Over the past decade, practice of sharing brain magnetic resonance imaging (MRI) data is increasing given significance of reproducibility and transparency in human neuroscience. Larger multimodal brain MRI databases are needed for more robust research findings considering potential possibilities of large variability in human neuroscience. There are currently more than tens of thousands of shared brain MRI datasets across multiple conditions and hundreds of neuroimaging studies using multimodality through shared brain MRI data repositories. This article critically reviews aims, procedures, and current state of brain MRI data sharing. This review focuses on projects and research findings using structural and functional MRI open databases and is further divided into T1- and diffusion-weighted images for structural MRI as well as resting-state and task-based functional MRI. The challenges and directions are finally discussed. Advances in brain MRI data sharing will lead to more rapid progression in human neuroscience by fostering effective longitudinal, multi-site, multimodal neuroimaging research.
Brain ; Dataset ; Foster Home Care ; Humans ; Information Dissemination ; Magnetic Resonance Imaging ; Neuroimaging ; Neurosciences ; Transcutaneous Electric Nerve Stimulation

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

OBJECTIVES: We aimed to identify the neuroimaging marker for prediction of the use of atypical antipsychotics (AAP) in dementia patients. METHODS: From April 2010 to March 2013, 31 patients who were diagnosed as dementia at the psychiatric department of Soonchunhyang University Hospital, completed the brain magnetic resonance imaging scan and cognitive test for dementia. Ten patients were treated with AAP for the improvement of behavioral and psychological symptoms of dementia (BPSD) and the other 21patients were not. Using T1 weighted and Fluid Attenuated Inversion Recovery (FLAIR) images of brain, areas of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and white matter hyperintensities (WMH) have been segmented and measured. Multivariate logistic regression models were applied for assessment of association between AAP use and the GM/WM ratio, the WMH/whole brain (GM + WM + CSF) ratio. RESULTS: There was a significant association between AAP use and the GM/WM ratio (odds ratio, OR = 1.18, 95% confidence interval, CI 1.01-1.38, p = 0.037), while there was no association between AAP use and the WMH/whole brain ratio (OR = 0.82, 95% CI 0.27-2.48, p = 0.73). CONCLUSIONS: The GM/WM ratio could be a biological marker for the prediction of AAP use and BPSD in patients with dementia. It was more likely to increase as dementia progress since atrophy of WM was more prominent than that of GM over aging.
Aging ; Antipsychotic Agents* ; Atrophy ; Biomarkers ; Brain* ; Cerebrospinal Fluid ; Dementia* ; Humans ; Logistic Models ; Magnetic Resonance Imaging* ; Neuroimaging

Objectives: Assessing the risks of youth suicide in educational and clinical settings is crucial.Therefore, this study developed a machine learning model to predict suicide attempts using the Korean Youth Risk Behavior Web-based Survey (KYRBWS). Methods: KYRBWS is conducted annually on Korean middle and high school students to assess their health-related behaviors. The KYRBWS data for 2021, which showed 1206 adolescents reporting suicide attempts out of 54848, was split into the training (n=43878) and test (n=10970) datasets. Thirty-nine features were selected from the KYRBWS questionnaire. The balanced accuracy of the model was employed as a metric to select the best model. Independent validations were conducted with the test dataset of 2021 KYRBWS (n=10970) and the external dataset of 2020 KYRBWS (n=54948). The clinical implication of the prediction by the selected model was measured for sensitivity, specificity, true prediction rate (TPR), and false prediction rate (FPR). Results: Balanced bag of histogram gradient boosting model has shown the best performance (balanced accuracy=0.803). This model shows 76.23% sensitivity, 83.08% specificity, 10.03% TPR, and 99.30% FPR for the test dataset as well as 77.25% sensitivity, 84.62% specificity, 9.31% TPR, and 99.45% FPR for the external dataset, respectively. Conclusion These results suggest that a specific machine learning model can predict suicide attempts among adolescents with high accuracy.

Objective: Suicidal ideation is one of the strongest predictors of suicide, and its relevance to impulsivity in depressed patients has been accumulated. Furthermore, high impulsivity patients show the attenuation of the Nogo amplitude in the GoNogo event-related potential (ERP). The purpose of the current study is to determine the correlation of Nogo ERP to the suicidal ideation depending on the condition of its presence or absence in major depressive disorder (MDD) patients. Methods: A total 162 participants (104 patients with suicidal ideation, 31 patients without suicidal ideation, and 27 healthy controls) were recruited, and performed GoNogo tasks during the electroencephalogram measurement. Depression, anxiety, suicidal ideation and impulsivity were assessed by self-rating scales. The clinical measures, behavioral data and Nogo ERP were compared among groups. Results: The MDD with suicidal ideation (SI) group showed significantly decreased Nogo P3 amplitudes compared to MDD without SI (Fz and Cz electrodes) and control group (all electrodes). The MDD with SI group also had significantly low accuracy of both Go and Nogo trails, compared to the MDD without group. The Nogo P3 amplitudes showed the negative relation to the scores of impulsivity, depression, anxiety and SI. Conclusion Our results concluded that the Nogo P3 ERP amplitude was decreased in MDD patients with SI compared to MDD patients without SI and controls. These findings suggest that the decreased Nogo P3 amplitude is the one of the candidate biomarker for impulsivity in MDD patients to evaluating SI.

Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suicidality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited significantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.

Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suicidality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited significantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.

OBJECTIVES: We aimed to explore the influence of depression on working memory in patients with mild cognitive impairment (MCI) and dementia. METHODS: Clinical and neuropsychological data of 43 subjects with mild cognitive impairment (MCI) (n=17) and dementia (n=26) who had visited Department of Psychiatry at Soonchunhyang University Seoul Hospital, were collected. The subjects were divided into depressed (n=18) and non-depressed (n=25) groups based on the Korean version of Short Geriatric Depression Scale. Two-way analysis of variance test was conducted to evaluate the influence of diagnosis (MCI and dementia), the presence of depression and their interaction on working memory which was measured by digit forward and backward span test. RESULTS: Among the patients with MCI, test score of digit backward span test in depressed group was significantly lower than in non-depressed group. However, among the patients with dementia, there was no significant difference in digit backward span test between depressed and non-depressed groups. CONCLUSION: This study suggests that the depression could deteriorate working memory measured by digit backward span test in patients with MCI, relative to in patients with dementia and it also implicates the diagnostic assessment for depression has clinically importance in patients with MCI.
Dementia* ; Depression* ; Diagnosis ; Humans ; Memory, Short-Term* ; Mild Cognitive Impairment* ; Seoul

PURPOSE: Schizophrenia is a devastating mental disorder and is known to be affected by genetic factors. The chromogranin B (CHGB), a member of the chromogranin gene family, has been proposed as a candidate gene associated with the risk of schizophrenia. The secretory pathway for peptide hormones and neuropeptides in the brain is regulated by chromogranin proteins. The aim of this study was to investigate the potential associations between genetic variants of CHGB and schizophrenia susceptibility. MATERIALS AND METHODS: In the current study, 15 single nucleotide polymorphisms of CHGB were genotyped in 310 schizophrenia patients and 604 healthy controls. RESULTS: Statistical analysis revealed that two genetic variants (non-synonymous rs910122; rs2821 in 3′-untranslated region) were associated with schizophrenia [minimum p=0.002; odds ratio (OR)=0.72], even after correction for multiple testing (p(corr)=0.02). Since schizophrenia is known to be differentially expressed between sexes, additional analysis for sex was performed. As a result, these two genetic variants (rs910122 and rs2821) and a haplotype (ht3) showed significant associations with schizophrenia in male subjects (p(corr)=0.02; OR=0.64), whereas the significance disappeared in female subjects (p>0.05). CONCLUSION: Although this study has limitations including a small number of samples and lack of functional study, our results suggest that genetic variants of CHGB may have sex-specific effects on the risk of schizophrenia and provide useful preliminary information for further study.
Brain ; Chromogranin B ; Female ; Haplotypes ; Humans ; Male ; Mental Disorders ; Neuropeptides ; Odds Ratio ; Peptide Hormones ; Polymorphism, Single Nucleotide ; Schizophrenia* ; Secretory Pathway