The Montreal Cognitive Assessment (MoCA) is known to have discriminative power for patients with Mild Cognitive Impairment (MCI). Recently Cognitive Reserve (CR) has been introduced as a factor that compensates cognitive decline. We aimed to assess whether the MoCA reflects CR. Furthermore, we assessed whether there were any differences in the efficacy between the MoCA and the Mini-Mental State Examination (MMSE) in reflecting CR. MoCA, MMSE, and the Cognitive Reserve Index questionnaire (CRIq) were administered to 221 healthy participants. Normative data and associated factors of the MoCA were identified. Correlation and regression analyses of the MoCA, MMSE and CRIq scores were performed, and the MoCA score was compared with the MMSE score to evaluate the degree to which the MoCA reflected CR. The MoCA reflected total CRIq score (CRI; B = 0.076, P < 0.001), CRI-Education (B = 0.066, P < 0.001), and CRI-Working activity (B = 0.025, P = 0.042), while MMSE reflected total CRI (B = 0.044, P < 0.001) and CRI-Education (B = 0.049, P < 0.001) only. The MoCA differed from the MMSE in the reflection of total CRI (Z = 2.30). In this study, we show that the MoCA score reflects CR more sensitively than the MMSE score. Therefore, we suggest that MoCA can be used to assess CR and early cognitive decline.

We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive–compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.

This study aimed to reveal the relationship between life activities and cognitive function and to evaluate the interaction between education and various leisure activities in predicting cognitive function. Using a cross-sectional research design with retrospective data, a total of 210 healthy Korean older adults participated and reported their years of education, working, and lifelong leisure activities. Cognitive function was measured using the Mini Mental State Examination. A hierarchical multiple regression analysis showed that education was positively associated with cognitive function, whereas working activity was not. Craft activities positively predicted cognitive function. Furthermore, education moderated the relationship between leisure activities and cognitive function. Only low-educated participants showed a decrease in cognitive function as they performed domestic chores and an increase in cognitive function as they participated in social activities and volunteering. High-educated participants showed no relation between leisure activities and cognitive function. The results of the current study suggest that the relationship between various leisure activities and cognitive function can vary based on the nature of the leisure activity and educational level. Professionals examining older adults’ cognitive function should pay closer attention to educational level, as well as life styles (i.e. leisure activities), to provide appropriate interventions.

Introduction: Attention-deficit/hyperactivity disorder (ADHD) affects a significant proportion of the pediatric population, making early detection crucial for effective intervention. Eye movements are controlled by brain regions associated with neuropsychological functions, such as selective attention, response inhibition, and working memory, and their deficits are related to the core characteristics of ADHD. Herein, we aimed to develop a screening model for ADHD using machine learning (ML) and eye-tracking features from tasks that reflect neuropsychological deficits in ADHD.Methods: Fifty-six children (mean age 8.38 ± 1.58, 45 males) diagnosed with ADHD based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition were recruited along with seventy-nine typically developing children (TDC) (mean age 8.80 ± 1.82, 33 males). Eye-tracking data were collected using a digital device during the performance of five behavioral tasks measuring selective attention, working memory, and response inhibition (pro-saccade task, anti-saccade task, memory-guided saccade task, change detection task, and Stroop task). ML was employed to select relevant eye-tracking features for ADHD, and to subsequently construct an optimal model classifying ADHD from TDC.Results: We identified 33 eye-tracking features in the five tasks with the potential to distinguish children with ADHD from TDC. Participants with ADHD showed increased saccade latency and degree, and shorter fixation time in eye-tracking tasks. A soft voting model integrating extra tree and random forest classifiers demonstrated high accuracy (76.3%) at identifying ADHD using eye-tracking features alone. A comparison of the model using only eye-tracking features with models using the Advanced Test of Attention or Stroop test showed no significant difference in the area under the curve (AUC) (p = 0.419 and p=0.235, respectively). Combining demographic, behavioral, and clinical data with eye-tracking features improved accuracy, but did not significantly alter the AUC (p=0.208).Discussion: Our study suggests that eye-tracking features hold promise as ADHD screening tools, even when obtained using a simple digital device. The current findings emphasize that eye-tracking features could be reliable indicators of impaired neurobiological functioning in individuals with ADHD. To enhance utility as a screening tool, future research should be conducted with a larger sample of participants with a more balanced gender ratio.

A close link has been established between self-harm and suicide risk in adolescents, and increasing attention is given to social media as possibly involved in this relationship. It is important to identify indicators of suicidality (i.e., suicide ideation or attempt) including aspects related to contagion in online and offline social networks and explore the role of social media in the relationship between social circumstances and suicidality in young adolescents with self-harm. This study explored characteristics of Korean adolescents with a recent history of self-harm and identified how behavioral and social features explain lifetime suicidality with emphasis on the impact of social media. Data came from a nationwide online survey among sixth- to ninth-graders with self-harm during the past 12 months (n = 906). We used χ² tests of independence to explore potential concomitants of lifetime suicidality and employed a multivariate logistic regression model to examine the relationship between the explanatory variables and suicidality. Sensitivity analyses were performed with lifetime suicide attempt in place of lifetime suicidality. 33.9% (n = 306) and 71.2% (n = 642) reported to have started self-harm by the time they were fourth- and six-graders, respectively; 44.3% (n = 400) reported that they have friends who self-harm. Having endorsed moderate/severe forms and multiple forms of self-harm (OR 5.36, p < 0.001; OR 3.13, p < 0.001), having engaged in self-harm for two years or more (OR 2.42, p = 0.001), having friends who self-harm (OR 1.92, p = 0.013), and having been bullied at school were associated with an increased odds of lifetime suicidality (OR 2.08, p = 0.004). Notably, having posted content about one’s self-harm on social media during the past 12 months was associated with an increased odds of lifetime suicidality (OR 3.15, p < 0.001), whereas having seen related content in the same period was not. Sensitivity analyses yielded similar results with lifetime suicide attempt, supporting our findings from the logistic regression. The current study suggests that self-harm may be prevalent from early adolescence in South Korea with assortative gathering. The relationship of vulnerable adolescents’ social circumstances to suicide risk may be compounded by the role of social media. As the role of social media can be linked to both risk (i.e., contagion) and benefit (i.e., social connection and support), pre-existing vulnerabilities alongside SH and what online communication centers on should be a focus of clinical attention.

Non-suicidal self-injury (NSSI) among adolescents continues to be a significant public health concern worldwide. A recent systematic review and meta-analysis found that the global prevalence of NSSI in adolescents aged 12–18 years was 17.2%, with higher rates reported among females (19.7%) than males (14.8%). This behavior has been linked to several negative outcomes, such as depression, anxiety, substance abuse, and suicidal ideation. The present study aimed to classify adolescents based on intrapersonal and interpersonal factors associated with NSSI proposed in Nock’s (2009) integrated model of NSSI, to identify distinct clusters targeting specific risk factors. This encompassed negative cognition, emotional vulnerability, poor coping skill, peer-victimization, family adaptability, and perceived stress. A total of 881 adolescents aged 11–16 years in South Korea completed self-reported questionnaires on automatic thoughts, depression, emotional regulation, peer victimization, family adaptability and perceived stress. Latent profile analysis (LPA) revealed three distinct classes: “the severe group”, “the moderate group”, “the mild group”. Class 3 (“severe group”: N = 127) exhibited greater severity related to NSSI, including negative cognition, emotional vulnerability, poor coping skills, peer victimization, and perceived stress, with weaker levels of factors that can prevent NSSI compared to class 1 (“mild group”: N = 416) and class 2 (“moderated group”: N = 338). The present study emphasizes the importance of considering both intrapersonal (e.g., negative automatic thoughts & emotional dysregulation) and interpersonal factors (i.e., peer victimization) when understanding NSSI - among adolescents. These findings can be utilized to develop interventions aimed at reducing the prevalence and severity of NSSI among adolescents.