Sex differences predominantly affected amplitude, with women typically demonstrating higher P100 or N75–P100 amplitudes in adult populations; latency differences were less consistent and often minimal in paediatric cohorts. Retinal image quality exerted a strong dose-dependent effect on P100 parameters: increasing refractive blur and higher-order aberrations were associated with progressive latency prolongation and amplitude reduction, particularly for small check sizes. Ocular dominance showed no clinically meaningful interocular asymmetry. Metabolic disturbances were associated with prolonged latency in selected populations, whereas anthropometric variables such as head size and height demonstrated weak or inconsistent associations. Among environmental factors, acute alcohol intake prolonged P100 latency, while moderate caffeine consumption had no significant effect.ConclusionAge and retinal image quality represent the primary physiological determinants of P100 latency and amplitude in healthy individuals. Most other modifiers exert modest or context-dependent effects. Consideration of these variables is essential for accurate interpretation of pattern VEP recordings and for establishing reliable local reference standards consistent with ISCEV recommendations.
Progenitor diversity during formation of the mammalian neocortex
Mammalian neocortical development follows a precise spatiotemporal sequence to generate the organized structure responsible for higher-order cognition and behavior. Increasing evidence suggests that diversification of neural stem and progenitor cells during prenatal development is a key step in the emergence of the intricate circuitry and functional architecture of the cerebral cortex. This review discusses novel findings with an emphasis on mechanisms and consequences of cell lineage variation during normal and altered brain development, including focus on neurodevelopmental disorders such as autism spectrum disorders and Down syndrome.
Autonomic imbalance and vascular injury in hypertensive chronic kidney disease: mechanisms and clinical potential of ultrasound-guided sympathetic blockade
Hypertensive Chronic Kidney Disease (CKD) constitutes a significant global health burden, characterized by a vicious cycle of hypertension and progressive renal decline. Autonomic imbalance, specifically sympathetic overactivity and parasympathetic withdrawal, is increasingly recognized as a central driver of this pathophysiology, interacting with traditional hemodynamic factors such as RAAS activation and volume overload. This review aims to elucidate the deep-seated mechanisms by which autonomic imbalance induces vascular injury and renal progression, and to evaluate the clinical potential of ultrasound-guided sympathetic blockade as a novel therapeutic strategy. We conducted a comprehensive narrative review of recent basic research and clinical evidence regarding the “Kidney-Brain-Vascular Axis” and neuromodulation therapies in the context of hypertensive CKD. The pathogenesis involves a maladaptive “Neuro-Immune-Vascular Axis.” Sympathetic overactivity not only induces hemodynamic stress but also disrupts the Th1/Th2 immune balance, accelerating vascular calcification and fibrosis. Ultrasound-guided sympathetic blockade offers a reversible, minimally invasive neuromodulation approach. Preliminary clinical evidence suggests it may lower blood pressure, improves vascular endothelial function, and potentially delays renal progression, particularly in patients with resistant hypertension, with a superior safety profile compared to renal denervation. Ultrasound-guided sympathetic blockade represents an emerging and investigational adjunct, constrained by the lack of large-scale randomized controlled trials and long-term outcome data.
Disease-associated RNA and protein signatures in iPSC-derived microglia model of Alzheimer’s disease
IntroductionMicroglia, the resident immune cells of the central nervous system, play a critical role in maintaining neural homeostasis and regulating inflammatory responses in the brain. Increasing evidence suggests that microglial dysfunction contributes to the progression of neurodegenerative diseases, including Alzheimer’s disease (AD). However, the molecular mechanisms underlying these alterations remain incompletely understood. This study aimed to characterize disease-associated molecular changes in microglia derived from induced pluripotent stem cells (iPSCs) of sporadic AD patients and healthy donors.MethodsiPSC-derived microglia from sporadic AD patients and healthy controls were analyzed using integrated multi-omics approaches, including total RNA sequencing, proteomics, and small non-coding RNA (sncRNA) sequencing. Gene Ontology (GO) analysis was performed to identify dysregulated biological pathways from transcriptomic and proteomic datasets. In addition, a modified T4 polynucleotide kinase (T4 PNK)-based sncRNA sequencing method was used to profile disease-associated sncRNAs and identify previously uncharacterized RNA species.ResultsComparative analyses revealed significant AD-associated alterations in mRNA, protein, and sncRNA expression profiles in iPSC-derived microglia. GO analysis demonstrated dysregulation of pathways related to extracellular communication, intracellular transport, cytoskeletal organization, and protein–protein interactions. Furthermore, the modified T4 PNK–sncRNA sequencing approach identified multiple disease-associated sncRNAs, including several novel and previously uncharacterized RNA species potentially linked to AD pathology.DiscussionThese findings demonstrate that iPSC-derived microglia provide a valuable model for studying molecular mechanisms associated with sporadic AD. The identified transcriptomic, proteomic, and sncRNA alterations highlight key pathways potentially involved in microglial dysfunction and neurodegeneration. In particular, the discovery of novel disease-associated sncRNAs may provide new insights into AD pathogenesis and reveal potential therapeutic targets for future investigation.
GGDA-net: geometry-guided deformable attention network for Alzheimer’s disease image classification
BackgroundConvolutional neural networks (CNNs) have achieved remarkable success in medical image analysis, including Alzheimer’s disease (AD) classification. However, conventional convolution operations rely on fixed sampling patterns, and most existing attention mechanisms primarily focus on feature responses while neglecting spatial sampling geometry, limiting their ability to capture structural variations in brain images.MethodsTo address these limitations, this paper proposes a Geometry-Guided Deformable Attention Network (GGDA-Net) for medical image classification. The proposed framework integrates Linear Deformable Convolution (LDConv) with a Geometry-Aware (GA) Attention mechanism to jointly model feature semantics and spatial geometry. Specifically, LDConv introduces adaptive spatial sampling through learnable offsets, enabling flexible modeling of geometric deformations in brain structures, while the GA attention exploits the resulting geometric cues to guide the network toward more informative anatomical regions.ResultsThe experimental results show that the accuracy rates on the two datasets reached 99.38 and 99.16% respectively, which are superior to the existing most advanced algorithms. At the same time, the model maintains a compact size and has a relatively low computational complexity. These results highlight the effectiveness of feature learning based on geometric perception in medical image analysis and Alzheimer’s disease diagnosis.
Task-state P300 and functional brain network abnormalities in adolescent major depressive disorder: a Stroop paradigm study
BackgroundCognitive control deficits are a core feature of adolescent major depressive disorder (MDD), yet the associated task-state neurophysiological mechanisms remain poorly characterized. This study investigated electrophysiological alterations in MDD using a Stroop color-word task.MethodsTwenty-two adolescents with MDD and fifteen age- and sex-matched healthy controls (HC) completed the task during 32-channel EEG recording. We analyzed P300 amplitude and latency, 1-30Hz power spectral density (PSD) in key cortical regions, and task-based functional connectivity using the phase locking value (PLV). A support vector machine (SVM) classifier with leave-one-out cross-validation was employed to assess the diagnostic utility of the multimodal features.ResultsRelative to HC, the MDD group exhibited significantly prolonged incongruent trial reaction times, reduced P300 amplitude at centro-parietal electrodes (Oz, PO7, O2), and enhanced alpha/beta-band PSD in occipitotemporal regions. Functional connectivity analysis revealed a task-state shift from a frontoparietal to an occipitotemporal network. The multimodal SVM model achieved 86.49% classification accuracy (AUC = 0.86).ConclusionTask-specific P300 hypoactivity, aberrant oscillatory dynamics, and functional network reorganization collectively distinguish adolescent MDD from HC. These findings provide convergent neurophysiological evidence for impaired cognitive control in MDD and highlight the potential of preliminary candidate EEG biomarkers for early identification, prognostic assessment, and monitoring treatment response in adolescent MDD.
Can directing self-enhancement from social to performance settings alleviate narcissistic personality disorder? Implications from narcissism-performance research
Aggression and emotional distress in adolescents: a cross-sectional chain mediation model of internet addiction and somatization
BackgroundAdolescent depression and anxiety are major public health concerns. Aggression is frequently associated with internalizing symptoms, but the behavioral and body related mechanisms underlying this association remain insufficiently clarified. This study examined a theoretically proposed chain mediation model linking aggression with depressive and anxiety symptoms through internet addiction and somatization.MethodsA cross-sectional survey was conducted among 5,307 high school students in Chongqing, China. Participants filled in the Buss and Perry Aggression Questionnaire (BPAQ), Internet Addiction Test (IAT), Patient Health Questionnaire – 15 (PHQ – 15), Patient Health Questionnaire – 9 (PHQ – 9) and Generalized Anxiety Disorder Scale – 7 (GAD – 7). Regression based chain mediation analyses with 5,000 bootstrap samples were performed using PROCESS Model 6, with gender and age controlled as covariates.ResultsThe results showed that aggression was positively correlated with depressive symptoms (β = 0.256, p < 0.001) and anxiety symptoms (β = 0.275, p < 0.001). Chain mediation analysis showed that aggression was indirectly associated with mental health through three distinct pathways: 1. the independent mediating effect of internet addiction; 2. the independent mediating effect of somatization; 3. the sequential chain mediating effect from internet addiction to somatization. The model explained more variance in depressive symptoms (R² = 58.2%) than in anxiety symptoms (R² = 53.5%). Furthermore, the association between somatization and depressive symptoms (β = 0.457) was stronger than that between somatization and anxiety symptoms (β = 0.428).ConclusionThis study supports a statistically significant chain mediation pattern in which aggression is associated with depressive and anxiety symptoms through internet addiction and somatization. The findings suggest that somatization may represent an important body related correlate in the association between maladaptive digital behavior and emotional distress, with a slightly stronger association observed for depressive symptoms than for anxiety symptoms. These findings highlight the importance of integrated school based interventions that address digital behavior regulation, somatic symptom monitoring, and emotional distress among adolescents with higher aggressive tendencies.
Toward clarifying ASAM’s inpatient and residential benzodiazepine tapering recommendations
A bibliometric analysis of neuroimaging studies on cognitive control in autism spectrum disorder (2000–2025)
ObjectiveThis study aims to systematically analyze neuroimaging research on cognitive control in Autism spectrum disorder (ASD) from 2000 to 2025 using bibliometric methods, in order to reveal the evolutionary trajectory, core knowledge base, research hotspots, and future frontiers of the field.MethodsA search was conducted on the Web of Science Core Collection and Scopus databases, resulting in the inclusion of 1,581 relevant articles. VOSviewer and the Bibliometrix package in R were utilized to conduct a comprehensive visualization and quantitative analysis of annual publication volume, country/institution/author collaboration networks, keyword co-occurrence, document co-citation, and thematic evolution.Results(1) The volume of research literature showed exponential growth, with an annual growth rate of 21.61%, entering a period of rapid development particularly after 2012, which is closely related to the popularization of functional magnetic resonance imaging (fMRI) technology. (2) “Functional connectivity,” “executive function,” and “default mode network” were the most central keywords. “Functional connectivity” rapidly became a hub connecting various themes after 2010, marking a paradigm shift from “functional localization” to “brain network dysregulation.” (3) The “Triple network model” proposed by Menon was the most cited document, laying the core theoretical foundation for understanding ASD as a disorder of large-scale brain network dysfunction. (4) “Transdiagnostic” research has emerged as a new hotspot, while “multimodal imaging,” “machine learning,” and “dynamic connectivity” represent highly promising future directions.ConclusionOver the past two decades, neuroimaging research on cognitive control in ASD has undergone a profound paradigm shift: from focusing on abnormal activation in isolated brain regions to exploring the static and dynamic dysregulation of large-scale brain networks. The research perspective has also expanded from a single-disorder model to a transdiagnostic framework that includes comparisons with other neurodevelopmental disorders (e.g., ADHD). Future research should focus on the fusion of multimodal data, the application of computational psychiatry methods, and the translation of basic research findings into personalized clinical interventions.