Childhood trauma is recognized as an important risk factor in suicidal ideation, however it is not fully understood how the different types of childhood maltreatment influence suicidal ideation nor what variables mediate the relationship between childhood trauma and suicidal ideation. This study examined the path from childhood trauma to suicidal ideation, including potential mediators. A sample of 211 healthy adults completed the Childhood Trauma Questionnaire (CTQ), Beck scale for Suicidal Ideation (BSI), Functional Social Support Questionnaire (FSSQ) and Hospital Anxiety and Depression Scale (HADS). Path analysis was used to investigate the relationship among study variables. Of the several types of childhood maltreatment we considered, only childhood sexual abuse directly predicted suicidal ideation (β=0.215, p=0.001). Childhood physical abuse (β=0.049, 95% confidence interval: 0.011-0.109) and childhood emotional abuse (β=0.042, 95% confidence interval: 0.001-0.107) indirectly predicted suicidal ideation through their association with anxiety. Childhood neglect indirectly predicted suicidal ideation through association with perceived social support (β=0.085, 95% confidence interval: 0.041-0.154). Our results confirmed that childhood sexual abuse is a strong predictor of suicidal ideation. Perceived social support mediated the relationship between suicidal ideation and neglect. Anxiety fully mediated the relationship between suicidal ideation and both physical abuse and emotional abuse. Interventions to reduce suicidal ideation among survivors of childhood trauma should focus on anxiety symptoms and attempt to increase their social support.

Eunok Moon, Seung-Hwan Lee, Do-Hyung Kim, and Boram Hwang. Clin Psychopharmacol Neurosci 2013;11:137-43. https://doi.org/10.9758/cpn.2013.11.3.137

Background: Automatic diagnosis of depression based on speech can complement mental health treatment methods in the future. Previous studies have reported that acoustic properties can be used to identify depression. However, few studies have attempted a large-scale differential diagnosis of patients with depressive disorders using acoustic characteristics of non-English speakers. Objective: This study proposes a framework for automatic depression detection using large-scale acoustic characteristics based on the Korean language. Methods: We recruited 153 patients who met the criteria for major depressive disorder and 165 healthy controls without current or past mental illness. Participants' voices were recorded on a smartphone while performing the task of reading predefined text-based sentences. Three approaches were evaluated and compared to detect depression using data sets with text-dependent read speech tasks: conventional machine learning models based on acoustic features, a proposed model that trains and classifies log-Mel spectrograms by applying a deep convolutional neural network (CNN) with a relatively small number of parameters, and models that train and classify log-Mel spectrograms by applying well-known pretrained networks. Results: The acoustic characteristics of the predefined text-based sentence reading automatically detected depression using the proposed CNN model. The highest accuracy achieved with the proposed CNN on the speech data was 78.14%. Our results show that the deep-learned acoustic characteristics lead to better performance than those obtained using the conventional approach and pretrained models. Conclusions: Checking the mood of patients with major depressive disorder and detecting the consistency of objective descriptions are very important research topics. This study suggests that the analysis of speech data recorded while reading text-dependent sentences could help predict depression status automatically by capturing the characteristics of depression. Our method is smartphone based, is easily accessible, and can contribute to the automatic identification of depressive states.

Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD). Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales. At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales. Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD.

Objective: Although there is strong evidence that childhood trauma is associated with the development of depression and anxiety, relatively few studies have explored potential mediating factors for this relationship. The present study aimed to evaluate the mediating role of rumination in the link between childhood trauma and mood status such as depression, anxiety and affective lability. Material and Methods: Two hundred and seven non-clinical participants completed the Childhood Trauma Questionnaire, the Ruminative Response Scale, the Beck Depression Inventory, the State Anxiety Inventory, and the Affective Lability Scale. Structural equation modeling was used to evaluate the results. Results: Our results supported that rumination is a meaningful mediator between childhood trauma and depression/anxiety in non-clinical participants. The mediation model indicated that childhood trauma and its subtypes are linked to depression and anxiety through three subtypes of rumination, thereby supporting a significant indirect relationship (Standardized coefficient [SC] = .56, p < .001 for the path from trauma to rumination; SC = .67, p < .001, from rumination to mood). The direct relationship between childhood trauma and mood symptoms was also significant in a model including rumination (SC = .68, p < .001). The mediation effect of rumination in the relationship between childhood trauma and mood was more predominant in female participants. Conclusions: The present study found that rumination mediates the influence of childhood trauma on the development of mood symptoms in non-clinical participants. Childhood trauma appears to be a critical determinant for developing symptoms of depression and anxiety.

Hyeonjin Jeon, Seung-Hwan Lee. Clin Psychopharmacol Neurosci 2018;16:18-31. https://doi.org/10.9758/cpn.2018.16.1.18