Many previous studies have shown that the APOE e4 genotype affects cognition, brain volume, glucose metabolism and amyloid deposition. However, these studies were conducted separately, and few studies simultaneously investigated the effects of the APOE e4 genotype on cognition, brain volume, glucose metabolism and amyloid deposition in Alzheimer disease (AD). The purpose of this study is to simultaneously investigate the association of the APOE e4 genotype with cognition, brain volume, glucose metabolism and amyloid deposition in patients with AD.

This is a cross-sectional study of 69 subjects with Alzheimer’s disease (AD). All subjects were divided into carriers and noncarriers of the ε4 allele. Forty APOE ε4 carriers and 29 APOE ε4 non-carriers underwent neuropsychological, structural magnetic resonance imaging, 18F-fluorodeoxyglucose positron emission tomography scans (18F-FDG-PET) and 18F-Florbetaben amyloid positron emission tomography scans (amyloid PET). Analysis of covariance (ANCOVA) was conducted to compare the differences on cognition, brain volume, glucose metabolism and amyloid deposition between APOE ε4 carriers and non-carriers after controlling demographics.

APOE ε4 carriers had 50% lower scores of SVLT_delayed recall compared to non-carriers (0.88 ± 1.65 vs 1.76 ± 1.75). However, APOE ε4 carriers performed better on other cognitive tests than non- carriers (K-BNT (11.04 ± 2.55 vs 9.66 ± 2.82), RCFT (25.73 ± 8.56 vs 20.15 ± 10.82), and Stroop test_color response (48.28 ± 26.33 vs 31.56 ± 27.03)). APOE ε4 carriers had slightly smaller hippocampal volume than non-carriers (3.09 ± 0.38 vs 3.32 ± 0.38), but greater total brain cortical thickness (1.45 ± 1.55 vs 1.37 ± 1.24).

We found that APOE e4 genotype is associated with cognition, brain volume in AD, suggesting that APOE e4 genotype can play a very important role in the underlying pathogenesis of AD.

Previous studies investigating neuropsychological profiles of cognitive impairment people have found a learning curve can be a useful indicator of AD diagnosis or progression. However, the data on the relationship between amyloid β (Aβ) deposition status and the learning curve in amnestic mild cognitive impairment (aMCI) are limited. In this study, we investigate the role of the learning curve in predicting Aβ deposition status in patients with aMCI.

This is a cross-sectional study of 67 aMCI patients (N = 67; 33 aMCI with amyloid positive (Aβ-PET (+)), and 34 aMCI with amyloid negative (Aβ-PET (-))). All participants underwent Seoul Neuropsychological Screening Battery for a comprehensive neuropsychological test battery and brain MRI. To determine Aβ deposition status, each participant underwent amyloid PET scans using 18F-florbetaben. The learning curve was obtained using immediate recall of Seoul Verbal Learning Test-learning curve (SVLT- learning curve). The association of cognitive test scores and dichotomized Aβ deposition status was examined using logistic regression models in patients with aMCI. Receiver operating characteristic (ROC) curves were used to examine the predictive ability of cognitive test to detect Aβ deposition status in aMCI.

Logistic regression models showed that SVLT-learning curve and Rey Complex Figure Test- delayed recall (RCFT-delayed recall) scores were significantly associated with Aβ deposition status. In ROC analysis to assess the predictive power, SVLT-learning curve (area under the curve (AUC) = 0.734, P = 0.001) and RCFT-delayed recall (AUC = 0.739, P = 0.001) independently discriminated Aβ-PET (+) and Aβ-PET (-). The combination of these clinical markers (SVLT-learning curve and RCFT-delayed recall) improved the predictive accuracy of Aβ-PET (+) (AUC = 0.833, P < 0.001).

Our findings of association of Aβ deposition status with SVLT-learning curve and RCFT- delayed recall suggest that these cognitive tests could be a useful screening tool for Aβ deposition status among aMCI patients in resource-limited clinics.

The COVID-19 pandemic poses a major threat to mental health and is associated with an increased risk of suicide. An understanding of suicidal behaviours during the pandemic is necessary for establishing policies to prevent suicides in such social conditions.

We aimed to investigate vulnerable individuals and the characteristics of changes in suicidal behaviour during the COVID-19 pandemic.

We retrospectively reviewed the medical records of patients with suicide attempts who visited the emergency department from February 2019 to January 2021. We analysed the demographic and clinical characteristics, risk factors and rescue factors of patients, and compared the findings between the pre-pandemic and pandemic periods.

In total, 519 patients were included. During the pre-pandemic and pandemic periods, 303 and 270 patients visited the emergency department after a suicide attempt, respectively. The proportion of suicide attempts by women (60.1% v. 69.3%, P = 0.035) and patients with a previous psychiatric illness (63.4% v. 72.9%, P = 0.006) increased during the COVID-19 pandemic. In addition, patients’ rescue scores during the pandemic were lower than those during the pre-pandemic period (12 (interquartile range: 11–13) v. 13 (interquartile range: 12–14), P < 0.001).

Women and people with previous psychiatric illnesses were more vulnerable to suicide attempts during the COVID-19 pandemic. Suicide prevention policies, such as continuous monitoring and staying in touch with vulnerable individuals, are necessary to cope with suicide risk.

Prognostic heterogeneity in early psychosis patients yields significant difficulties in determining the degree and duration of early intervention; this heterogeneity highlights the need for prognostic biomarkers. Although mismatch negativity (MMN) has been widely studied across early phases of psychotic disorders, its potential as a common prognostic biomarker in early periods, such as clinical high risk (CHR) for psychosis and first-episode psychosis (FEP), has not been fully studied.

A total of 104 FEP patients, 102 CHR individuals, and 107 healthy controls (HCs) participated in baseline MMN recording. Clinical outcomes were assessed; 17 FEP patients were treatment resistant, 73 FEP patients were nonresistant, 56 CHR individuals were nonremitters (15 transitioned to a psychotic disorder), and 22 CHR subjects were remitters. Baseline MMN amplitudes were compared across clinical outcome groups and tested for utility prognostic biomarkers using binary logistic regression.

MMN amplitudes were greatest in HCs, intermediate in CHR subjects, and smallest in FEP patients. In the clinical outcome groups, MMN amplitudes were reduced from the baseline in both FEP and CHR patients with poor prognostic trajectories. Reduced baseline MMN amplitudes were a significant predictor of later treatment resistance in FEP patients [Exp(β) = 2.100, 95% confidence interval (CI) 1.104–3.993, p = 0.024] and nonremission in CHR individuals [Exp(β) = 1.898, 95% CI 1.065–3.374, p = 0.030].

These findings suggest that MMN could be used as a common prognostic biomarker across early psychosis periods, which will aid clinical decisions for early intervention.

Functional impairment in daily activity is a cornerstone in distinguishing the clinical progression of dementia. Multiple indicators based on neuroimaging and neuropsychological instruments are used to assess the levels of impairment and disease severity; however, it remains unclear how multivariate patterns of predictors uniquely predict the functional ability and how the relative importance of various predictors differs.

In this study, 881 older adults with subjective cognitive complaints, mild cognitive impairment (MCI), and dementia with Alzheimer’s type completed brain structural magnetic resonance imaging (MRI), neuropsychological assessment, and a survey of instrumental activities of daily living (IADL). We utilized the partial least square (PLS) method to identify latent components that are predictive of IADL.

The result showed distinct brain components (gray matter density of cerebellar, medial temporal, subcortical, limbic, and default network regions) and cognitive–behavioral components (general cognitive abilities, processing speed, and executive function, episodic memory, and neuropsychiatric symptoms) were predictive of IADL. Subsequent path analysis showed that the effect of brain structural components on IADL was largely mediated by cognitive and behavioral components. When comparing hierarchical regression models, the brain structural measures minimally added the explanatory power of cognitive and behavioral measures on IADL.

Our finding suggests that cerebellar structure and orbitofrontal cortex, alongside with medial temporal lobe, play an important role in the maintenance of functional status in older adults with or without dementia. Moreover, the significance of brain structural volume affects real-life functional activities via disruptions in multiple cognitive and behavioral functions.

Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years.

Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis.

The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels.

Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.

Obsession and delusion are theoretically distinct from each other in terms of reality testing. Despite such phenomenological distinction, no extant studies have examined the identification of common and distinct neural correlates of obsession and delusion by employing biologically grounded methods. Here, we investigated dimensional effects of obsession and delusion spanning across the traditional diagnostic boundaries reflected upon the resting-state functional connectivity (RSFC) using connectome-wide association studies (CWAS).

Our study sample comprised of 96 patients with obsessive–compulsive disorder, 75 patients with schizophrenia, and 65 healthy controls. A connectome-wide analysis was conducted to examine the relationship between obsession and delusion severity and RFSC using multivariate distance-based matrix regression.

Obsession was associated with the supplementary motor area, precentral gyrus, and superior parietal lobule, while delusion was associated with the precuneus. Follow-up seed-based RSFC and modularity analyses revealed that obsession was related to aberrant inter-network connectivity strength. Additional inter-network analyses demonstrated the association between obsession severity and inter-network connectivity between the frontoparietal control network and the dorsal attention network.

Our CWAS study based on the Research Domain Criteria (RDoC) provides novel evidence for the circuit-level functional dysconnectivity associated with obsession and delusion severity across diagnostic boundaries. Further refinement and accumulation of biomarkers from studies embedded within the RDoC framework would provide useful information in treating individuals who have some obsession or delusion symptoms but cannot be identified by the category of clinical symptoms alone.