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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.

The moderating effect of cognitive function on the association between social support and late-life depressive symptoms has not been thoroughly investigated. Identifying cognitive function as a possible moderator of this association might help plan community-based interventions for late-life depressive symptoms.Participants were community-dwelling older adults who visited a community-based mental health center. The ENRICHD Social Support Instrument and the Montgomery-Asberg Depression Rating Scale were used to evaluate social support and depressive symptoms, respectively. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination. Data from 1088 and 506 participants were included in the cross-sectional and longitudinal analyses, respectively. Multiple linear regression analysis was performed to assess the effects of social support on depressive symptoms and the possible moderating effect of cognition.After adjusting for possible confounders, greater social support at baseline was associated with fewer depressive symptoms in both cross-sectional (estimate = -0.25 standard error [SE] = 0.03, P 0.001) and longitudinal analyses (estimate = -0.11, SE = 0.05, P = 0.014). Moreover, the association between social support and depressive symptoms was significantly moderated by cognitive function (P for interaction0.001 for cross-sectional analysis, and P for interaction = 0.011 for longitudinal analysis).The tool for assessing social support was self-reported. There may have been a selection bias in the study sample.Greater social support was associated with fewer late-life depressive symptoms in both analyses. However, social support may have less benefits for alleviating depressive symptoms in older adults with cognitive decline.

Synergistic effects of amyloid deposition and cerebral small vessel disease (CSVD) on the systematic disruption of large-scale brain anatomical organization are not well known. We investigated the brain structural covariance network (SCN) in 245 cognitively impaired older adults with the information of amyloid deposition and CSVD represented by white matter hyperintensities (WMH). We stratified the participants into 4 groups based on amyloid burden (A+/A -) and WMH severity (W+/W-). Using source-based morphometry analysis, we selected 13 independent components (ICs) in functional brain networks. SCNs between ICs were defined using Pearson correlations between individual weights; SCNs of the A+W+ group were compared with those of other groups using Fisher's r-to-z transformation. Our results revealed that SCN characteristics related to amyloid burden with CSVD could be represented by decreased intra- and increased cortico-subcortical inter-network connectivity in the salience (SN) and default mode networks (DMN), decreased cortico-subcortical internetwork connectivity in the central executive network (CEN), and altered internetwork connectivity among DMN-SN-CEN. Amyloid deposition and CSVD maybe associated with altered connectivity in structural networks in the brain and should be considered when assessing network disruption.

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 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.

Objective We aimed to present the study design and baseline cross-sectional participant characteristics of biobank innovations for chronic cerebrovascular disease with Alzheimer’s disease study (BICWALZS) participants.Methods A total of 1,013 participants were enrolled in BICWALZS from October 2016 to December 2020. All participants underwent clinical assessments, basic blood tests, and standardized neuropsychological tests (n=1,013). We performed brain magnetic resonance imaging (MRI, n=817), brain amyloid positron emission tomography (PET, n=713), single nucleotide polymorphism microarray chip (K-Chip, n=949), locomotor activity assessment (actigraphy, n=200), and patient-derived dermal fibroblast sampling (n=175) on a subset of participants.Results The mean age was 72.8 years, and 658 (65.0%) were females. Based on clinical assessments, total of 168, 534, 211, 80, and 20 had subjective cognitive decline, mild cognitive impairment (MCI), Alzheimer’s dementia, vascular dementia, and other types of dementia or not otherwise specified, respectively. Based on neuroimaging biomarkers and cognition, 199, 159, 78, and 204 were cognitively normal (CN), Alzheimer’s disease (AD)-related cognitive impairment, vascular cognitive impairment, and not otherwise specified due to mixed pathology (NOS). Each group exhibited many differences in various clinical, neuropsychological, and neuroimaging results at baseline. Baseline characteristics of BICWALZS participants in the MCI, AD, and vascular dementia groups were generally acceptable and consistent with 26 worldwide dementia cohorts and another independent AD cohort in Korea.Conclusion The BICWALZS is a prospective and longitudinal study assessing various clinical and biomarker characteristics in older adults with cognitive complaints. Details of the recruitment process, methodology, and baseline assessment results are described in this paper.

Actigraphy-derived locomotor activity recordings are novel and critical tools for evaluating rest-activity pattern and circadian phase in humans. We conducted a narrative review assessing rest-activity pattern and circadian phase alterations within various stages of Alzheimer’s disease, in consideration of the reciprocal associations between neurodegeneration and circadian rhythm disruption in patients with Alzheimer’s disease. The goal of this review was to characterize possible associations between circadian rhythm disruption and neurodegeneration in Alzheimer’s disease. To the best of our knowledge, only two studies have assessed rest-activity pattern and circadian phase alterations in the preclinical Alzheimer’s disease stage and the results of the studies were inconsistent. Several studies have evaluated rest-activity pattern and circadian phase alterations in patients with Alzheimer’s dementia. The most replicated findings were delayed phase and increased activity fragmentation, represented as increased intra-daily variability. Unfortunately, many studies performed in dementia patients have not examined neuroimaging biomarkers or structured neuropsychological tests, thus limiting the specification of dementia clinical diagnoses. Future studies should consider a more comprehensive evaluation of various clinical and biomarker characteristics in patients with dementia or Alzheimer’s disease.

BackgroundAlzheimer’s disease (AD) is a complex multifactorial progressive dementia affecting all human populations. As a collaboration model between the International Hundred K+ Cohorts Consortium (IHCC) and the Davos Alzheimer Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for AD.MethodsThe GIRA model was created to include a polygenic risk score (PRS) calculated from the AD GWAS loci, theAPOEhaplotypes, and non-genetic covariates including age, sex and first 3 principal components of population substructure. The model was first validated using a ancestrally diverse dataset from the eMERGE network, and subsequently validated in a South-Asian population in the UK and 3 East-Asian populations. The distributions of the PRS scores were also explored in populations from 3 African regions. In two validation sites, the PRS was tested for associated with the levels of plasma proteomics markers.ResultsWe created a trans-ethnic GIRA model for the risk prediction of AD and validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD, highlighting molecular mechanisms underlying the previously observed correlations between these clinical phenotypes.ConclusionsAs the initial effort by the IHCC to leverage existing large scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications. The PRS scores in this model also contribute new research discoveries for the molecular pathogenesis of AD as demonstrated by the proteomic data.

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.

Objective Early detection and proper management of mental illness can help to prevent severe deterioration. However, with limited financial and human resources of community mental health services, it is not practical to carry out all conventional screening tools simultaneously. In this study, we aimed to develop and validate a brief but comprehensive screening questionnaire for four common mental illnesses of the elderly.Methods The brief screening for four mental illnesses of elderly (BS4MI-elderly) is a 14-item binary response questionnaire that covers dementia, depressive disorder, sleep disorder, and hwa-byung. To test validity, we compared conventional scale scores for three groups of participants classified using the BS4MI-elderly. The sensitivity, specificity, predictive value of positive test, likelihood ratio of positive test and internal consistency of the BS4MI-elderly were assessed. Finally, a correlation analysis between the BS4MI-elderly and general mental health scales was conducted.Results A total of 254 participants aged over 65 years were recruited. The BS4MI-elderly showed moderate to high sensitivity for the test that distinguishes the normal group from the risk and disorder groups (dementia: 0.61, depressive disorder: 0.88, sleep disorder: 0.85, hwa-byung: 0.94) and high specificity for the test that distinguishes the disorder group from the normal and risk groups (dementia: 0.91, depressive disorder: 0.93, hwa-byung: 0.84, sleep disorder: 0.84). The BS4MI-elderly also exhibited good internal consistency and significant correlations with general mental health scales.Conclusion The BS4MI-elderly, a brief but comprehensive screening tool, could be a useful instrument for screening the elderly in community mental health services.

We explored the associations of actigraphy-derived rest-activity patterns and circadian phase parameters with clinical symptoms and level 1 polysomnography (PSG) results in patients with chronic insomnia to evaluate the clinical implications of actigraphy-derived parameters for PSG performance. Seventy-five participants underwent actigraphy assessments and level 1 PSG. Exploratory correlation analyses between parameters derived from actigraphy, PSG, and clinical assessments were performed. Study participants were classified into two groups, and group differences were investigated after adjusting for potential covariates. Participants with poorer rest-activity patterns on actigraphy (low inter-day stability and high intra-daily variability) exhibited higher insomnia severity index scores compared to participants with better rest-activity patterns. No between-group differences in PSG parameters were observed. Late-phase participants (late least active 5-h and most active 10-h onset times) exhibited higher insomnia severity scores, longer sleep latency, longer rapid eye movement latency, and lower apnea-hypopnea index compared to early-phase participants. These associations remained significant even after adjusting for potential covariates. Some actigraphy-derived rest-activity patterns and circadian phase parameters were significantly associated with clinical symptoms and PSG results, suggesting a possible adjunctive role of actigraphy-derived rest-activity patterns and circadian phase parameters in deriving plans for PSG lights-off time and assessing the possible insomnia pathophysiology.

Accumulating evidence indicates that amyloid-beta (Aβ) deposition and biogenic aldehyde accumulation contribute to the pathogenesis of neurodegenerative diseases. Human aldehyde dehydrogenase 2 (ALDH2) metabolizes biogenic aldehydes produced in the brain to prevent damage. However, r671G>A, a single nucleotide polymorphism of ALDH2, causes aldehyde accumulation and decreased ALDH2 activity. We aimed to investigate whether Aβ deposition and rs671 polymorphism have an interaction effect on cortical thickness (CTh). We grouped 179 participants in the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study as follows: amyloid (–) [A(–)] and amyloid (+) [A(+)] groups based on the Aβ deposition degree; A-carrier (AC) and GG (GG) groups based on the presence/absence of the rs671 A allele; and their combinations, i.e., A(–)AC, A(–)GG, A(+)AC, and A(+)GG groups. A multiple regression analysis identified nine regions of interest. Compared with the A(–)GG group, the A(–)AC group showed thinner CTh in all regions. There were no significant differences between the A(+)AC and A(+)GG groups. We observed an interaction effect of amyloid deposition and rs671 polymorphism on CTh. The CTh in the A(–) group appeared to be strongly influenced by rs671 polymorphism, which could have contributed to cortical thinning and biogenic aldehyde accumulation in the AC group. Additionally, CTh in the A(+) group appeared to be strongly influenced by amyloid deposition.

Background Suicide has emerged as a serious concern for public health; however, only few studies have revealed the differences between major psychiatric disorders and suicide. Recent studies have attempted to quantify research domain criteria (RDoC) into numeric scores to systematically use them in computerized methods. The RDoC scores were used to reveal the characteristics of suicide and its association with major psychiatric disorders. Objective We intended to investigate the differences in the dimensional psychopathology among hospitalized suicidal patients and the association between the dimensional psychopathology of psychiatric disorders and length of hospital stay. Methods This retrospective study enrolled hospitalized suicidal patients diagnosed with major psychiatric disorders (depression, schizophrenia, and bipolar disorder) between January 2010 and December 2020 at a tertiary hospital in South Korea. The RDoC scores were calculated using the patients’ admission notes. To measure the differences between psychiatric disorder cohorts, analysis of variance and the Cochran Q test were conducted and post hoc analysis for RDoC domains was performed with the independent two-sample t test. A linear regression model was used to analyze the association between the RDoC scores and sociodemographic features and comorbidity index. To estimate the association between the RDoC scores and length of hospital stay, multiple logistic regression models were applied to each psychiatric disorder group. Results We retrieved 732 admissions for 571 patients (465 with depression, 73 with schizophrenia, and 33 with bipolar disorder). We found significant differences in the dimensional psychopathology according to the psychiatric disorders. The patient group with depression showed the highest negative RDoC domain scores. In the cognitive and social RDoC domains, the groups with schizophrenia and bipolar disorder scored higher than the group with depression. In the arousal RDoC domain, the depression and bipolar disorder groups scored higher than the group with schizophrenia. We identified significant associations between the RDoC scores and length of stay for the depression and bipolar disorder groups. The odds ratios (ORs) of the length of stay were increased because of the higher negative RDoC domain scores in the group with depression (OR 1.058, 95% CI 1.006-1.114) and decreased by higher arousal RDoC domain scores in the group with bipolar disorder (OR 0.537, 95% CI 0.285-0.815). Conclusions This study showed the association between the dimensional psychopathology of major psychiatric disorders related to suicide and the length of hospital stay and identified differences in the dimensional psychopathology of major psychiatric disorders. This may provide new perspectives for understanding suicidal patients.

Objective Motor, perceptual, and cognitive functions are known to affect driving competence. Subcortical ischemic changes on brain magnetic resonance imaging (MRI) can reflect reduction in cognitive and motor performance. However, few studies have reported the relationship between subcortical ischemic changes and driving competence of the elderly. Thus, the objective of this study was to investigate the association between subcortical ischemic changes on MRI and driving abilities of the elderly. Methods Participants (n=540) were drawn from a nationwide, multicenter, hospital-based, longitudinal cohort. Each participant underwent MRI scan and interview for driving capacity categorized into ‘now driving’ and ‘driving cessation (driven before, not driving now)’. Participants were divided into three groups (mild, n=389; moderate, n=116; and severe, n=35) depending on the degree of white matter hyperintensity (WMH) on MRI at baseline. Driving status was evaluated at follow-up. Statistical analyses were conducted using χ2 test, analysis of variance (ANOVA), structured equation model (SEM), and generalized estimating equation (GEE). Results In SEM, greater baseline degree of WMH was directly associated with driving cessation regardless of cognitive or motor dysfunction (β=-0.110, p

BACKGROUND Suicide has emerged as a serious concern for public health; however, only few studies have revealed the differences between major psychiatric disorders and suicide. Recent studies have attempted to quantify research domain criteria (RDoC) into numeric scores to systematically use them in computerized methods. The RDoC scores were used to reveal the characteristics of suicide and its association with major psychiatric disorders. OBJECTIVE We intended to investigate the differences in the dimensional psychopathology among hospitalized suicidal patients and the association between the dimensional psychopathology of psychiatric disorders and length of hospital stay. METHODS This retrospective study enrolled hospitalized suicidal patients diagnosed with major psychiatric disorders (depression, schizophrenia, and bipolar disorder) between January 2010 and December 2020 at a tertiary hospital in South Korea. The RDoC scores were calculated using the patients’ admission notes. To measure the differences between psychiatric disorder cohorts, analysis of variance and the Cochran Q test were conducted and post hoc analysis for RDoC domains was performed with the independent two-sample t test. A linear regression model was used to analyze the association between the RDoC scores and sociodemographic features and comorbidity index. To estimate the association between the RDoC scores and length of hospital stay, multiple logistic regression models were applied to each psychiatric disorder group. RESULTS We retrieved 732 admissions for 571 patients (465 with depression, 73 with schizophrenia, and 33 with bipolar disorder). We found significant differences in the dimensional psychopathology according to the psychiatric disorders. The patient group with depression showed the highest negative RDoC domain scores. In the cognitive and social RDoC domains, the groups with schizophrenia and bipolar disorder scored higher than the group with depression. In the arousal RDoC domain, the depression and bipolar disorder groups scored higher than the group with schizophrenia. We identified significant associations between the RDoC scores and length of stay for the depression and bipolar disorder groups. The odds ratios (ORs) of the length of stay were increased because of the higher negative RDoC domain scores in the group with depression (OR 1.058, 95% CI 1.006-1.114) and decreased by higher arousal RDoC domain scores in the group with bipolar disorder (OR 0.537, 95% CI 0.285-0.815). CONCLUSIONS This study showed the association between the dimensional psychopathology of major psychiatric disorders related to suicide and the length of hospital stay and identified differences in the dimensional psychopathology of major psychiatric disorders. This may provide new perspectives for understanding suicidal patients. CLINICALTRIAL

Background Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks. Objective The aim of this study was to impute missing values in data using a deep learning approach. Methods To develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning–based imputation model with the National Health and Nutrition Examination Survey data set and validated it with the external Korea National Health and Nutrition Examination Survey and the Korean Chronic Cerebrovascular Disease Oriented Biobank data sets which consist of daily records measuring activity counts. The partial root mean square error and partial mean absolute error of the imputed intervals (partial RMSE and partial MAE, respectively) were calculated using our deep learning–based imputation model (zero-inflated denoising convolutional autoencoder) as well as using other approaches (mean imputation, zero-inflated Poisson regression, and Bayesian regression). Results The zero-inflated denoising convolutional autoencoder exhibited a partial RMSE of 839.3 counts and partial MAE of 431.1 counts, whereas mean imputation achieved a partial RMSE of 1053.2 counts and partial MAE of 545.4 counts, the zero-inflated Poisson regression model achieved a partial RMSE of 1255.6 counts and partial MAE of 508.6 counts, and Bayesian regression achieved a partial RMSE of 924.5 counts and partial MAE of 605.8 counts. Conclusions Our deep learning–based imputation model performed better than the other methods when imputing missing values in actigraphy data.

Background The impact of the interrelationship between depression and cognitive impairment on risk of falls remains unclear. In addition, gender differences should be considered to further understand the relationships between depression, cognitive impairment, and risk of falls. Methods Older adults who completed the Living Profiles of Older People Survey in Korea were included. In total, 14,055 participants completed the baseline survey, and 7,150 participants completed a follow-up survey after 3 years. We classified participants into 4 groups based on depression and cognitive impairment. Results Using generalized estimating equation models, older adults with depression but without cognitive impairment had a higher risk of falls (OR=1.49, 95% CI=1.35–1.63), compared to a reference group with neither depression nor cognitive impairment. However, older adults who had cognitive impairment but not depression had a similar risk of falls (OR=1.03, 95% CI=0.91–1.16) to the reference group. Finally, older adults who had both depression and cognitive impairment had the highest risk of falls (OR=1.95, 95% CI=1.73–2.20) compared to the reference group. In addition, depression seemed to have a bigger effect on the risk of falls in men compared to women (p for interaction Limitations These results should be interpreted with caution, considering the use of screening tool for defining depression or cognitive impairment, and lack of information on psychotropic medication use. Conclusions Depression alone or depression combined with cognitive impairment was associated with higher risk of falls in older adults. In addition, gender differences in the risk of falls was noted.

Epigenetic dysregulation has been known to be involved in neurodegenerative diseases, including amnestic mild cognitive impairment (MCI). The aim of this study was to investigate the genome-wide DNA methylation analysis, in order to identify epigenetic dysregulation in blood from patients with MCI. Here, we investigated whether epigenetic dysregulation in MCI and whether such an aberration could be detected in blood circulation. Genome-wide bisulfite sequencing targeted 84 million bases covering 3.7 million CpG sites was comparatively analyzed in MCI and control groups. And correlation between DNA methylation and transcriptomic changes was sought. Significant differentially methylated regions (DMRs) distinguishing the MCI and control groups were identified and functionally annotated. Most DMRs specific to MCI were enriched between - 2 kb and + 2 kb of the CpG island start sites located within or near gene promoters. Representative hypo- and hypermethylated DMRs in MCI were confirmed to be correlated to mRNA expression changes with the comparative delta Ct method. DNA methylation aberrations involving metal ion homeostasis, axon growth, inflammasome, and others in this study may be less-invasive, easily measurable blood biomarker candidates for MCI.

BACKGROUND Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks. OBJECTIVE The aim of this study was to impute missing values in data using a deep learning approach. METHODS To develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning–based imputation model with the National Health and Nutrition Examination Survey data set and validated it with the external Korea National Health and Nutrition Examination Survey and the Korean Chronic Cerebrovascular Disease Oriented Biobank data sets which consist of daily records measuring activity counts. The partial root mean square error and partial mean absolute error of the imputed intervals (partial RMSE and partial MAE, respectively) were calculated using our deep learning–based imputation model (zero-inflated denoising convolutional autoencoder) as well as using other approaches (mean imputation, zero-inflated Poisson regression, and Bayesian regression). RESULTS The zero-inflated denoising convolutional autoencoder exhibited a partial RMSE of 839.3 counts and partial MAE of 431.1 counts, whereas mean imputation achieved a partial RMSE of 1053.2 counts and partial MAE of 545.4 counts, the zero-inflated Poisson regression model achieved a partial RMSE of 1255.6 counts and partial MAE of 508.6 counts, and Bayesian regression achieved a partial RMSE of 924.5 counts and partial MAE of 605.8 counts. CONCLUSIONS Our deep learning–based imputation model performed better than the other methods when imputing missing values in actigraphy data.

Background Children today are exposed to various media devices, and their usage of these is increasing. Prior studies have outlined forms of harm this can potentially cause. However, there has been little empirical research on the use of media devices among preschool children in Asia. The aim of this study was to examine and analyze longitudinal trends in media device use among Korean preschool children, focusing on the frequency of engagement, time spent with, and ownership of media devices, delineated by sex. Methods Four hundred parents of children aged 2–5 years were invited to enroll. The baseline assessment, Wave 1, was conducted between December 2015 and June 2016, and follow-up assessments, Wave 2 and Wave 3, were conducted annually for the following 2 years. Time of media use, frequency of media use, and ownership of media devices (TV, tablet PCs, and smartphones) were investigated. Results Ownership of tablet PCs increased significantly between Wave 1 and Wave 3 for boys and girls (corrected P < 0.001). Frequency of media use increased significantly between Wave 1 and Wave 3 only in boys' use of tablet PCs (mean difference 0.8 day/wk). Time of media use increased significantly between Wave 1 and Wave 3 for both sexes in all devices, measured by mean difference on weekdays and weekends (TV by 0.6 and 0.7 hr/day, tablet PCs by 0.6 and 0.8 hr/day, and smartphones by 0.4 and 0.4 hr/day). Children spent more time using media devices during weekends than on weekdays. Conclusion This study observed an increase in the tendency of media device use among preschool children in Korea. The patterns of use indicate that paying attention to the types of devices children use is needed, as well as vigilance on weekends., Graphical Abstract

Background Given that current unimodal strategies for treating late-life depression are insufficient, the awareness of the necessity and importance of multidomain intervention has increased. We assessed the efficacy of multidomain intervention in reducing symptoms of late-life depression. Methods This was a 12-week community-based randomized controlled trial in 78 older adults diagnosed with major depressive disorder. Participants were randomly assigned to the multidomain intervention or supportive therapy group. We provided four home visits and 12 telephone calls over 12 weeks. Four therapeutic approaches (physical activity, healthy diet, social activity, and brief cognitive restructuring) were incorporated into the multidomain intervention. The primary outcome was the change in depressive symptoms, as measured by the Montgomery–Asberg Depression Rating Scale (MADRS). Secondarily, we investigated changes in resting-state functional connectivity. Results The MADRS total score was reduced more in the multidomain intervention group than in the supportive therapy group during the 12 weeks (intervention × time interaction, P = =0.007). After correction for multiple comparisons, the multidomain intervention group exhibited a lower MADRS total score at week 12 (score difference 5.117; P = =0.029). At follow-up, the multidomain intervention group also exhibited less functional connectivity between the posterior cingulate cortex and left inferior parietal lobule within the default mode network (FDR Limitations Caution is needed in the interpretation of the results, considering the small sample size and high percentage of female participants. Conclusions A 12-week multidomain intervention resulted in a greater reduction of depressive symptoms among the elderly with major depressive disorder than their counterparts who received supportive therapy.

Background We compared the validity of models of subcortical ischemic depression (SID) and depression-executive dysfunction syndrome (DED) in predicting functional disability in the elderly. Methods We obtained data from elderly Korean subjects (n = 1356) aged 60 years or older at baseline from the CREDOS study from November 2005 to July 2014. A generalized estimating equation (GEE) model was constructed to measure functional disability using instrumental activity of daily living as a primary outcome. A risk factor of interest was SID and DED evaluated by a visual rating scale of deep white matter hyperintensity in MRI, Stroop test and Geriatric Depression Scale (GDS) score. Receiver-operating-characteristic plots and area under the curve (AUC) test were applied to examine the difference of the two definitions of vascular depression with predicted values of functional disability outcome. Results The mean (SD) follow-up duration of the participants was 1.7 (0.9) years. The GEE model showed that presence of SID at baseline predicted functional disability compared to non-depressed subjects (GDS score: Odds ratio [OR] 1.76; 95% CI 1.23, 2.53; p = 0.002). The association was also statistically significant among the DED group (OR 1.48; 95% CI 1.15, 1.92; p = 0.003). There were no significant differences in predicting functional disability (95% CI: −0.003 to 0.009, p = 0.366) according to AUC differences between SID and DED. Conclusions The results will be useful in evaluating the cardinal features of the vascular depression hypothesis in predicting functional disability.

Background In keeping with increasing interest in dementia, few recent studies suggest that clinical course of mild cognitive impairment vary across different studies with hospital-based subjects showing higher rates of conversion than community-based subjects. Objective The main objective of the present study was to assess whether the clinical conversion or reversion rates differ according to recruitment source. Methods The baseline study subjects comprised of patients who were diagnosed with mild cognitive impairment in community-based GDEMCIS or hospital-based CREDOS. The two studies had nearly the same protocol and were performed over a similar period. We used propensity score matching for baseline comparability. After that, Cox proportional hazards regression analyses were conducted to estimate the hazard ratios and 95% confidence intervals of clinical conversion or reversion. Results Based on 89 GDEMCIS subjects, 1 : 4 propensity score matching was conducted and 356 CREDOS subjects were selected. After adjusting for covariates including baseline demographics, comorbidity, depression, disability, and neuropsychological result, Cox proportional hazard regression analysis for time to clinical conversion indicated that recruitment from hospital-based CREDOS exhibited hazard ratio of 2.13 (95% CI, 1.08-4.21), as compared to recruitment from community-based GDEMCIS. Similarly, Cox proportional hazard regression analysis for time to reversion indicated that recruitment from hospital-based CREDOS exhibited hazard ratio of 0.34 (95% CI, 0.20-0.59), as compared to recruitment from community-based GDEMCIS. Conclusion The present study demonstrated that even after the matching process and adjustments for baseline covariates, recruitment source greatly affected the course of mild cognitive impairment.

Background We aimed to examine differential effects of WMH on progression of depressive symptoms according to APOE e4 status in the elderly. Methods We obtained data from elderly Korean subjects ( n =707) aged 60 years or older at baseline from the CREDOS study from November 2005 to July 2014. A linear mixed model stratified according to APOE genotype (APOE e4 carrier vs. non-carrier) was constructed using GDS score as a primary outcome and degree of overall, deep, periventricular WMH evaluated by a visual rating scale as a risk factor of interest. We also tested interaction between APOE e4, WMH and time as predictors of clinical progression on GDS scores to examine the moderating effect of APOE e4 allele on the relationship between degree of WMH and progression of geriatric depressive symptoms. Results The mean (SD) follow-up duration of the participants was 2.0 (0.8) years. Among APOE e4 carriers, a severe degree of overall and deep WMH, but not periventricular WMH, predicted progression of geriatric depressive symptoms (overall WMH: coefficient=0.96, p =0.010; deep WMH: 0.87, p =0.016). There were significant interaction between APOE e4, degree of WMH and time in predicting GDS increase (5d f , F =2.28, p =0.046). Limitations Only subjects seeking medical attention and with follow-up measurements were enrolled in this study. Specific location of WMH and use of antidepressant were uncontrolled. Conclusions Considering biological markers such as degree of WMH and APOE e4 status may be clinically relevant to predicting progression of geriatric depressive symptoms.

Background We sought to investigate the possible associations of rest-activity patterns with cortical amyloid burden, medial temporal lobe (MTL) neurodegeneration, and cognitive function in patients in the early stage of cognitive impairment. Methods Rest-activity patterns were assessed in 100 participants (70 with mild cognitive impairment and 30 with mild dementia) using wrist actigraphy. All participants underwent 18F-flutemetamol positron emission tomography (PET) imaging to quantify cortical amyloid burden, structural brain magnetic resonance imaging (MRI) to quantify MTL grey matter volume, neuropsychological testing, and clinical diagnosis. We used multiple linear regression models adjusted for covariates, including demographics, diabetes, hypertension, depressive symptom, psychotropic medication, sleep medication, weekend effect, and apolipoprotein-e allele status. Findings After adjusting for possible confounders, we found that the midline estimation of statistic of rhythm (MESOR) associated positively with frontal/executive function (estimate = 1.17, standard error [SE] = 0.37, p = 0.002). The least active 5-h (L5) onset time associated positively with MTL grey matter volume and memory function (estimate = 1.24, SE = 0.33, p = 0.001, and estimate = 3.77, SE = 1.22, p = 0.003, respectively), particularly in amyloid-negative participants. Additional path analysis revealed that MTL grey matter volume partially mediated the association between L5 onset time and memory function in amyloid-negative participants. Interpretation Decreased MESOR and advanced L5 onset time may be useful as early signs of cognitive decline or MTL neurodegeneration. Furthermore, amyloid pathology may act as a moderator of the relationships between rest-activity patterns, neurodegeneration, and cognitive function. Funding Korea Centres for Disease Control and Prevention (#4845–303); National Research Foundation of Korea (2019M3C7A1031905, 2019R1A5A2026045).

Our study aimed to assess the longitudinal association of frequency of contact with non-cohabitating adult children and risk of depression in the elderly.Elderly aged ≥60 years were included from Living Profiles of Older People Survey (LPOPS) in Korea. The baseline assessment, Wave 1, was conducted in 2008, and follow-up assessment, Wave 2, was conducted in 2011. We included participants who completed both waves and excluded those who met the following criteria: no adult children, living with adult children, cognitive impairment at either waves, and depression at baseline (n=4398). We defined infrequent contact as1 time per month face-to-face contact or1 time per week phone contact and classified participants into four groups based on contact method and frequency. Depression was measured using the 15-item geriatric depression scales (SGDS-K).In multivariable logistic regression analysis, infrequent face-to-face and phone contact group had adjusted odds ratio (OR) of 1.86 (95% CI, 1.44-2.42) when compared with frequent face-to-face and phone contact group. Frequent face-to-face contact with infrequent phone contact group and infrequent face-to-face contact with frequent phone contact group had adjusted OR of 1.49 (95% CI, 1.12-1.98) and 1.44 (95% CI, 1.15-1.80), respectively, when compared with frequent face-to-face and phone contact group.These results propose that the risk of subsequent depression in elderly is associated with frequency of contact with non-cohabitating adult children. Moreover, the efficacy of face-to-face contact and that of phone contact were similar, while the group lacking both types of contact demonstrated the highest risk of depression.

OBJECTIVE The objective of this study was to compare the effectiveness of usual care management (UCM) and a newly-developed lifestyle modification with contingency management (LMCM) for geriatric depressive symptoms in the community. METHODS A randomized controlled trial was conducted in 93 older adults with major depressive disorder at community mental health centers. A 12 week multi-domain LMCM was developed by providing positive reinforcement using 'gold medal stickers' as a symbolic incentive to motivate their participation and adherence. Participants were allocated to LMCM (n=47) and UCM (n=46) groups. They were then subjected to the 12 week treatment. Effects of the two intervention methods on Geriatric Depression Scale were determined using mixed model analysis. RESULTS Participants in the LMCM group had greater decline in GDS score per month than participants in the UCM group after adjusting for age, sex, years of education, living alone, and MMSE scores at baseline examination [coefficient for GDS score (95% CI): -1.08 (-1.51, -0.65), p

Background and Aims Heat shock proteins (HSPs) have been regarded as cytoprotectants that protect brain cells during the progression of neurodegenerative diseases and from damage resulting from cerebral ischemia. In this study, we assessed the association between plasma HSP 70/27 levels and cognitive decline. Methods Among participants in the community-based cohort study of dementia called the Gwangju Dementia and Mild Cognitive Impairment Study, subjects without cognitive impairment at baseline, who then either remained without impairment (non-conversion group), or suffered mild cognitive impairment (MCI) (conversion group) (non-conversion group, N = 36; conversion group, N = 30) were analyzed. Results After a five to six year follow-up period, comparison of the plasma HSP 70 and HSP 27 levels of the two groups revealed that only the plasma HSP 70 level was associated with a conversion to MCI after adjustments for age, gender, years of education, follow-up duration, APOE e4, hypertension, and diabetes (repeated measure analysis of variance: F = 7.59, p = 0.008). Furthermore, an increase in plasma HSP 70 level was associated with cognitive decline in language and executive function (linear mixed model: Korean Boston Naming Test, -0.426 [-0.781, -0.071], p = 0.019; Controlled Oral Word Association Test, -0.176 [-0.328, -0.023], p = 0.024; Stroop Test, -0.304 [-0.458, -0.150], p