Physical activity is a modifiable lifestyle factor with potential to support cognitive resilience. However, the association of moderate-to-vigorous physical activity (MVPA) intensity, and timing, with cognitive function and region-specific brain structure remain poorly understood. We analyzed data from 45,892 UK Biobank participants aged 60 years and older with valid wrist-worn accelerometer data, cognitive testing, and structural brain MRI. MVPA was measured both continuously (mins per week) and categorically (thresholded using >=150 min/week based on WHO guidelines). Associations with cognitive performance and regional brain volumes were evaluated using multivariable linear models adjusted for demographic, socioeconomic, and health-related covariates. We conducted secondary analyses on MVPA timing and subgroup effects. Higher MVPA was associated with better performance across cognitive domains, including reasoning, memory, executive function, and processing speed. These associations persisted in fully adjusted models and were higher among participants meeting WHO guidelines. Greater MVPA was also associated with subcortical brain regions (caudate, putamen, pallidum, thalamus), as well as regional gray matter volumes involved in emotion, working memory, and perceptual processing. Secondary analyses showed that MVPA at any time of day was associated with cognitive functions and brain volume particularly in the midday-afternoon and evening. Sensitivity analysis shows consistent findings across subgroups, with evidence of dose-response relationships. Higher MVPA is associated with preserved brain structure and enhanced cognitive function in later life. Public health strategies to increase MVPA may support healthy cognitive aging and generate substantial economic benefits, with global gains projected to reach USD 760 billion annually by 2050.

-- Despite extensive research on the relationship between sleep and cognition, the connection between sleep microstructure and human performance across specific cognitive domains remains underexplored. This study investigates whether deep learning models can predict executive functions, particularly cognitive adaptability and conceptual reasoning from physiological processes during a night's sleep. T o address this, we introduce CogPSGFormer, a multi-scale convolutional-transformer model designed to process multi-modal polysomno-graphic data. This model integrates one-channel ECG and EEG signals along with extracted features, including EEG power bands and heart rate variability parameters, to capture complementary information across modalities. A thorough evaluation of the CogPSGFormer architecture was conducted to optimize the processing of extended sleep signals and identify the most effective configuration. The proposed framework was evaluated on 817 individuals from the ST AGES dataset using cross-validation. The model achieved 80.3% accuracy in classifying individuals into low vs. high cognitive performance groups on unseen data based on Penn Conditional Exclusion T est (PCET) scores. I. INTRODUCTION Cognitive decline linked to changes in sleep characteristics--such as variations in sleep architecture, quality, and duration--represents a significant global health challenge.

Astronauts aboard the International Space Station (ISS) had slower memory, attention and processing speed after six months, raising concerns about the impact of cognitive impairment on future space missions to Mars. The extreme environment of space, with reduced gravity, harsh radiation and the lack of regular sunrises and sunsets, can have dramatic effects on astronaut health, from muscle loss to an increased risk of heart disease. However, the cognitive effects of long-term space travel are less well documented. Inside NASA's ambitious plan to bring the ISS crashing back to Earth Now, Sheena Dev at NASA's Johnson Space Center in Houston, Texas, and her colleagues have looked at the cognitive performance of 25 astronauts during their time on the ISS. The team ran the astronauts through 10 tests, some of which were done on Earth, once before and twice after the mission, while others were done on the ISS, both early and later in the mission.

Extended time in space is not exactly harmless to the human body. Radiation, altered gravity, sleep loss, can all take their toll on astronauts. Some are even hospitalized upon their return to Earth. Minor mistakes in space can have devastating consequences, so it is important to know how these stresses can impact an astronaut's cognitive performance. A new study published November 20 in the journal Frontiers in Physiology followed 25 astronauts in Low Earth orbit aboard the International Space Station (ISS).

The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement machine learning models to predict individual cognitive performance scores. We study a large-scale database from the HCP-YA study. We apply an atlas-based fiber cluster parcellation to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHAP, to assess the importance of each fiber cluster for prediction. Our results demonstrate that shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are as effective for prediction as microstructure and connectivity measures. The overall best-performing feature is a shape feature, irregularity, which describes how different a cluster's shape is from an idealized cylinder. Further interpretation using SHAP values suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.

Alterations in Heart Rate (HR) and Heart Rate Variability (HRV) can reflect autonomic dysfunction associated with neurodegeneration. We investigate the influence of Mild Cognitive Impairment (MCI) on HR and its variability measures in the Indian population by designing a complete signal processing pipeline to detect the R-wave peaks and compute HR and HRV features from ECG recordings of 10 seconds, for point-of-care applications. The study cohort involves 297 urban participants, among which 48.48% are male and 51.51% are female. From the Addenbrooke's Cognitive Examination-III (ACE-III), MCI is detected in 19.19% of participants and the rest, 80.8% of them are cognitively healthy. Statistical features like central tendency (mean and root mean square (RMS) of the Normal-to-Normal (NN) intervals) and dispersion (standard deviation (SD) of all NN intervals (SDNN) and root mean square of successive differences of NN intervals (RMSSD)) of beat-to-beat intervals are computed. The Wilcoxon rank sum test reveals that mean of NN intervals (p = 0.0021), the RMS of NN intervals (p = 0.0014), the SDNN (p = 0.0192) and the RMSSD (p = 0.0206) values differ significantly between MCI and non-MCI classes, for a level of significance, 0.05. Machine learning classifiers like, Support Vector Machine (SVM), Discriminant Analysis (DA) and Naive Bayes (NB) driven by mean NN intervals, RMS, SDNN and RMSSD, show a high accuracy of 80.80% on each individual feature input. Individuals with MCI are observed to have comparatively higher HR than healthy subjects. HR and its variability can be considered as potential biomarkers for detecting MCI.

Generative AI as a metacognitive agent: A comparative mixed-method study with human participants on ICF-mimicking exam performance Jelena Pavlović University of Belgrade, Faculty of Philosophy & Koučing centar Resarch Lab Jugoslav Krstić, Luka Mitrović, Đorđe Babić, Adrijana Milosavljević, Milena Nikolić, Tijana Karaklić & Tijana Mitrović Koučing centar Research Lab Abstract This study investigates the metacognitive capabilities of Large Language Models (LLMs) relative to human metacognition in the context of the International Coaching Federation (ICF)-mimicking exam, a situational judgment test related to coaching competencies. Using a mixed-method approach, we assessed the metacognitive performance--including sensitivity, accuracy in probabilistic predictions, and bias--of human participants and five advanced LLMs: GPT-4, Claude-3-Opus 3, Mistral Large, Llama 3, and Gemini 1.5 Pro. The results indicate that LLMs outperformed humans across all metacognitive metrics, particularly in terms of reduced overconfidence, compared to humans. However, both LLMs and humans showed less adaptability in ambiguous scenarios, adhering closely to predefined decision frameworks. The study suggests that Generative AI can effectively engage in human-like metacognitive processing without conscious awareness. Implications of the study are discussed in relation to development of AI simulators that scaffold cognitive and metacognitive aspects of mastering coaching competencies. More broadly, implications of these results are discussed in relation to development of metacognitive modules that lead towards more autonomous and intuitive AI systems. Keywords: Generative AI, metacognition, metacognitive agents, ICF exam Introduction Metacognition, the ability to understand and regulate one's cognitive processes, is a fundamental aspect of human learning, decision making and problem solving. Traditionally viewed as a conscious process, metacognition involves activities such as planning, monitoring, and evaluating one's performance during cognitive tasks. However, recent studies suggest that certain metacognitive processes can occur without conscious awareness, challenging the traditional boundaries of how metacognition is understood and measured Kentridge and Heywood (2000). In the field of generative artificial intelligence, particularly in Large Language Models (LLMs), metacognitive-like processes may manifest as algorithms adapt, learn, and optimize performance. This raises intriguing questions about the nature of metacognition in non-conscious entities and its comparison to human metacognitive processes. The present study aims to explore these questions by comparing the metacognitive processes of human participants and LLMs within the context of the International Coaching Federation (ICF) exam performance.

Predicting and understanding the changes in cognitive performance, especially after a longitudinal intervention, is a fundamental goal in neuroscience. Longitudinal brain stimulation-based interventions like transcranial direct current stimulation (tDCS) induce short-term changes in the resting membrane potential and influence cognitive processes. However, very little research has been conducted on predicting these changes in cognitive performance post-intervention. In this research, we intend to address this gap in the literature by employing different EEG-based functional connectivity analyses and machine learning algorithms to predict changes in cognitive performance in a complex multitasking task. Forty subjects were divided into experimental and active-control conditions. On Day 1, all subjects executed a multitasking task with simultaneous 32-channel EEG being acquired. From Day 2 to Day 7, subjects in the experimental condition undertook 15 minutes of 2mA anodal tDCS stimulation during task training. Subjects in the active-control condition undertook 15 minutes of sham stimulation during task training. On Day 10, all subjects again executed the multitasking task with EEG acquisition. Source-level functional connectivity metrics, namely phase lag index and directed transfer function, were extracted from the EEG data on Day 1 and Day 10. Various machine learning models were employed to predict changes in cognitive performance. Results revealed that the multi-layer perceptron and directed transfer function recorded a cross-validation training RMSE of 5.11% and a test RMSE of 4.97%. We discuss the implications of our results in developing real-time cognitive state assessors for accurately predicting cognitive performance in dynamic and complex tasks post-tDCS intervention

A study of 1400 preschool children in Canada has found that those tested during the covid-19 pandemic did better on several cognitive measures than those assessed before the outbreak began. The team behind these results thinks this is because these children have parents with a relatively high income who may have spent more time with them during the height of the pandemic. Most of the other studies looking at how the pandemic has affected children concluded that it has been overwhelmingly negative. However, these studies almost all looked at social and emotional skills rather than cognitive abilities and at school-age children rather than preschool children, says Mark Wade at the University of Toronto, who was involved in the latest Canadian research. "It isn't necessarily the case that the pandemic has been totally and irreversibly bad for kids," he says.

When asked to think of someone really'gritty', an image of US marshal Rooster Cogburn in the film'True Grit' may spring to mind. Throughout the story, Cogburn demonstrates an unwavering determination to catch the criminals and fugitives he pursues, and protect 14-year-old Mattie, who has hired him to track down her father's killer. Now a new study has found that the brains of'gritty' people like Cogburn work differently to those who are less perseverant towards their goals. It found that people who are more determined to achieve their long-term goals find it easier to consider all available information while remaining sensitive to new conflicting information. This may help them be more aware of the presence of conflicting goals in their everyday life that could take them off-track from their longer term ones.