Search Results (175)

This study proposes a framework that leverages natural language processing and unsupervised machine learning techniques to measure, identify, and classify examinees’ writing strategies. The framework integrates three categories of writing strategies (text complexity, evidence use, and argument structure) to identify the characteristics of examinees’ writing. Additionally, a measurement model is used to calibrate examinees’ writing proficiency. An empirical example is presented to demonstrate the performance of the framework. The data comprise 430 Grade 8 examinees’ responses to English Language Arts (ELA) assessments in the United States. Using K-means clustering, distinct patterns were identified in each category. The one-parameter logistic measurement model was applied to estimate examinees’ writing proficiency. Analyses revealed significant effects of text complexity and evidence use on writing proficiency, while argument structure was not significant. This study has implications for writing instruction and assessment design that highlight the point that effective writing is not simply a matter of isolated skill acquisition, but rather the coordinated implementation of complementary strategies, a finding that supports cognitive developmental theories of writing. Full article

Introduction: Air pollution is a risk factor for a variety of cardiopulmonary diseases and is a contributing factor to cancer, diabetes, and cognitive impairment. The impact on mortality is not clearly elucidated. Objectives: The goal of this study is to determine the impact (if any) of air pollution on the 5-year mortality of patients in the American Family Cohort (AFC) dataset. Methods: The AFC dataset is derived from the American Board of Family Medicine PRIME Registry electronic health record data. It includes longitudinal information from 6.6 million unique patients from an estimated 800 primary care practices across 47 states, with 40% coming from rural areas. The Environmental Protection Agency’s Air Quality Index (AQI) measures were downloaded for the study period (2016–2022). Using the Python library pandas, the AFC and EPA datasets were merged with respect to date, time, and location. Cox Regression Models were performed on the merged dataset to determine the impact (if any) of air quality on patients’ five-year survival. In the model, AQI was handled as a time-independent (time-fixed) covariate. Results: The group with AQI > 50 had an adjusted hazard of death that was 4.02 times higher than the hazard of death in the group with AQI ≤ 50 (95% CI: 3.36, 4.82, p < 0.05). The hazard of death was 6.73 times higher in persons older than 80 years of age (95% CI: 5.47, 8.28; p < 0.05) compared to those younger than 80 years of age. Black/African American patients had a 4.27 times higher hazard of death (95%CI: 3.47, 5.26; p < 0.05) compared to other races. We also found that regional effects played a role in survival. Conclusions: Poor air quality was associated with a higher hazard of mortality, and this phenomenon was particularly pronounced in Black/African American patients and patients older than 80 years of age. Air pollution is an important social determinant of health. Public health initiatives that improve air quality are necessary to improve health outcomes. Full article

Background: The N-methyl-d-aspartate receptor (NMDA-R) is a glutamate ionotropic receptor in the brain that is crucial for synaptic plasticity, which underlies learning and memory formation. Dysfunction of NMDA receptors is implicated in various neurological diseases due to their roles in both normal cognition and excitotoxicity. However, their dynamics are challenging to capture accurately due to their high complexity and non-linear behavior. Methods: This article presents the elaboration and calibration of experimentally constrained computational models of GluN1/GluN2A NMDA-R dynamics: (1) a nine-state kinetic model optimized to replicate experimental data and (2) a computationally efficient look-up table model capable of replicating the dynamics of the nine-state kinetic model with a highly reduced footprint. Determination of the kinetic model’s parameter values was performed using the particle swarm optimization algorithm. The optimized kinetic model was then used to generate a rich input–output dataset to train the look-up table synapse model and estimate its coefficients. Results: Optimization produced a kinetic model capable of accurately reproducing experimentally found results such as frequency-dependent potentiation and the temporal response due to synaptic release of glutamate. Furthermore, the look-up table synapse model was able to closely mimic the dynamics of the optimized kinetic model. Conclusions: The results obtained with both models indicate that they constitute accurate alternatives for faithfully reproducing the dynamics of NMDA-Rs. High computational efficiency is also achieved with the use of the look-up table synapse model, making this implementation an ideal option for inclusion in large-scale neuronal models. Full article

Background/Objectives: Gamma oscillations (30–100 Hz), which are essential for memory, attention, and cortical synchronization, remain underexplored in Alzheimer’s disease (AD) research. While resting-state EEG studies have predominantly examined lower frequency bands (delta to beta), gamma activity may more accurately reflect early synaptic dysfunction and other mechanisms relevant to AD pathophysiology. AD is a common age-related neurodegenerative disorder frequently associated with altered resting-state EEG (rEEG) patterns. This study analyzed gamma power spectral density (PSD) during eyes-open (EOR) and eyes-closed (ECR) resting-state EEG in AD patients compared to cognitively normal (CN) individuals. Methods: rEEG data from 534 participants (269 CN, 265 AD) aged 40–90 were analyzed. Quantitative EEG (qEEG) analysis focused on the gamma band (30–100 Hz) using PSD estimation with the Welch method, coherence matrices, and coherence-based functional connectivity. Data preprocessing and analysis were performed using EEGLAB and Brainstorm in MATLAB R2024b. Group comparisons were conducted using ANOVA for unadjusted models and linear regression with age adjustment using log₁₀-transformed PSD values in Python (version 3.13.2, 2025). Results: AD patients exhibited significantly elevated gamma PSD in frontal and temporal regions during EOR and ECR states compared to CN. During ECR, gamma PSD was markedly higher in the AD group (Mean = 0.0860 ± 0.0590) than CN (Mean = 0.0042 ± 0.0010), with a large effect size (Cohen’s d = 1.960, p < 0.001). Conversely, after adjusting for age, the group difference was no longer statistically significant (β = −0.0047, SE = 0.0054, p = 0.391), while age remained a significant predictor of gamma power (β = −0.0008, p = 0.019). Pairwise coherence matrix and coherence-based functional connectivity were increased in AD during ECR but decreased in EOR relative to CN. Conclusions: Gamma oscillatory activity in the 30–100 Hz range differed significantly between AD and CN individuals during resting-state EEG, particularly under ECR conditions. However, age-adjusted analyses revealed that these differences are not AD-specific, suggesting that gamma band changes may reflect aging-related processes more than disease effects. These findings contribute to the evolving understanding of gamma dynamics in dementia and support further investigation of gamma PSD as a potential, age-sensitive biomarker. Full article

Background/Objectives: Cognitive impairment is a frequent and underrecognized comorbidity in elderly patients with heart failure with preserved ejection fraction (HFpEF), contributing to poor outcomes and complicating disease management. This study aimed to identify risk factors associated with cognitive impairment in elderly HFpEF patients from Western Romania and to develop a point-based risk score for clinical use. Methods: We conducted a cross-sectional analysis of HFpEF patients aged ≥65 years. Cognitive status was assessed using the Mini-Mental State Examination-2 (MMSE-2), with significant impairment defined as a score <24. Multivariable logistic regression analysis was performed to identify independent predictors of cognitive dysfunction. Results: A total of 326 HFpEF patients were included. Diabetes mellitus, prior stroke or transient ischemic attack (TIA), carotid artery disease, elevated N-terminal pro–B-type natriuretic peptide (NT-proBNP), and reduced estimated glomerular filtration rate (eGFR) were independently associated with cognitive impairment. Higher Kansas City Cardiomyopathy Questionnaire (12-KCCQ) scores and anticoagulant therapy for atrial fibrillation were associated with a lower risk. Based on these variables, a simple point-based cognitive risk score was developed, demonstrating strong discriminatory ability (area under the curve = 0.84). A threshold of ≥2 points identified cognitive impairment with 75% sensitivity and 83% specificity. Conclusions: Our findings underscore the importance of integrated cardiovascular and cognitive assessment in elderly HFpEF patients. The developed risk score offers a pragmatic tool for the early identification of cognitive dysfunction, potentially informing timely interventions and preventive strategies. Full article

Driven by the commercialization of intelligent ships, the increasingly complex mixed maritime traffic environment presents significant challenges for collision avoidance between multiple ships due to cognitive and behavioral differences between intelligent and traditional ships. Therefore, it is essential to develop a human-like collision avoidance strategy that incorporates traditional navigational experience and handling practices, enhancing explainability and autonomy. By addressing the actual decision-making needs for predicting other ships’ intentions and considering potential risk impacts, a hierarchical strategy is designed that first seeks course direction adjustment and then determines the magnitude of adjustment. A direction adjustment intention estimation model is proposed, accounting for risk membership and COLREGS, to predict other ships’ collision avoidance intentions. Additionally, an intention influence model and a state influence model are introduced to design decision-making objectives, forming an optimization function based on angle range and maneuvering time constraints to determine the appropriate adjustment magnitude. The results demonstrate the strategy’s effectiveness across various scenarios. Specifically, the distance between ships increased by nearly 25% during the process, significantly enhancing safety. It is worth mentioning that the model has the potential to enhance intelligent ships’ capabilities in complex situational handling and intention understanding. Full article

Background: The sense of agency (SoA)—the subjective experience of controlling one’s own actions and their consequences—is a fundamental aspect of human cognition, volition, and motor control. Understanding how the SoA arises and is disrupted in neuropsychiatric disorders has significant implications for human–machine interface (HMI) design for neurorehabilitation. Traditional cognitive models of agency often fail to capture its full complexity, especially in dynamic and uncertain environments. Objective: This review synthesizes computational models—particularly predictive coding, Bayesian inference, and optimal control theories—to provide a unified framework for understanding the SoA in both healthy and dysfunctional brains. It aims to demonstrate how these models can inform the design of adaptive HMIs and therapeutic tools by aligning with the brain’s own inference and control mechanisms. Methods: I reviewed the foundational and contemporary literature on predictive coding, Kalman filtering, the Linear–Quadratic–Gaussian (LQG) control framework, and active inference. I explored their integration with neurophysiological mechanisms, focusing on the somato-cognitive action network (SCAN) and its role in sensorimotor integration, intention encoding, and the judgment of agency. Case studies, simulations, and XR-based rehabilitation paradigms using robotic haptics were used to illustrate theoretical concepts. Results: The SoA emerges from hierarchical inference processes that combine top–down motor intentions with bottom–up sensory feedback. Predictive coding frameworks, especially when implemented via Kalman filters and LQG control, provide a mechanistic basis for modeling motor learning, error correction, and adaptive control. Disruptions in these inference processes underlie symptoms in disorders such as functional movement disorder. XR-based interventions using robotic interfaces can restore the SoA by modulating sensory precision and motor predictions through adaptive feedback and suggestion. Computer simulations demonstrate how internal models, and hypnotic suggestions influence state estimation, motor execution, and the recovery of agency. Conclusions: Predictive coding and active inference offer a powerful computational framework for understanding and enhancing the SoA in health and disease. The SCAN system serves as a neural hub for integrating motor plans with cognitive and affective processes. Future work should explore the real-time modulation of agency via biofeedback, simulation, and SCAN-targeted non-invasive brain stimulation. Full article

Background: Sex differences in the association of cognitive function and imaging measures with dementia have not been fully investigated. Understanding sex differences in the dementia-related socioeconomic, cognitive, and imaging measurements is crucial for uncovering sex-related pathways to dementia and facilitating early diagnosis, family planning, and cost control. Methods: We selected data from the Open Access Series of Imaging Studies, with longitudinal measurements of brain volumes, on 150 individuals aged 60 to 96 years. Dementia status was determined using the Clinical Dementia Rating (CDR) scale, and Alzheimer’s disease was diagnosed as a CDR of ≥0.5. Generalized estimating equation models were used to estimate the associations of socioeconomic, cognitive, and imaging factors with dementia in men and women. Results: The study sample consisted of 88 women (58.7%) and 62 men (41.3%), and the average age of the subjects was 75.4 years at the initial visit. A lower socioeconomic status was associated with a reduced estimated total intracranial volume in men, but not in women. Ageing and lower MMSE scores were associated with a reduced nWBV in both men and women. Lower education affected dementia more in women than in men. Age, education, Mini-Mental State Examination (MMSE), and normalized whole-brain volume (nWBV) were associated with dementia in women, while only MMSE and nWBV were associated with dementia in men. Conclusions: The association between education and the prevalence of dementia differs in men and women. Women may have more risk factors for dementia than men. Full article

Background/Objective: Food insecurity remains a significant public health concern that negatively impacts college students’ academic performance and health. One in three college students experiences inconsistent access to food, known as food insecurity, which has attracted significant research interest. This study examined the effectiveness of the 10-item United States Department of Agriculture Food Security Scale Module (USDA-FSSM) in accurately and effectively measuring food security among college students. Methods: A mixed-methods approach was utilized to assess qualitative individual cognitive interviews and survey quantitative data. An online survey was used to collect demographic data and food security status from 462 college students recruited from three public universities in the United States. Qualitative interviews with a subset of participants (n = 26) were conducted to gain further insight into college students’ perceptions and interpretations of the 10-item USDA food security survey. Results: Fourteen (14%) participants were food-insecure, and 12% were at risk of food insecurity. Qualitative data revealed that students misinterpreted some of the language used in the 10-item USDA-FSSM. Participants also indicated difficulty estimating food security experiences over the 12-month reference period in the 10-item USDA-FSSM. Conclusions: This study demonstrates that college students misinterpret food security terms in the 10-item USDA-FSSM, which affects the prevalence rate determined by the measure, emphasizing the need for a validated college student-specific food security survey to inform effective policy and interventions. Full article

On 11 March 2020, the World Health Organization (WHO) declared a pandemic caused by SARS-CoV-2, raising global health concerns. Reports of persistent and new symptoms following the acute phase of infection highlighted the complexities of recovery and prompted the investigation of what is now termed long COVID. Officially recognized by the WHO in October 2021, long COVID presents various health implications, though the terminology—such as post-COVID syndrome and post-acute sequelae of COVID-19 (PASC)—remains inconsistent, complicating diagnostic standardization. Long COVID affects an estimated 10% to 30% of SARS-CoV-2-infected individuals, with common symptoms including fatigue, dyspnea, cognitive dysfunction, and joint pain, all of which significantly impair quality of life. Public perception is influenced by factors like education and health history, while misinformation and stigma hinder accurate diagnosis and treatment. The absence of biomarkers and overlap with other post-viral syndromes further complicate clinical recognition. Experts emphasize the need for refined diagnostic criteria and integrated strategies combining biomedical research, public policy, and educational initiatives to improve clinical management, address healthcare inequalities, and mitigate the impacts of long COVID. This review unveils the state of the art and knowledge gaps to encourage discussion, with the aim of achieving better clinical decision-making and public awareness related to long COVID. Full article

Classification of neurocognitive states from Electroencephalography (EEG) data is complex due to inherent challenges such as noise, non-stationarity, non-linearity, and the high-dimensional and sparse nature of connectivity patterns. Graph-theoretical approaches provide a powerful framework for analysing the latent state dynamics using connectivity measures across spatio-temporal-spectral dimensions. This study applies the graph Koopman embedding kernels (GKKE) method to extract latent neuro-markers of seizures from epileptiform EEG activity. EEG-derived graphs were constructed using correlation and mean phase locking value (mPLV), with adjacency matrices generated via threshold-binarised connectivity. Graph kernels, including Random Walk, Weisfeiler–Lehman (WL), and spectral-decomposition (SD) kernels, were evaluated for latent space feature extraction by approximating Koopman spectral decomposition. The potential of graph Koopman embeddings in identifying latent metastable connectivity structures has been demonstrated with empirical analyses. The robustness of these features was evaluated using classifiers such as Decision Trees, Support Vector Machine (SVM), and Random Forest, on Epilepsy-EEG from the Children’s Hospital Boston’s (CHB)-MIT dataset and cognitive-load-EEG datasets from online repositories. The classification workflow combining mPLV connectivity measure, WL graph Koopman kernel, and Decision Tree (DT) outperformed the alternative combinations, particularly considering the accuracy (91.7%) and F1-score (88.9%), The comparative investigation presented in results section convinces that employing cost-sensitive learning improved the F1-score for the mPLV-WL-DT workflow to 91% compared to 88.9% without cost-sensitive learning. This work advances EEG-based neuro-marker estimation, facilitating reliable assistive tools for prognosis and cognitive training protocols. Full article

Alzheimer’s disease (AD) affects an estimated 6.9 million older adults in the United States and is projected to impact as many as 13.8 million people by 2060. As studies continue to search for ways to combat the development and progression of AD, it is imperative to ensure that confident diagnoses can be made before the onset of severe clinical symptoms and new therapies can be evaluated effectively. Tau positron emission tomography (PET) has emerged as one method that may be capable of both, given its ability to recognize the presence of tau, a primary pathologic hallmark of AD; its usefulness in determining the spatial distribution of tau, which is necessary for differentiating AD from other tauopathies; and its association with measures of cognition. This review aims to evaluate the scope of tau PET’s utility in clinical trials and practice. Firstly, the potential of using tau PET for differential diagnoses, distinguishing AD from other dementias, is considered. Next, the value of tau PET as a tool for staging disease progression is investigated. Finally, tau PET as a prognostic method for identifying the individuals most at risk of cognitive decline and, therefore, most in need of, and likely to benefit from, intervention, is discussed. Full article

Particle swarm optimization (PSO), an important solving method in the field of swarm intelligence, is recognized as one of the most effective metaheuristics for addressing optimization problems. Many adaptive strategies have been developed to improve the performance of PSO. Despite these advances, a key problem lies in defining the configuration criteria of the adaptive algorithm. This study presents an adaptive variant of PSO that relies on fitness landscape analysis, particularly via ruggedness factor estimation. Our approach involves adaptively updating the cognitive and acceleration factors based on the estimation of the ruggedness factor using a machine learning-based method and a deterministic way. We tested them on global optimization functions and the feature selection problem. The proposed method gives encouraging results, outperforming native PSO in almost all instances and remaining competitive with state-of-the-art methods. Full article

Human–robot collaborative systems have been adopted by manufacturing organizations with the objective of releasing physical workload to the human factor. However, the roles and responsibilities of human operators in these semi-automated systems have not been properly analyzed. This might carry important consequences in the cognitive dimension of ergonomics, which then contradicts the main well-being goals of collaborative work. Therefore, we designed a series of collaborative scenarios where we shifted the assignment of work responsibilities between humans and robots while executing a quality inspection task. Variations in the state of cognitive ergonomics were estimated with subjective and objective techniques via workload tests and physiological responses respectively. Furthermore, we introduced a work design framework based on 50 state-of-the-art applications for a structured implementation of human–robot collaborative systems that contemplates the underlying organizational and technological components necessary to fulfill its basic functionalities. Human operators that possessed responsibility roles over collaborative robots presented better results in terms of cognitive workload and spare mental capacity alike. In this regard, mental demand is seen as a key workload variable to consider when designing collaborative work in current manufacturing settings. Full article

Patients with mild cognitive impairment due to Alzheimer’s disease (ADMCI) typically show abnormally high delta (<4 Hz) and low alpha (8–12 Hz) rhythms measured from resting-state eyes-closed electroencephalographic (rsEEG) activity. Here, we hypothesized that the abnormalities in rsEEG activity may be greater in ADMCI patients than in those with MCI not due to AD (noADMCI). Furthermore, they may be associated with the diagnostic cerebrospinal fluid (CSF) amyloid–tau biomarkers in ADMCI patients. An international database provided clinical–demographic–rsEEG datasets for cognitively unimpaired older (Healthy; N = 45), ADMCI (N = 70), and noADMCI (N = 45) participants. The rsEEG rhythms spanned individual delta, theta, and alpha frequency bands. The eLORETA freeware estimated cortical rsEEG sources. Posterior rsEEG alpha source activities were reduced in the ADMCI group compared not only to the Healthy group but also to the noADMCI group (p < 0.001). Negative associations between the CSF phospho-tau and total tau levels and posterior rsEEG alpha source activities were observed in the ADMCI group (p < 0.001), whereas those with CSF amyloid beta 42 levels were marginal. These results suggest that neurophysiological brain neural oscillatory synchronization mechanisms regulating cortical arousal and vigilance through rsEEG alpha rhythms are mainly affected by brain tauopathy in ADMCI patients. Full article