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: 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: 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: 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: Categorical syndrome such as schizophrenia could be the complex of many continuous mental structure phenotypes including several personality development/degeneration dimensions. This is the study to search heritability and familiality of MMPI personality dimensions in the Korean schizophrenic LD (Linkage Disequilibrium) families. METHODS: We have recruited 204 probands (with schizophrenia) with their parents and siblings whenever possible. We have used MMPI questionnaires for measuring personality and symptomatic dimensions. Heritabilities of personality dimensions in total 543 family members were estimated using Sequential Oligogenic Linkage Analysis Routines (SOLAR). Personality dimensions in total family members were compared with those in 307 healthy unrelated controls for measuring the familialities using ANOVA analysis. RESULTS: Seven of the 10 MMPI variables were significantly heritable and were included in the subsequent analyses. The three groups (control, unaffected 1st degree relative, case) were found to be significantly different with the expected order of average group scores for all heritable dimensions. CONCLUSION: Our results show that the aberrations in several personality dimensions could form the complexity of schizophrenic syndrome as a result of genetic-environment coactions or interactions in spite of some limitations (recruited family, phenotyping).
Humans ; MMPI* ; Parents ; Phenotype ; Schizophrenia* ; Siblings

OBJECTIVE: A popular design for the investigation of such effects, including effects of parent-of-origin (imprinting), maternal genotype, and maternal-fetal genotype interactions, is to collect deoxyribonucleic acid (DNA) from affected offspring and their mothers and to compare with an appropriate control sample. We investigate the effects of estimation of maternal, imprinting and interaction effects using multimodal modeling using parents and their offspring with schizophrenia in Korean population. METHODS: We have recruited 27 probands (with schizophrenia) with their parents and siblings whenever possible. We analyzed 20 SNPs of 7 neuronal genes in chromosome 18. We used EMIM analysis program for the estimation of maternal, imprinting and interaction effects using multimodal modeling. RESULTS: Of analyzed 20 single nucleotide polymorphisms (SNPs), significant SNP (rs 2276186) was suggested in EMIM analysis for child genetics effects (p=0.0225438044) and child genetic effects allowing for maternal genetic effects (p=0.0209453210) with very stringent multiple comparison Bonferroni correction. CONCLUSION: Our results are the pilot study for epigenetic study in mental disorder and help to understanding and use of EMIM statistical genetics analysis program with many limitations including small pedigree numbers.
Child ; Chromosomes, Human, Pair 18 ; DNA ; Epigenomics ; Genetics ; Genotype ; Humans ; Linear Models ; Mental Disorders ; Mothers ; Neurons ; Parents ; Pedigree ; Pilot Projects ; Polymorphism, Single Nucleotide ; Schizophrenia ; Siblings

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.