Depression and Anxiety最新文献

Background: 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.

Methods: Gaussian graphical model network analysis with Least Absolute Shrinkage and Selection Operator (LASSO) regularization was conducted on UK Biobank data (N = 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.

Results: 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 (S = 0.178, p < 0.01) and edge weights (M = 0.062, p < 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 (p < 0.001).

Conclusions: 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.

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 (r = −0.39, p = 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 (r = 0.56, r = 0.51, p < 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 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.