The comorbidity of obsessive-compulsive disorder (OCD) and major depressive disorder (MDD) is a prevalent clinical phenomenon, which is associated with greater symptom severity, suicide risk, and poorer treatment outcomes. However, the neural basis of this comorbidity remains unclear. The aim of this study was to investigate the common and unique neural basis of comorbidity compared with OCD and MDD alone.

There is a growing interest in understanding insight or illness awareness in anxiety; however, most assessment instruments were designed for psychosis. The unique features of anxiety highlight the need for tailored measures to accurately evaluate insight. The aim of this study was to develop and validate the Schedule for the Assessment of Insight in Anxiety (SAI-A), a clinician-rated scale for assessing insight in anxiety disorders. We interviewed 46 participants diagnosed with anxiety disorders, conducted SAI-A interviews, and administered self-report measures. Using correlation and principal component analysis (PCA), we identified and assessed scale components, ensuring their reliability and consistency. The SAI-A demonstrated acceptable psychometric properties, including convergent validity with an established self-report measure ( = -0.39, =0.008) and internal consistency (Cronbach's α = 0.70). It showed moderate to strong agreement, interrater reliability (weighted kappa = 0.53, intraclass correlation coefficient [ICC] = 0.67), and test-retest reliability (ICC = 0.65). Two distinct insight components emerged: awareness of disorder and need for treatment. Higher overall SAI-A scores correlated with symptom severity and impairment ( = 0.56,  = 0.51, < 0.001, respectively) and medication usage. The SAI-A is a valid and reliable assessment tool, providing a comprehensive framework for understanding and addressing insight in the context of anxiety disorders.

The present study aimed to examine the bidirectional relationship between internet use and depressive symptoms among middle-aged and older adults. Moreover, it explored whether proactive health behavior mediates the association between internet use and depressive symptoms. We used the latest three-wave data (2015, 2018, and 2020) from the China Health and Retirement Longitudinal Study (CHARLS), which included 11,332 participants aged 45 years and older. The bidirectional relationship between internet use and depressive symptoms was examined using a cross-lagged model. The mediating role of proactive health behavior was also investigated using a cross-lagged mediation model. Cross-lagged models indicated reciprocal effects between depressive symptoms and internet use. Internet use had a greater impact on subsequent depressive symptoms than vice versa. Mediation analyses further revealed that proactive health behavior significantly mediated the path from internet use to depressive symptoms. Furthermore, subgroup analyses showed these effects were not significantly heterogeneous in subgroups by age and chronic disease status. This study sheds light on the direction of the association between internet use and depressive symptoms. Internet use could reduce depressive symptoms among middle-aged and older adults by enhancing proactive health behavior.

Personality traits and childhood abuse were found to be associated with depressive symptoms and with each other. However, no previous study has elucidated the directional interrelationship among those factors in a clinical population of patients with major depressive disorder (MDD). This study sought to construct networks to explicate the directional interrelationship among those factors and social disability, identify the most central factor, and explore potentially existing causality chains implied by the directed association chains.

This cross-sectional study, utilizing National Health and Nutritional Examination Surveys (NHANESs) data from 2005 to 2018, examines the association between age at first live birth and depression among women aged 12 years or older.

There is growing recognition of the connection between childhood trauma and postpartum depressive symptoms. However, the specific patterns and complex relationships among them remain largely unclear. This study employs network analysis to dissect the intricate associations between postpartum women's childhood trauma and postpartum depressive symptoms, aiming to lay a foundation for targeted interventions.

Many patients experience psychological distress in the preoperative phase, whilst screening based on cut-off points of assessment scales showed limited value in predicting clinical postoperative adverse outcomes.

[This corrects the article DOI: 10.1155/2024/9719635.].

Past research showed that lower emotional clarity (EC) was associated with more maladaptive emotion regulation (ER) and psychopathology, such as obsessive-compulsive (OC) disorder (OCD). However, most of these studies used single time-point, retrospective self-reports. Next to high risk for recall biases and low ecological validity, this assessment method is only able to capture between-person differences (i.e., individuals generally high vs. low in EC). It therefore neglects temporal variations in EC and resulting within-person differences (i.e., moments with higher-than-usual vs. lower-than-usual EC within one individual). To address this gap, our study uses intensive longitudinal data based on a 6-day ecological momentary assessment (EMA) design with up to six measurements daily. In total,  = 72 individuals diagnosed with OCD and  = 54 mentally healthy controls (HCs) reported on EC, ER behavior, and OC symptoms. Our results confirm that EC was significantly lower in individuals with OCD, even when controlling for baseline depression. Furthermore, lower within-person EC was associated with a higher number of used avoidance-oriented ER strategies, a lower number of engagement-oriented ER strategies and lower ER effectiveness. Surprisingly, these associations were more pronounced in the control (vs. OCD) group. In individuals with OCD, results indicated a negative concurrent (but not subsequent) association between EC and OC symptoms. Explanations for nonsignificant findings and possible implications for the role of EC in OCD are discussed.

[This corrects the article DOI: 10.1155/2024/4747449.].

Previous studies have shown that individuals with depression have an impaired ability in encoding single facial expressions. However, little is known about how depressive tendencies-subclinical emotional distress that may progress to clinical depression-affect the perception of the average emotion of multiple faces. To address this question, the current study investigated whether depressive tendencies affect explicit or implicit ensemble perception of emotion. In Study 1, participants viewed sets of four emotionally varying faces (ranging from angry to happy) for 2000 or 50 ms, then judged if a subsequent test face was angrier than the average emotion of the preceding set. Results showed that the high depressive symptom (HDS) group had a point of subjective equality (PSE) more biased toward anger compared to the low depressive symptom (LDS) group when exposure time was 2000 ms. However, this difference disappeared when the time was shortened to 50 ms. In Study 2, we assessed the automatic perception of ensemble emotion by requiring participants to judge whether a probe face was a member of the preceding set, a task that does not explicitly demand averaging. Results indicated that the HDS and LDS groups had a similar likelihood of misidentifying the set mean as a member under both 2000 and 50 ms conditions, indicating a comparable automatic coding of ensemble emotion. Together, the current research demonstrates that depressive tendencies can bias ensemble coding for emotional faces at explicit level but not at implicit level.

Poor sleep has been associated with mental health concerns such as anxiety and depression. Prior evidence suggests that smartphone use may be a factor in poor sleep and mental ill-health in young adults, though most studies have relied on self-reported measures of smartphone use and sleep, which can be unreliable. This study used objective and subjective measures to examine the relationship between time spent using smartphones, sleep duration, quality, regularity, and symptoms of depression and anxiety in a sample of self-selected poor sleepers.

Current assessment of depression primarily relies on psychological scales. Although the use of machine learning in depression has grown, limited reports are available on multiple neurophysiological measurements. We employed machine learning algorithms incorporating eye tracking, visual evoked potentials (VEPs), and auditory P300 to classify depression among Chinese medical students.

Anxiety's prevalence is increasing, making it a widespread mental health concern. However, scale-based diagnostic methods have limitations. Music therapy helps regulate emotions and alleviate anxiety symptoms. Functional near-infrared spectroscopy (fNIRS) offers a novel approach to diagnosing mental disorders by measuring changes in the concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) in the superficial layers of the brain, thereby reflecting brain activation. This is the first study to use fNIRS to examine the impact of music therapy on anxiety. fNIRS was used to measure changes in HbO and HbR in the superficial brain regions of individuals with anxiety symptoms to evaluate music therapy effectiveness and identify brain regions associated with anxiety. This study recruited 83 participants: 17 comprised the healthy control group, and 66 comprised the anxiety group. The anxiety group was divided into an intervention group (34 participants) and a waiting-list group (32 participants). The intervention group underwent 12 music therapy sessions and exhibited significant changes compared with the waiting group. These changes included connectivity between Wernicke's area and the dorsolateral prefrontal cortex (DLPFC) as well as the visual association cortex and Broca's triangular area. These results suggested that the connectivity characteristics of these brain regions were associated with anxiety. Music therapy significantly improved brain network connectivity characteristics in individuals with anxiety symptoms. Furthermore, fNIRS indicators could serve as biomarkers for the auxiliary identification of anxiety symptoms, aiding early identification and intervention. ClinicalTrials.gov identifier: NCT05648539.

Dietary diversity has been found to be related to depressive symptoms. However, the relationship between the trajectory of dietary diversity score (DDS) and depressive symptoms in Chinese older adults remains unclear.

Sleep problem among coronary heart disease (CHD) patients has emerged as a pressing health problem. This study aimed to explore different sleep quality patterns and their associations with depressive and anxiety symptoms among CHD patients.

Extant questionnaire measures of stress frequently conflate stress exposure and response and can be confounded by factors such as trait neuroticism; by contrast, contextual interviews target stress exposure but require significant resources that are a barrier to swift data collection. We reasoned that it may be possible to use researcher-rated "snapshots" of brief participant-written descriptions of stress to obtain similar independence from trait neuroticism as interviews do, even though such an approach would not provide all the benefits of interview measures. This study evaluates the psychometric properties of this novel stress assessment approach using both researcher and self-report ratings, in part by examining the contribution of these indicators of stress to internalizing subfacets. Adults ( = 378) reported on their stress during the COVID-19 pandemic (May-June, 2020) using two measures that covered 11 life domains (~4158 ratings per measure). Inter-rater reliability for researcher ratings of participant stressor descriptions was good, and both self-rated perceived stress and researcher-rated stress had significantly smaller correlations with neuroticism compared to a traditional perceived stress measure, indicating favorable discriminant validity. Both approaches generated acceptable two-factor (interpersonal and noninterpersonal) structures. Interpersonal and noninterpersonal self- and researcher-rated stress were associated with internalizing facets, with some variation. These results provide initial evidence that two novel and rapid methods of measuring stress retain certain appealing properties of life stress interviews (LSIs), for occasions in which interviews are not feasible.

The mental health of mothers has an important impact on both the growth and development of infants and on the health of mothers themselves. Family decision-making may play an important role in mother's mental health, yet little research has explored the relationship. This paper explores the association and influential pathways between family decision-making and mental health among mothers of infants in rural western China.

Previous studies have indicated an association between mood symptoms and cognitive decline in the Alzheimer's disease (AD) spectrum. Amyloid-beta (Aβ) deposition in the brain, which is a core pathological characteristic of AD, along with the presence of plasma biomarkers, such as phosphorylated tau protein (p-tau), constitutes an early predictive indicator for AD. We attempted to explore the relationship between mood symptoms and the presence of AD-related plasma biomarkers in patients with brain Aβ deposition.

With rapid population aging in China, understanding the relationship between depression symptoms and cognitive function is crucial for improving the mental health of older adults. This study investigates these dynamics using data from the China Health and Retirement Longitudinal Study (CHARLS).

This study investigates the association between obstructive sleep apnea (OSA) and the risk of developing psychiatric disorders. OSA, characterized by intermittent hypoxia and sleep fragmentation, contributes to brain damage and emotional regulation issues, which may predispose individuals to psychiatric conditions such as depression, anxiety, bipolar disorder, and schizophrenia. The research focuses on understanding the heightened risks of these disorders in OSA patients to inform clinical interventions. To assess the risk differences for psychiatric disorders in patients with OSA compared to those without OSA.

Autistic traits (ATs) and social anxiety (SA) are closely associated; however, few studies have investigated the potential mediating mechanism of this relationship using longitudinal data. This study examined: (1) the developmental trajectories of ATs, rumination, and SA among college students; (2) whether the baseline levels of ATs predicted the developmental trajectories of SA; and (3) whether the trajectories of rumination mediated this longitudinal association.

Depression, a prevalent mood disorder, is often accompanied by considerable functional impairment. Its relationship with specific physical functioning domains and potential variations by symptom type or sex, however, has not been fully clarified. This study investigates these associations, paying particular attention to overall severity, sex differences, and cognitive-affective versus somatic symptom dimensions.

This study investigated the relationships between anxiety, depression symptoms, and C-reactive protein (CRP) across insomnia severity levels using network analysis and examined the structural differences within these networks. Gaussian graphical model network analysis with Least Absolute Shrinkage and Selection Operator (LASSO) regularization was conducted on UK Biobank data ( = 143,027). Depression and anxiety symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively. CRP was quantified using immunoturbidimetric-high-sensitivity analysis. Participants were categorized by insomnia frequency (never/rarely, sometimes, and usually). The strength symptoms and expected influence identified core symptoms, while bridge expected influence (bridge EI) determined bridge symptoms. Network comparison tests (NCTs) were performed pairwise across the three groups to assess differences in global strength and edge weights. Across all networks, "Depressed mood" demonstrated the highest strength centrality, while "Irritability" exhibited the highest bridge EI. "Depressed mood" had the highest expected influence centrality in the never/rarely insomnia group and "Uncontrollable worry" in other groups. NCTs revealed significant differences in global strength ( = 0.178, < 0.01) and edge weights ( = 0.062, < 0.01) between the never/rarely and usually insomnia groups, with stronger connections between depressive symptoms (energy/appetite) and CRP in the usually insomnia group ( < 0.001). The central roles of depressed mood, uncontrollable worry, and irritability in the anxiety-depression-CRP network across all insomnia severity groups suggest that these symptoms represent potential targets for future intervention research. Notably, network structure differed across insomnia severity; the strengthened associations between depressive symptoms and CRP in the usually insomnia group suggest that insomnia severity may be an important factor to consider in understanding the relationships between affective and inflammatory processes.

This study aims to address critical gaps in understanding the bidirectional relationships between food insecurity, anxiety, and depression in Meru County, Kenya. By employing a cross-lagged panel analysis, we seek to clarify these temporal dynamics, contributing to the design of targeted interventions that integrate food security and mental health in the context of climate change.

The advancement of artificial intelligence (AI) tools utilizing electroencephalography (EEG) for diagnosing major depressive disorder (MDD) has shown significant progress. However, the practical implementation of these tools is often impeded by the large amount of EEG data required for training AI models and the lack of explanations for the MDD diagnoses. This study aims to develop an interpretable deep-learning-based computer-aided diagnostic system for diagnosing male MDD patients using explainable AI (XAI) algorithms. The CAD system was designed to facilitate the diagnostic process by using a reduced number of EEG channels and data length while enhancing understanding of the neurophysiological characteristics of male MDD. Resting-state EEG data were collected from 40 male MDD patients (20-63 years) and 41 gender-matched healthy controls (HCs, 19-61 years). A shallow convolutional neural network (CNN; Shallow ConvNet) model was utilized to distinguish between MDD patients and HCs. Relevance scores were extracted by the layer-wise relevance propagation (LRP) method, integrated with the Shallow ConvNet, to interpret the outcomes of the deep-learning-based CAD system. Additionally, changes in diagnostic performance were assessed by progressively reducing the number of channels using an LRP-based channel selection method, as well as EEG data length. Our XAI-based CAD system showed a high diagnostic performance of 100% when using the whole 62 channels with 180-s EEG data. A relatively high diagnostic performance over 90% was retained with only five channels with 60-s EEG data. Neurophysiologically meaningful brain areas, such as fronto-central, centro-parietal, and occipital areas, also revealed significant differences in relevance scores extracted by the LRP-method between the two groups. This study successfully developed a high performance and practical XAI-based CAD system for male MDD patients. Our developed CAD system not only achieves high diagnostic accuracy but also provides meaningful neurophysiological biomarkers for male MDD patients.

Depression and social anxiety are frequently co-occurring conditions that significantly impact young people. Anhedonia may be important to consider in early interventions for these conditions, but the roles of specific dimensions of anhedonia-anticipatory and consummatory-are not well understood. This study explored the symptom connectivity of depression and social anxiety in university students, focusing on how the two dimensions of anhedonia relate to symptoms of both conditions.

Anxiety is a significant mental health challenge for women of reproductive age worldwide, often contributing to broader psychological issues. However, research on anxiety prevalence among this demographic, particularly in Bangladesh, remains limited. This study addresses this gap by identifying potential predictors of anxiety among married women in Bangladesh. Using data from the nationally representative Bangladesh Demographic and Health Survey (BDHS) 2022, the generalized anxiety disorder (GAD-7) scale was employed to assess anxiety levels. Descriptive and inferential statistical analyses, including one-way ANOVA and stepwise multiple regression, were conducted to identify key predictors. The findings reveal that 25.8% of married women in Bangladesh experience mild to severe anxiety, with 4.1% reporting moderate to severe anxiety. A six-factor model derived from stepwise multiple regression explained 17.3% of the variance in anxiety levels. The most significant predictor was a history of terminated pregnancy, accounting for 6.8% of the variance ( change = 0.068; ≤ 0.001). Other notable predictors included pressure from spouses or family members ( change = 0.038; ≤ 0.001), educational status ( change = 0.028; < 0.001), religion ( change = 0.019.; =0.018), continuation of education after marriage ( change = 0.012; =0.030), and husband's educational attainment ( change = 0.007; =0.033). Additional factors such as employment after marriage, age at first sexual intercourse, and wealth status also played significant roles. The study highlights the substantial prevalence of anxiety among married women in Bangladesh, emphasizing the influence of socioeconomic along with other potential factors. Further research is needed to develop targeted interventions addressing socioeconomic and behavioral determinants, ensuring the mental well-being of married women.

Major depressive disorder (MDD) is common and associated with high social and economic burden. Knowledge of characteristics of hospitalized adults with MDD can help identify clinical treatment and prevention targets.