Objectives: ：This study was aimed to investigate whether 1) sexual dimorphism in subcortical gray matter volumes (GMV) and the length ratio between the second and fourth digits (2D:4D) would be found and 2) 2D:4D would have associated with subcortical GMV in healthy elderly people. Methods: ：Sixty-two females aged 70.3±6.3 (mean±SD) years and 23 males aged 70.4±4.9 years were recruited from the Dementia Clinic in the Pusan National University Hospital. The subjects with the clinical dementia rating scale-sum of boxes (CDR-SB) total score greater than 2.0, any psychiatric or neurological disease, or any pathologic lesion on brain MRI other than micro-angiopathy were excluded. The 2D:4D of the left and right hands were measured 3 times each. Volumetric segmentation of T1-weighted MRI scans was done by Freesurfer software (v7.1.1.1). Results: ：2D:4Ds of males were smaller than those of females significantly on repeated measures ANOVA. The males’ thalamus, putamen, hippocampus in both hemispheres and the right amygdala were larger than females’. These differences were not significant after controlling for age, education and total intracranial volume (ICV). In the females, the left 2D:4D was negatively correlated with the left hippocampal volume. In the males, 2D:4D was positively correlated with the volumes of ipsilateral or contralateral thalamus, hippocampus, amygdala and accumbens. These correlations were not significant after Bonferroni’s correction, except for the right accumbens. Conclusions ：Sexual dimorphism of 2D:4D is preserved in healthy elderly people. There is a significant correlation between the right 2D:4D and GMV of the accumbens in males.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: Although maintenance treatment for mood disorders is important, the treatment discontinuation rate is reported to be high. This study aimed to investigate the dropout rates and associated factors in mood disorders. Methods: The patients in a mood disorder clinic (n = 535) were examined. Demographic and clinical factors, scores of psychometric scales, time to dropout from initial treatment in patients with bipolar disorder (BP) (n = 288) and depressive disorder (DD) (n = 143) were evaluated based on database of the mood disorder clinic. Results: Among the studied patients with BP and DD, 50% showed dropout in 4.05 and 2.17 years, respectively. The mean survival times were 8.90 years in bipolar disorder I (BP-I), 5.19 years in bipolar II disorder, 3.22 years in bipolar disorder not otherwise specified, 4.24 years in major depressive disorder, and 4.03 years in other depressive disorders.In the multivariate Cox proportional hazards regression model in the BP group, diagnosis BP-I was found to be significantly related to the decrease in dropout rate (hazard ratio [HR] = 0.22, p = 0.001); however, increased past suicide attempt number was significantly related to the increase in dropout rate (HR = 1.13, p = 0.017). In the DD group, none of anxiety disorders as comorbidity, increased scores of openness, and extraversion personality were related to the increase in dropout rate. Conclusion Patients with BP, especially BP-I, showed a lower dropout rate as compared to patients with other mood disorders.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: Due to the high frequency of depressive symptoms associated with breast cancer, it is crucial to screen for depression in breast cancer patients. While numerous screening tools are available for depression in this population, there is a need for a brief and convenient tool to enhance clinical use. This study aims to investigate the psychometric properties of the Patient Health Questionnaire-9 (PHQ-9) in patients with breast cancer. Methods: Patients with breast cancer (n=327) who visited the Breast Cancer Clinic were included in this study. The reliability of the PHQ-9 was analyzed by Cronbach’s α, and the construct validity of the PHQ-9 was explored by factor analysis. The concurrent validity of the PHQ-9 was evaluated by Pearson correlation analysis with the Hospital Anxiety and Depression Scale (HADS) and Perceived Stress Scale (PSS). Results: The values of Cronbach’s α ranged from 0.800 to 0.879 was acceptable. The exploratory factor analysis revealed that the one-factor model and two-factor model of the PHQ-9 explained 46% and 57% of the variance, respectively. The PHQ-9 were significantly correlated with those of HADS (r=0.702, p<0.001) and PSS (r=0.466, p<0.001). Consequently, the PHQ-9 demonstrated acceptable reliability and validity in breast cancer patients. Conclusion The findings of this study indicate that the PHQ-9 exhibits acceptable reliability and validity in patients with breast cancer. The convenience of this brief self-report questionnaire suggests its potential as a reliable and valid tool for assessing depression in breast cancer clinics.

Objective: Given the long-term and severe distress experienced during breast cancer treatment, detecting depression among breast cancer patients is clinically crucial. This study aimed to explore a machine-learning model using self-report questionnaires to screen for depression in patients with breast cancer. Methods: A total of 327 patients who visited the breast cancer clinic were included in this study. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9), Beck Depression Inventory (BDI), and Hospital Anxiety and Depression Scale (HADS). The depression was evaluated according to the Diagnostic and Statistical Manual of Mental Disorders 5th edition. The prediction model’s performance based on supervised machine learning was conducted using MATLAB2022. Results: The BDI showed an area under the curve (AUC) of 0.785 when using the logistic regression (LR) classifier.The HADS and PHQ-9 showed an AUC of 0.784 and 0.756 when using the linear discriminant analysis, respectively.The combinations of BDI and HADS showed an AUC of 0.812 when using the LR. The combinations of PHQ-9, BDI, and HADS showed an AUC of 0.807 when using LR. Conclusion The combination model with BDI and HADS in breast cancer patients might be better than the method using a single scale. In future studies, it is necessary to explore strategies that can improve the performance of the model by integrating the method using questionnaires and other methods.

Objective: This study examined the volume of choroid plexus across AD without delusion (AD-D), AD with paranoid delusion (AD+PD), and AD with misidentification delusion (AD+MD). Methods: This is a hospital based cross-sectional study of patients with AD. The main outcome measure is the volume of choroid plexus that were measured as regions of interest with magnetic resonance imaging and the FreeSurfer analysis at baseline. Analysis of covariance (ANCOVA) was conducted to compare the differences on the volume of choroid plexus across AD-D, AD+PD, and AD+MD after controlling demographics. Results: There was no volume difference in the both choroid plexus between AD-D and AD+D. However, the volumes of both cho-roid plexus were significantly reduced in AD+MD compared to AD+PD. Conclusion Our study demonstrates that AD+MD has significantly reduced volumes of choroid plexus compared to AD+PD. These findings suggest that AD+MD and AD+PD may have different pathophysiological mechanisms related to neuroimmune re-sponses in the choroid plexus.

Objective: Although previous studies have shown association between anterior corpus callosum (ACC) and various psychotic disorder, the effect of ACC on development on psychotic symptoms in Alzheimer’s disease (AD) is still unclear. The purpose of this study is to investigate the association of ACC with the development of psychosis in patients with AD. Methods: This is a hospital based cross-sectional study of 241 AD patients. The main outcome measure is the volume of ACC that were measured as regions of interest with magnetic resonance imaging and the FreeSurfer analysis at baseline. Analysis of covariance and Logistic regression analysis conducted to assess the association between the volume of ACC and the presence of psychosis in AD, adjusting for age, education, Clinical Dementia Rating-Sum of Boxes, and total intracranial volume. Results: We found that the volume of ACC is significantly reduced in AD with psychosis (AD+P) compared to AD without psychosis (AD-P) (774.27±142.96 vs. 833.09±142.04, p=0.005). The volume of ACC associated with the presence of psychosis in AD (odds ratio=0.995; 95% confidence interval=0.993-0.997; p=0.006). Conclusion We have found that reduced volume of ACC in AD+P, suggesting that ACC might play an important role in the underlying pathogenesis of development of psychotic symptoms in AD.

Objective: As coping strategies can influence the illness course of mood disorder, they could be potential targets for psychological intervention. The current study investigated the similarities and differences in stress coping styles between bipolar disorder (BD) and depressive disorder (DD). Methods: Subjects with BD (n=135) and DD (n=100) who met the DSM-IV diagnostic criteria were included in this analysis. Coping strategies were assessed using the coping inventory for stressful situations and depressive symptoms were assessed by Beck depression inventory. Results: The BD group showed significantly more avoidant and task-oriented coping than the DD group (t=2.714, p=0.007; t=2.193, p=0.039). After excluding the effect of the depressive symptoms themselves (by comparing two groups in non-depressive state), the BD group still showed significantly more avoidant and task-oriented coping than the DD group (t=2.040, p=0.045; t=2.556, p=0.013), but when the symptoms of depression get greater, the difference between BD and DD coping strategies were reduced. Conclusion Subjects with BD tend to use more task and avoidant coping than DD subjects. But when the symptoms of depression get greater, the difference in coping strategies between BD and DD were reduced.