We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49–89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06–1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76–3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33–2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44–3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43–4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

This cross-sectional, observational study aimed to integrate the analyses of relationships of physical activity, depression, and sleep with cognitive function in community-dwelling older adults using a single model. To this end, physical activity, sleep, depression, and cognitive function in 864 community-dwelling older adults from the Suwon Geriatric Mental Health Center were assessed using the International Physical Activity Questionnaire, Montgomery-Asberg Depression Rating Scale, Pittsburgh Sleep Quality Index, and Mini-Mental State Examination for Dementia Screening, respectively. Their sociodemographic characteristics were also recorded. After adjusting for confounders, multiple linear regression analysis was performed to investigate the effects of physical activity, sleep, and depression on cognitive function. Models 4, 5, 7, and 14 of PROCESS were applied to verify the mediating and moderating effects of all variables. Physical activity had a direct effect on cognitive function (effect = 0.97, p < 0.01) and indirect effect (effect = 0.36; confidence interval: 0.18, 0.57) through depression. Moreover, mediated moderation effects of sleep were confirmed in the pathways where physical activity affects cognitive function through depression (F-coeff = 13.37, p < 0.001). Furthermore, these relationships differed with age. Thus, the associations among physical activity, depression, and sleep are important in interventions for the cognitive function of community-dwelling older adults. Such interventions should focus on different factors depending on age.

BackgroundIdentifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited. This study aimed to develop psychosis-relapse prediction models using various types of clinical notes and structured data.MethodsClinical data were extracted from the electronic health records of the Ajou University Medical Center in South Korea. The study population included patients with psychotic disorders, and outcome was psychosis relapse within 1 year. Using only structured data, we developed an initial prediction model, then three natural language processing (NLP)-enriched models using three types of clinical notes (psychological tests, admission notes, and initial nursing assessment) and one complete model. Latent Dirichlet Allocation was used to cluster the clinical context into similar topics. All models applied the least absolute shrinkage and selection operator logistic regression algorithm. We also performed an external validation using another hospital database.ResultsA total of 330 patients were included, and 62 (18.8%) experienced psychosis relapse. Six predictors were used in the initial model and 10 additional topics from Latent Dirichlet Allocation processing were added in the enriched models. The model derived from all notes showed the highest value of the area under the receiver operating characteristic (AUROC = 0.946) in the internal validation, followed by models based on the psychological test notes, admission notes, initial nursing assessments, and structured data only (0.902, 0.855, 0.798, and 0.784, respectively). The external validation was performed using only the initial nursing assessment note, and the AUROC was 0.616.ConclusionsWe developed prediction models for psychosis relapse using the NLP-enrichment method. Models using clinical notes were more effective than models using only structured data, suggesting the importance of unstructured data in psychosis prediction.

The field of neuroarchitecture explores how various architectural elements impact human physical and mental health, based on neuroscience principles. With the development of functional neuroimaging and electroencephalogram studies, researchers can now visualize and quantify how different architectural factors affect brain activity, emotions, and cognition. Mobile Brain/Body Imaging is a new research methodology that records a moving person’s brain activity and bodily sensations in real time, promising to be a useful tool for space and urban design. In this article, we discuss neuroarchitecture from the perspective of circadian rhythm, physical health, and mental health. Studies have shown that artificial light at night disrupts the circadian rhythm, leading to acute and chronic negative health effects. Conversely, creating a personcentered light environment or incorporating nature-like elements can have a positive impact on health. Research has also shown that exposure to nature reduces self-rumination and contributes to psychological well-being. Neuroarchitecture studies on other factors, such as ceiling height, wall colors, and the movement of people in the building, should be expanded to gain greater insights and practical applications. The convergence of neuroscience and architecture has the potential to identify architectural elements that benefit human physical and mental health.

Background Long commuting times have a negative impact on mental health. However, few studies have explored the relationship between commuting time and well-being based on urbanization by region. Our study examines this relationship as well as the effect of regional differences on Korean workers. Methods We used data from the sixth Korean Working Conditions Survey. Commuting time and occupational factors were assessed using a questionnaire, and subjective well-being was assessed using the World Health Organization-5 Well-Being Index. Regions were divided into the cities and the provinces based on Korea’s administrative divisions. Logistic regression analysis was performed to investigate the association between commuting time and well-being. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) for well-being were estimated, using participants commuting time of < 20 minutes as a reference group. Results The total number of workers was 29,458 (13,855 men, 15,603 women). We found higher aORs for low well-being among workers with long commuting times (aOR, 1.23; 95% CI, 1.11–1.36 and aOR, 1.28; 95% CI, 1.16–1.42 for 60–79 and ≥ 80 minutes, respectively). When stratified by sex and region, higher aORs for low well-being were found only in the workers who lived in cities. Conclusion Long commuting time was negatively associated with well-being in Korean wage workers living in the cities. Policies for reducing commuting time should be discussed to address the mental health of workers, especially those living in metropolitan cities.