Search Results (565)

The deployment of Vision-Language Models (VLMs) in Satellite IoT scenarios is critical for real-time disaster assessment but is often hindered by the substantial memory and compute requirements of state-of-the-art models. While parameter-efficient fine-tuning (PEFT) enables adaptation, with minimal computational overhead, standard supervised methods often fail to ensure robustness and reliability on resource-constrained edge devices. To address this, we propose EdgeV-SE, a self-reflective fine-tuning framework that significantly enhances the performance of VLM without introducing any inference-time overhead. Our framework incorporates an uncertainty-aware self-reflection mechanism with asymmetric dual pathways: a generative linguistic pathway and an auxiliary discriminative visual pathway. By estimating uncertainty from the linguistic pathway using a log-likelihood margin between class verbalizers, EdgeV-SE identifies ambiguous samples and refines its decision boundaries via consistency regularization and cross-pathway mutual learning. Experimental results on hurricane damage assessment demonstrate that our approach improves image classification accuracy, enhances image–text semantic alignment, and achieves superior caption quality. Notably, our work achieves these gains while maintaining practical deployment on a commercial off-the-shelf edge device such as NVIDIA Jetson Orin Nano, preserving the inference latency and memory footprint. Overall, our work contributes a unified self-reflective fine-tuning framework that improves robustness, calibration, and deployability of VLMs on edge devices. Full article

Background/Objectives: Children born small for gestational age (SGA) are at increased risk for impaired growth, metabolic disturbances, and neurodevelopmental difficulties. Although previous research has examined cognitive and behavioral outcomes in this population, findings remain inconsistent. Moreover, limited evidence is available regarding the potential effects of recombinant human growth hormone (rhGH) therapy on cognitive development. We aimed to assess cognitive performance, emotional–behavioral functioning, and neonatal predictors of neurocognitive outcomes in term SGA children compared with age- and sex-matched peers born appropriate for gestational age (AGA). We also explored potential differences in cognitive outcomes between rhGH-treated and untreated SGA children. Methods: A total of 18 term SGA children and 23 AGA controls underwent anthropometric measurements, biochemical evaluation, cognitive testing using the Wechsler Intelligence Scale for Children—Fourth Edition (WISC-IV), and behavioral assessment through the Child Behavior Checklist (CBCL). Birth weight, length, and head circumference were analyzed as potential predictors of cognitive performance. Results: SGA children demonstrated significantly lower Intelligence Quotient (IQ) scores than AGA peers, with marked weaknesses in Perceptual Reasoning index (PRI) and Processing Speed index (PSI), while Verbal Comprehension and Working Memory were preserved. They also exhibited higher internalizing behavioral symptoms, whereas externalizing behaviors did not differ between groups. Birth head circumference emerged as a strong predictor of PRI and a modest predictor of PSI. No associations were found between rhGH treatment parameters and cognitive outcomes. Larger longitudinal studies are needed to clarify how early growth restriction affects brain development and cognition and whether GH therapy influences these processes. Full article

Background/Objectives: Mild Cognitive Impairment (MCI) represents a critical transition stage between normal aging and dementia, with executive dysfunction playing a key prognostic role. Traditional neuropsychological tests show limited ecological validity and may fail to detect early executive deficits. Virtual Reality (VR) offers an innovative alternative by reproducing everyday situations in realistic environments. This study investigated whether the Picture Interpretation Test 360° (PIT 360°), a VR-based assessment, can (1) discriminate between MCI patients and healthy controls (HCs); (2) identify executive dysfunction within the MCI group; and (3) correlate with standard neuropsychological measures. Methods: One hundred and one participants aged ≥65 years (53 MCI, 48 HCs) underwent a comprehensive neuropsychological assessment and PIT 360° evaluation. The PIT 360° requires interpreting a complex scene in a 360-degree virtual environment. Hierarchical linear regression, Receiver operating characteristic (ROC) curve analysis, and binary logistic regression were performed to examine group differences and diagnostic accuracy. MCI patients were stratified based on their performance on the Modified Five Point Test to identify visuospatial dysexecutive deficits. Results: MCI patients showed significantly longer PIT 360° completion times than HCs (92.6 vs. 65.3 s, p = 0.006), independent of age. MCI patients with visuospatial dysexecutive deficits exhibited the most severe deficits (median = 105 s, p = 0.017 vs. HCs). ROC analysis revealed adequate discriminative ability (AUC = 0.64, 95% CI [0.53, 0.75]) with a preliminary, sample-derived cut-off at ≥22 s, yielding high sensitivity (86.5%) but low specificity (42.6%). This threshold requires validation in independent samples. PIT 360° completion time correlated significantly with visuospatial executive functions, visual memory, and verbal fluency. Conclusions: The PIT 360° effectively screens for visuospatial executive dysfunction in MCI with high sensitivity, making it suitable for ruling out clinically significant impairment. Its ecological validity, brief administration, and correlations with traditional measures support integration into routine clinical practice for early detection and rehabilitation planning. Full article

Objectives: Innovative, evidence-based interventions for developmental dyslexia (DD) are necessary. While traditional methods remain valuable, newer approaches, such as cognitive–motor training, show the potential to improve literacy skills for those with DD. Verbal Working Memory–Balance (VWM-B) is a novel cognitive–motor training program that has demonstrated positive effects on reading, cognitive functions, and motor skills in children with DD. This extension study explored the neural mechanisms of VWM-B through voxel-to-voxel intrinsic functional connectivity (FC) analysis in children with DD. Methods: Resting-state fMRI data from 16 participants were collected in a quasi-double-blind randomized clinical trial with control and experimental groups, pre- and post-intervention measurements, and 15 training sessions over 5 weeks. Results: The mixed ANOVA interaction was significant for the right and left postcentral gyrus, bilateral precuneus, left superior frontal gyrus, and left posterior division of the supramarginal and angular gyri. Decreased FC in the postcentral gyri indicates reduced motor task engagement due to automation following VWM-B training. Conversely, increased FC in the bilateral precuneus, left superior frontal gyrus, and left posterior divisions of the supramarginal and angular gyri suggests a shift of cognitive resources from motor tasks to the cognitive functions associated with VWM-B. Conclusions: In conclusion, the study highlights that cognitive–motor dual-task training is more effective than single-task cognitive training for improving cognitive and motor functions in children with DD, emphasizing the importance of postural control and automaticity in dyslexia. The trial for this study was registered on 8 February 2018 with the Iranian Registry of Clinical Trials (IRCT20171219037953N1). Full article

Background/Objective: Neuroimmune and metabolic dysregulation have been increasingly implicated in the cognitive heterogeneity of autism spectrum disorder (ASD). However, it remains unclear whether anti-inflammatory diets engage distinct biological and cognitive pathways in autistic and neurotypical children. This study examined whether a 12-week anti-inflammatory dietary protocol produces group-specific neuroimmune–metabolic signatures and cognitive responses in autistic children, neurotypical children receiving the same diet, and untreated neurotypical controls. Methods: Twenty-two children (11 with ASD, six a on neurotypical diet [NT-diet], and five neurotypical controls [NT-control]) completed pre–post assessments of plasma IFN-γ, CXCL1, RANTES (CCL5), trimethylamine-N-oxide (TMAO), and an extensive ENI-2/WISC-IV neuropsychological battery. Linear mixed-effects models were used to test the Time × Group effects on biomarkers and cognitive domains, adjusting for age, sex, and baseline TMAO. Bayesian estimation quantified individual changes (posterior means, 95% credible intervals, and posterior probabilities). Immune–cognitive coupling was explored using Δ–Δ correlation matrices, network metrics (node strength, degree centrality), exploratory mediation models, and responder (≥0.5 SD domain improvement) versus non-responder analyses. Results: In ASD, the diet induced robust reductions in IFN-γ, RANTES, CXCL1, and TMAO, with decisive Bayesian evidence for IFN-γ and RANTES suppression (posterior P(δ < 0) > 0.99). These shifts were selectively associated with gains in verbal learning, semantic fluency, verbal reasoning, attention, and visuoconstructive abilities, whereas working memory and executive flexibility changes were heterogeneous, revealing executive vulnerability in individuals with smaller TMAO reductions. NT-diet children showed modest but consistent improvements in visuospatial processing, attention, and processing speed, with minimal biomarker changes; NT controls remained biologically and cognitively stable. Network analyses in ASD revealed a dense chemokine-anchored architecture with CXCL1 and RANTES as central hubs linking biomarker reductions to improvements in fluency, memory, attention, and executive flexibility. ΔTMAO predicted changes in executive flexibility only in ASD (explaining >50% of the variance), functioning as a metabolic node of executive susceptibility. Responders displayed larger coordinated decreases in all biomarkers and broader cognitive gains compared to non-responders. Conclusions: A structured anti-inflammatory diet elicits an ASD-specific, coordinated neuroimmune–metabolic response in which suppression of CXCL1 and RANTES and modulation of TMAO are tightly coupled with selective improvements in verbal, attentional, and executive domains. Neurotypical children exhibit modest metabolism-linked cognitive benefits and minimal immune modulation. These findings support a precision-nutrition framework in ASD, emphasizing baseline immunometabolic profiling and network-level biomarkers (CXCL1, RANTES, TMAO) to stratify responders and design combinatorial interventions targeting neuroimmune–metabolic pathways. Full article

Classroom acoustics and noise exposure significantly impact students’ emotional, cognitive, and academic well-being. This study investigates how classroom noise and acoustics affect auditory and cognitive performance among 131 children in three primary schools in northeast Italy. Student performance was assessed using standardised tests evaluating working memory, verbal short and long-term memory, and visuospatial memory. Children were tested under two distinct acoustic conditions: ambient classroom noise and artificially induced noise (comprising a sequence of typical internal and external classroom sounds, intelligible speech, and unintelligible conversations). Prior to testing, hearing threshold was assessed, in order to reveal any existing impairments. Following each experimental session, children rated their perceived effort and fatigue in completing the tests. Acoustic characterisation of empty classrooms was performed using Reverberation Time (T₂₀), Clarity (C₅₀), and Speech Transmission Index (STI), while noise level was measured during all testing phases. Regression analysis was employed to correlate noise levels and reverberation times with class-average performance and perception scores. Results indicate that noise significantly impaired both verbal working memory and visual attention, increasing perceived effort and fatigue. Notably, both ambient and induced noise conditions exhibited comparable adverse effects on attentional and memory task performance. These findings underscore the critical importance of acoustic design in educational environments and provide empirical support for developing classroom acoustic standards. 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

Mobile games have become one of the fastest-growing segments of the digital economy, and in-app advertisements represent a major source of revenue while shaping consumer attention and memory processes. This study examined the relationship between visual attention and brand recall of in-app advertisements in a mobile sports game using mobile eye-tracking technology. A total of 79 participants (47 male, 32 female; Mage = 25.8) actively played a mobile sports game for ten minutes while their eye movements were recorded with Tobii Pro Glasses 2. Areas of interest (AOIs) were defined for embedded advertisements, and fixation-related measures were analyzed. Brand recall was assessed through unaided, verbal-aided, and visual-aided measures, followed by demographic comparisons based on gender, mobile sports game experience and interest in tennis. Results from Generalized Linear Mixed Models (GLMMs) revealed that brand placement was the strongest predictor of recall (p < 0.001), overriding raw fixation duration. Specifically, brands integrated into task-relevant zones (e.g., the central net area) achieved significantly higher recall odds compared to peripheral ads, regardless of marginal variations in dwell time. While eye movement metrics varied by gender and interest, the multivariate model confirmed that in active gameplay, task-integration drives memory encoding more effectively than passive visual salience. These findings suggest that active gameplay imposes unique cognitive demands, altering how attention and memory interact. The study contributes both theoretically by extending advertising research into ecologically valid gaming contexts and practically by informing strategies for optimizing mobile in-app advertising. Full article

Background/Objectives: This paper presents the results of predicting chronological age from psychophysiological tests using machine learning regressors. Methods: Subjects completed a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. The sample included 99 subjects, 68 percent of whom were men and 32 percent were women. Based on the test results, 43 features were generated. To determine the optimal feature selection method, several approaches were tested alongside the regression models using MAE, $R^2$, and $CV\text{\_}{R}^2$ metrics. SHAP and Permutation Importance (via Random Forest) delivered the best performance with 10 features. Features selected through Permutation Importance were used in subsequent analyses. To predict participants’ age from psychophysiological test results, we evaluated several regression models, including Random Forest, Extra Trees, Gradient Boosting, SVR, Linear Regression, LassoCV, RidgeCV, ElasticNetCV, AdaBoost, and Bagging. Model performance was compared using the determination coefficient ($R^2$) and mean absolute error (MAE). Cross-validated performance ($CV\text{\_}{R}^2$) was estimated via 5-fold cross-validation. To assess metric stability and uncertainty, bootstrapping (1000 resamples) was applied to the test set, yielding distributions of MAE and RMSE from which mean values and 95% confidence intervals were derived. Results: The study identified RidgeCV with winsorization and standardization as the best model for predicting cognitive age, achieving a mean absolute error of 5.7 years and an $R^2$ of 0.60. Feature importance was evaluated using SHAP values and permutation importance. SHAP analysis showed that stroop_time_color and stroop_var_attempt_time were the strongest predictors, followed by several task-timing features with moderate contributions. Permutation importance confirmed this ranking, with these two features causing the largest performance drop when permuted. Partial dependence plots further indicated clear positive relationships between these key features and predicted age. Correlation analysis stratified by sex revealed that most features were significantly associated with age, with stronger effects generally observed in men. Conclusions: Feature selection revealed Stroop timing measures and task-related metrics from math and campimetry tests as the strongest predictors, reflecting core cognitive processes linked to aging. The results underscore the value of careful outlier handling, feature selection, and interpretable regularized models for analyzing psychophysiological data. Future work should include longitudinal studies and integration with biological markers to further improve clinical relevance. Full article

Background: Resting-state electroencephalography (EEG) abnormalities have been widely studied in mild cognitive impairment (MCI) and are linked to cognition. Traditionally, research has focused on the absolute power spectrum, which includes both aperiodic (1/f) and periodic components. However, fewer studies have examined aperiodic (1/f) and periodic components separately and their relationship to cognition in cognitively healthy older adults and individuals with MCI. Objectives: This study examined (i) group differences in resting-state absolute power, 1/f-adjusted power, and 1/f slope in individuals with MCI and cognitively healthy controls, and (ii) associations between cognition and 1/f-adjusted power and slope within each group. Methods: Nineteen individuals were included in each group. All participants completed eyes-open resting-state EEG and a cognitive battery assessing global functioning, cognitive control, verbal fluency, naming, and episodic memory. Absolute power and 1/f-adjusted power in theta (4–7 Hz), alpha (8–12 Hz), and beta (13–30 Hz) bands and 1/f slope were extracted. Results: No group differences emerged in the resting-state measures. In the controls, a flatter 1/f slope was linked to worse verbal fluency, but no significant associations were observed in the MCI group. Conclusions: Although there were no group differences, the link between 1/f slope and cognition in the controls highlights the value of separately examining periodic and aperiodic brain activity to better understand cognition in individuals with MCI and healthy aging. Full article

This theoretical paper introduces the Verbal–Cognitive Scaffold (VCS) Model, a cognitively inclusive framework which proposes the cognitive architectures underlying computational thinking (CT). Moving beyond monolithic theories of cognition (e.g., executive-function and metacognitive control models), the VCS Model posits inner speech (InSp) as the predominant cognitive pathway supporting CT operations in neurotypical populations. Synthesizing interdisciplinary scholarship across cognitive science, computational theory, neurodiversity research, and others, this framework articulates distinct mechanisms through which InSp supports CT. The model specifies four primary pathways linking InSp to CT components: verbal working memory supporting decomposition, symbolic representation facilitating pattern recognition and abstraction, sequential processing enabling algorithmic thinking, and dialogic self-questioning enhancing debugging processes. Crucially, the model posits these verbally mediated pathways as modal rather than universal. Although non-verbal architectures are acknowledged as possible alternative routes, their precise mechanisms remain underspecified in the existing literature and, therefore, are not the focus of the current theoretical exploration. Given this context, this manuscript focuses on the well-documented verbal support provided by InSp. The VCS Model’s theoretical contributions include the following: (1) specification of nuanced cognitive support systems where distinct InSp functions selectively enable particular CT operations; (2) generation of empirically testable predictions regarding aptitude–pathway interactions in computational training and performance; and (3) compatibility with future empirical efforts to inquire into neurodivergent strategies that may diverge from verbal architectures, while acknowledging that these alternatives remain underexplored. Individual variations in InSp phenomenology are theorized to predict distinctive patterns of CT engagement. This comprehensive framework, thus, elaborates and extends existing verbal mediation theories by specifying how InSp supports and enables CT, while laying the groundwork for possible future inquiry into alternative, non-verbal cognitive pathways. Full article

Background/Objectives: Alzheimer’s disease (AD) accounts for ~70% of global dementia cases, with projections estimating 139 million affected individuals by 2050. This increasing burden highlights the urgent need for accessible, cost-effective diagnostic tools, particularly in low- and middle-income countries (LMICs). Traditional neuropsychological assessments, while effective, are resource-intensive and time-consuming. Methods: A total of 760 older adults (394 [51.8%] with AD) were recruited and neuropsychologically evaluated at the Instituto Colombiano de Neuropedagogía (ICN) in collaboration with Universidad del Norte (UN), Barranquilla. Machine learning (ML) algorithms were trained on a screening protocol incorporating demographic data and neuropsychological measures assessing memory, language, executive function, and praxis. Model performance was determined using 10-fold cross-validation. Variable importance analyses identified key predictors to develop optimized, abbreviated ML-based protocols. Metrics of compactness, cohesion, and separation further quantified diagnostic differentiation performance. Results: The eXtreme Gradient Boosting (xgbTree) algorithm achieved the highest diagnostic accuracy (91%) with the full protocol. Five ML-optimized screening protocols were also developed. The most efficient, the ICN-UN battery (including MMSE, Rey–Osterrieth Complex Figure recall, Rey Auditory Verbal Learning, Lawton & Brody Scale, and FAST), maintained strong diagnostic performance while reducing screening time from over four hours to under 25 min. Conclusions: The ML-optimized ICN-UN protocol offers a rapid, accurate, and scalable AD screening solution for LMICs. While promising for clinical adoption and earlier detection, further validation in diverse populations is recommended. Full article

Background/Objectives: Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an effective treatment for motor symptoms in Parkinson’s disease (PD), but concerns remain regarding its impact on cognitive function. Identifying neuroanatomical predictors of postoperative cognitive decline could improve patient selection and outcomes. This study aims to investigate the relationship between preoperative brain morphology and postoperative neuropsychological outcomes in PD patients undergoing bilateral STN-DBS. Methods: Thirty-eight PD patients underwent standardized neuropsychological testing and preoperative MRI before and 3–24 months after STN-DBS. Manual MRI morphometric measurements were obtained for 42 cortical, subcortical, and ventricular parameters. Changes in cognitive domains—including executive function, memory, language, visuospatial abilities, attention, and global cognition—were analyzed, and correlations between structural metrics and cognitive changes were assessed using Spearman’s coefficients. Results: Significant postoperative declines occurred selectively in language functions: verbal fluency (phonemic and semantic, d = −0.49 to −0.84) and confrontation naming (d = −0.47). Memory, executive functions, attention, and global cognition remained preserved. Enlarged lateral ventricles were consistently associated with poorer outcomes across multiple domains, while increased left precentral gyrus width correlated with executive and memory decline. Additionally, smaller midbrain and cingulate gyrus width were associated with greater executive impairment. Conclusions: STN-DBS in PD is associated with selective postoperative cognitive changes, most prominently in verbal fluency. Simple preoperative MRI morphometric measures, including ventricular size, limbic structure volumes, and specific cortical parameters, may serve as clinically feasible predictors of cognitive risk. Incorporating such measures into preoperative assessments could enhance patient selection, counseling, and individualized surgical planning. Full article

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive disease with a major impact on respiratory function, but also with possible underestimated effects on cognitive function. Although interest in cognitive impairment in chronic respiratory diseases, such as COPD, has increased, data on IPF remain limited and heterogeneous. Objective: This systematic review aimed to synthesize current evidence on cognitive impairment in IPF, identify the most affected domains, and evaluate the certainty of evidence using standardized methodological tools. Methods: A systematic review was conducted according to PRISMA 2020, with a registered PROSPERO protocol (CRD420251041866). Four databases (PubMed, Scopus, Web of Science, Cochrane Library) were searched for studies from 2014 to 2025. Methodological quality and certainty of evidence were appraised with the Joanna Briggs Institute (JBI) and GRADE frameworks. Results: Four studies met the inclusion criteria (two cross-sectional, one descriptive, one case–control). Across investigations, working and verbal memory emerged as the most consistently impaired domains, followed by processing speed and executive function, whereas visuospatial and language abilities were less frequently affected. Cognitive impairment was present even in mild IPF and became more pronounced with lower DLCO, shorter 6 min walk distance, greater desaturation, and obstructive sleep apnea. Certainty of evidence ranged from low to moderate due to small samples and heterogeneous testing. Conclusions: Cognitive dysfunction, particularly in memory, attention, and executive domains, is a frequent but under-recognized feature of IPF. Routine screening with brief, validated tools such as the MoCA may facilitate early detection and guide individualized rehabilitation. Full article

Background: Cognitive and physical deficits are core features of schizophrenia. Although Baduanjin and brisk walking (BW) have shown promise as intervention strategies, comparative evidence with follow-up and considering maintenance is limited. Objective: This study compared the effects of Baduanjin, BW, and health education (HE) on cognitive and physical outcomes in schizophrenia and examined whether a maintenance program could sustain these effects. Methods: In this single-blind three-arm randomized controlled trial, 60 patients with schizophrenia were assigned to Baduanjin (n = 20), BW (n = 20), or HE (n = 20). Interventions were conducted three times weekly for 12 weeks, each lasting 60 min, followed by a four-week home-based maintenance program with brochures and short message reminders. Cognitive outcomes were assessed using the Brief Assessment of Cognition in Schizophrenia, and physical outcomes included the Six-Minute Walk Test (6MWT), 30-Second Chair Stand Test (30CST), Timed Up-and-Go (TUG), motor dual-task TUG (TUGmanual), and cognitive dual-task TUG (TUGcognitive). Results: Baduanjin produced larger improvements than HE in verbal memory, attention and processing speed, executive function, and global cognition. BW significantly enhanced the working memory and global cognition versus HE, with additional improvements in attention and processing speed at follow-up. Both Baduanjin and BW improved the walking distance and lower-limb strength compared with HE, while Baduanjin outperformed BW and HE in balance and dual-task outcomes. Conclusions: Baduanjin and BW improved cognitive and physical functions in individuals with schizophrenia. Maintenance programs with short message reminders may help sustain these benefits. Full article