Depression and Anxiety最新文献

Background

Myopia and mental disorders share common environmental and behavioral risk factors. Previous studies have been inconsistent regarding the association between them, this systematic review and meta-analysis aims to explore the association between myopia and mental disorders.

Methods

Databases, including PubMed, Web of Science, Wiley online library, and Cochrane Library were searched for eligible publications from January 2000 to July 2024. Studies assessing the relationship between myopia and any one of the mental disorders were identified. Pooled odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs) were estimated using random-effects models.

Results

We identified 15 articles (two cohort studies and 13 cross-sectional studies) examining the impact of myopia on mental disorders, involving 2,841,653 participants from seven countries. Pooled results indicated that myopia was significantly associated with an increased risk of mental disorders (OR = 1.41, 95% CI: 1.23, 1.59). Besides, a significant relationship was observed between the mental disorders and myopia (OR = 1.15, 95% CI: 1.01, 1.29) based on eight articles, including 1,942,855 participants from five countries. Subgroup analyses showed that the association of myopia with both anxiety and depression was significant, with ORs of 1.49 and 1.33, respectively.

Conclusions

Myopia and mental disorders may influence each other, with each condition potentially exacerbating the risk of the other. These findings help to explore the possible interplay between mental disorders and myopia.

Background

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.

Methods

A total of 67 patients with comorbid OCD and MDD, 89 patients with OCD alone, 94 patients with MDD alone, and 94 healthy controls (HCs) completed the acquisition of T1-weighted structural images and were included in the present study. The gray matter volume of each brain region in the AAL116 atlas was calculated, based on which the structural covariance between each pair of brain regions was measured. One-way ANCOVA were performed to explore the structural covariance differences among the four groups.

Results

Compared with HC, patients with comorbidity and patients with OCD or MDD alone showed some common altered structural connections (p < 0.05, false discovery rate [FDR] correction). Patients with comorbidity showed unique altered correlation strength of gray matter volume between left cerebelum_crus1 and left orbital frontal cortex (OFC), left Rolandic operculum, right rectus, right parahippocampal gyrus, left and right fusiform gyrus (FG), left Heschl gyrus, left and right superior temporal gyrus (STG), and left cerebelum_crus2; these 10 connections were also significantly different when comparing comorbidity with OCD and MDD separately (p < 0.05, FDR correction).

Conclusions

Compared with OCD or MDD alone, comorbidity showed both common and unique altered structural covariant connections of gray matter structure. The unique structural connections observed in comorbidity were concentrated between the cerebellum and other brain regions. These findings highlight the crucial role of the cerebellum in the neural basis of comorbid OCD and MDD.

Background

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.

Methods

Participants (N = 99; 70.7% female) wore an actigraph for 2 weeks to assess their habitual nightly sleep duration and regularity. Their average daily smartphone screen use was collected over 1 week with a smartphone application. Standardized questionnaires were used to assess sleep quality and mental health symptoms.

Results

No statistically significant associations were found between objective smartphone screen use and any sleep or mental health variables. Sleep disturbance, sleep-related daytime impairment, anxiety, and depression were positively correlated. However, regression models identified that only sleep-related daytime impairment explained unique variance in anxiety and depression when adjusted for sleep disturbance and duration, gender, age, and screen time.

Limitations

Specific timing of smartphone screen use (e.g., evening use) and/or application content were not collected as part of this study.

Conclusions

These results conflict with prior evidence demonstrating negative relationships between self-reported smartphone screen use, sleep, and mental health. Further research incorporating objective measurement of smartphone screen use, focusing on critical periods for sleep, may provide a more nuanced picture of this relationship. Results also demonstrate the differing roles of night-time sleep disturbance and daytime sleep-related impairment in mental health.

Trial Registration: Australian New Zealand Clinical Trials Registry number: ACTRN12621000132842

Background

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.

Methods

A total of 66 students with depression and 72 matched controls were recruited; eye tracking, VEPs, and auditory P300 data were collected. Descriptive analyses and group comparisons were performed between the depression and control groups. Then, multivariate logistic regression (LR) analysis was conducted to evaluate the relationship between eye tracking, VEPs, and auditory P300 features and Patient Health Questionnaire-9 (PHQ-9) scores. Furthermore, the study employed six classifiers to differentiate between depression and nondepression. Five-fold cross-validation was employed. Model performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), precision, accuracy, recall, and F1 score. We applied SHapley Additive exPlanations (SHAP) values to explain the model.

Results

Depression group was characterized by lower response search scores, higher D values, and prolonged P100 latencies in both eyes. No significant differences were observed in auditory P300 features. Random forest (RF) classifier demonstrated superior classification performance relative to the other five machine learning algorithms. Models utilizing combined features showed enhanced performance compared with those based solely on eye tracking or VEP features. Utilizing the SHAP method, we identified that P100 latency in the right eye was the most significant feature across all machine learning models.

Conclusions

Chinese medical students with depression exhibited reduced responsive search scores and extended P100 latencies, suggesting impairments in attention and visual information processing associated with depression. The combined eye tracking and VEPs proved to be more effective than single features for distinguishing depression and nondepression. P100 latency in the right eye may be the most significant predictor of depression.