Depression and Anxiety最新文献

Background: Transcranial magnetic stimulation (TMS) is a widely used treatment for major depressive disorders (MDD). However, its physiological effects remain unclear, and its efficacy varies among patients. Functional connectivity (FC) within MDD-related networks has shown potential for predicting TMS efficacy. White matter (WM) micromorphology contributes to these connections and may provide new insights into the prediction of TMS effects and its antidepressant mechanism in MDD, which has not been thoroughly investigated. Therefore, this study aimed to identify potential predictors of TMS efficacy using whole-brain WM microstructural characteristics.

Methods: This retrospective study included 41 patients with MDD who completed 20-session TMS treatments. We examined intra- and extracellular WM using neurite orientation dispersion and density imaging (NODDI). We examined the correlations between pre-TMS whole-brain WM measures and the reduction in Montgomery–Åsberg Depression Rating Scale (MADRS) scores after TMS, as well as the longitudinal changes in regional WM measures correlated with MADRS reduction.

Results: The pre-TMS intracellular neurite density index (NDI) within the bilateral internal capsule (IC), right posterior corona radiata (PCR), right superior corona radiata (SCR), corpus callosum (CC), and bilateral posterior thalamic radiation (PTR) were positively associated with the reduction in MADRS scores. The pre-TMS extracellular free-water fraction (FWF) within the bilateral IC, splenium of the CC, and right SCR was significantly related to the reduction in MADRS scores. The FWF within the left posterior limb of the IC, left PCR, and left SCR decreased after TMS treatment.

Limitations: The study’s findings cannot rule out the potential effects of concurrent pharmaceutical therapies, as a control group not receiving TMS treatment was not included.

Conclusions: Regional NDI and FWF values at baseline could predict TMS efficacy in patients with MDD. Reducing the extracellular FWF in the left hemisphere may be a potential therapeutic mechanism of TMS in MDD.

Background: Subthreshold depression (SD) is regarded as a prodromal stage and a substantial risk factor for major depressive disorder (MDD). The timely identification of SD is of critical clinical significance. This study aimed to develop a machine learning (ML) classification model for the identification of individuals with SD using functional near-infrared spectroscopic imaging (fNIRS) and the verbal fluency task (VFT).

Methods: This study recruited a total of 70 participants with SD and matched 73 healthy controls (HCs) to differentiate between the two groups based on functional connectivity (FC) features during fNIRS–VFT, using an interpretable random forest (RF) classification model.

Results: The RF model demonstrated an area under the curve (AUC) of 0.77, an accuracy (ACC) of 75.86%, a sensitivity of 75.00%, a specificity of 76.00% and an F1 score of 0.75 for identifying participants with SD. The highest-ranked FC features, in terms of importance, were identified between Channel (CH) 26 (the right frontal eye fields (FEFs)) and CH 30 (the right FEF), CH 3 (the left premotor and supplementary motor cortex (PMC-and-SMA)) and CH 42 (the right PMC-and-SMA), as well as CH 26 (the right FEF) and CH 32 (the right primary somatosensory cortex (PSC)).

Conclusion: The RF model has the capacity to effectively classify individuals with SD efficacy based on the abnormal FC features of fNIRS–VFT, particularly in the right FEF, bilateral PSC and right PMC-and-SMA. The findings of this study have provided a foundation for large-scale screening of SD populations, offering promising opportunities for the early diagnosis and prevention of MDD.

Objective: Interest in the study of anxiety disorders is growing rapidly. However, there is still a lack of comprehensive bibliometrics research in measuring and analyzing global scientific publications. The aim of this article is to review the current research status and trends of anxiety disorders worldwide through bibliometrics and to provide reliable support for future in-depth research.

Methods: In this bibliometric study, we utilized VOSviewer to evaluate the Web of Science Core Collection (WoSCC) publications and to build visualizing maps to analyze the research progress on this topic between January 1, 2000, and August 7, 2024.

Results: Up to August 7, 2024, the study included a total of 67,386 English original articles and review articles published in 4054 journals by 206,496 authors from 164 countries/regions. The United States was a leader in articles, researchers, and institutions in the field of anxiety disorders. Using cluster analysis, the keywords were divided into five major clusters: (1) etiology and pathogenesis, (2) clinical classification and interventions, (3) comorbidity, (4) anxiety caused by the COVID-19 pandemic and specific groups, and (5) anxiety disorders in children and adolescents.

Conclusion: Molecular and neural circuit mechanisms, adolescent psychological problems, objective indicators of diagnosis and classification, and technology-assisted therapy are still the focus of future research. The findings provide a comprehensive overview of anxiety disorder research, which may help physicians, researchers, and local authorities and community health staff to more fully understand trends and influence in the field and highlight under-researched areas, which could be the basis for future research.

Background: Artificial intelligence (AI)–based psychotherapeutic interventions may bring a new and viable approach to expanding psychiatric care. However, evidence of their effectiveness remains scarce. We evaluated the efficacy of AI-based psychotherapeutic interventions on depressive, anxiety, and stress symptoms at postintervention and follow-up assessments.

Methods: A three-step comprehensive search via nine electronic databases (PubMed, Embase, CINAHL, Cochrane Library, Scopus, IEEE Xplore, Web of Science, PsycINFO, and ProQuest Dissertations and Theses) was performed.

Results: Thirty randomized controlled trials (RCTs) in 31 publications involving 6100 participants from nine countries were included. The majority (79.1%) of trials with intention-to-treat analysis but less than half (48.6%) of trials with perprotocol analysis were graded as low risk. Meta-analyses showed that interventions significantly reduced depressive symptoms at the postintervention assessment (t = −4.40, p = 0.001) with medium effect size (g = −0.54, 95% CI: −0.79 to −0.29) and at 6–12 months of assessment (t = −3.14, p < 0.016) with small effect size (g = −0.23, 95% CI: −0.40 to −0.06) in comparison with comparators. Our subgroup analyses revealed that the depressed participants had a significantly larger effect size in reducing depressive symptoms than participants with stress and other conditions. At postintervention and follow-up assessments, we discovered that AI-based psychotherapeutic interventions did not significantly alter anxiety, stress, and the total scores of depressive, anxiety, and stress symptoms in comparison to comparators. The random-effects univariate meta-regression did not identify any significant covariates for depressive and anxiety symptoms at postintervention. The certainty of evidence ranged between moderate and very low.

Conclusions: AI-based psychotherapeutic interventions can be used in addition to usual treatments for reducing depressive symptoms. Well-designed RCTs with long-term follow-up data are warranted.

Trial Registration: CRD42022330228

Background: Virtual reality exposure therapy (VRET), an innovative form of exposure therapy (ET), has been demonstrated to be effective in treating acrophobia. However, its neural mechanisms of action and how it differs from traditional imaginal exposure therapy (IET) remain unclear. This study utilized resting-state functional magnetic resonance imaging (fMRI) to investigate the effects of VRET on brain activity in acrophobic patients and to explore the potential mechanisms underlying its therapeutic action.

Method: Fifty patients with acrophobia were randomly assigned to either an experimental group (25 patients) or a control group (25 patients) based on different treatments. The experimental group received VRET, while the control group received conventional IET. A mixed-design repeated-measures analysis of variance (ANOVA) was performed on the whole brain to identify brain regions affected by the intervention. Both groups of patients underwent treatment twice a week for 3 weeks. fMRI scans were performed for all patients at baseline and after treatment to facilitate a comparison of clinical effects at the end of the treatment period. The degree centrality (DC) values of the blood oxygenation level dependent signals across the entire brain were analyzed. A mixed-design repeated-measures ANOVA was conducted on the pre- and post-intervention data to identify brain regions affected by the intervention. The degree of symptom improvement was assessed using self-report measures, including the Acrophobia Questionnaire (AQ), the Attitude Toward Heights Questionnaire, the Behavior Avoidance Test, and the 7-item Generalized Anxiety Disorder Scale. These assessments were correlated with pre- and post-intervention differences in brain activity. Additionally, a functional connectivity (FC) analysis was conducted to identify any atypical connectivity patterns following the ET.

Results: There was a significant positive correlation between the change in scores on the AQ and the right middle temporal gyrus (MTG) (r = 0.442, p = 0.045). After VRET, DC values in the right calcarine, MTG, cuneus, and precuneus were decreased (p < 0.005), while DC values in the postcentral gyrus decreased after IET (p < 0.05). Additionally, reduced FC between the right MTG and both the right medial superior frontal gyrus and the left MTG was observed in acrophobia patients following VRET. In the IET group, reduced FC between the left MTG and the left superior temporal gyrus was found (p < 0.005).

Conclusion: Preliminary results suggest that VRET may improve abnormal brain activity in acrophobia by modulating the activity of the default mode network and the primary visual cortex.

Background: Escitalopram and sertraline are first-line medications for treating depression. They belong to selective serotonin reuptake inhibitors (SSRIs) and are widely used due to their effectiveness and fewer side effects. However, despite the significant efficacy of escitalopram and sertraline, there is a large variation among individuals. Therefore, predicting symptom improvement based on the baseline period is crucial.

Methods: In this study, we conducted functional connectivity (FC) analysis of electroencephalogram (EEG) data during resting-state with eyes closed, resting-state with eyes open, watching neutral videos, negative videos, and comedy videos for 30 untreated depression patients over 2 weeks. Each modality yielded 18 EEG FC features. Based on the treatment response at 8 weeks, patients were divided into treatment-effective and treatment-ineffective groups. The dataset was randomly split into a 75% training set and a 25% independent test set. Feature selection was performed on these FC features in the training set, and the selected features were used to classify the effective and ineffective groups using the support vector machine (SVM) machine learning algorithm. Fivefold cross-validation was conducted on the training set to obtain validation results, followed by testing on the test set. The Spearman’s correlation method was used to analyze the correlation between each EEG feature value and the reduction rate of the Hamilton Depression Rating Scale for Depression (HAMD-17) scores from baseline to 8 weeks, with Bonferroni correction applied.

Results: The study found that out of all modalities, 33 features achieved classification accuracies of over 95% on the validation set, and two features achieved classification accuracies of over 85% on the independent test set. A total of 58 feature values were found to be correlated with the reduction rate of HAMD-17 scores from baseline to 8 weeks.

Conclusions: The findings from this research suggest that EEG FC features at baseline can be used to differentiate between effective and ineffective groups with high accuracy using machine learning models. Multiple feature values and HAMD-17 scores were found to be correlated with the reduction rate of HAMD-17 scores from baseline to 8 weeks, and these correlated feature values can be used to predict treatment efficacy.

Trial Registration: ClinicalTrials.gov identifier: NCT05775809

Background: Suicide is one of the leading causes of premature death, and dysfunctional reward processing may serve as a potential mechanism. However, effective treatment targeting reward circuits is rarely reported.

Objective: The present study investigated the therapeutic efficacy of two individualized protocols, repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS), targeting the left dorsolateral prefrontal cortex (lDLPFC)–nucleus accumbens (NAcc) circuit on suicidal ideation among patients with major depressive disorder (MDD).

Methods: Here, 40 healthy controls (HCs) and 70 MDD patients (MDDs) were recruited for this double-blinded, sham-controlled clinical trial. The reward learning process during the Iowa gambling task (IGT) was initially measured at the baseline. Further, 62 MDDs were assigned to receive 15 daily sessions of individualized rTMS (n = 25), iTBS (n = 15), or sham treatment (n = 22) to the site of strongest lDLPFC–NAcc connectivity.

Results: We found MDDs demonstrated abnormalities in both IGT performance and reward-associated event-related potential (ERP) components compared to HCs. MDDs in the rTMS and iTBS groups showed significant improvements in suicidal ideation and anhedonia symptoms compared to the sham group. The rTMS group also exhibited a more negative-going N170 and feedback-related negativity (FRN) after treatment, and the increase in N170 absolute amplitude posttreatment showed a trend of correlation with improved Temporal Experience Pleasure Scales (TEPSs) and TEPS-anticipatory (TEPS-ant) scores.

Conclusion: The current study indicates that reward circuit-based rTMS and iTBS showed comparable antisuicidal effects in depressive patients, suggesting that the lDLPFC–NAcc pathway may serve as a potential treatment target.

Trial Registration: ClinicalTrials.gov identifier: NCT03991572

Background: As the evidence linking depression and sleep quality to overall survival (OS) is lacking, we aim to evaluate the individual and joint associations of depression-related symptoms and sleep quality with OS among patients with ovarian cancer (OC).

Method: A total of 554 patients with OC were included. Depression-related symptoms and sleep quality were measured using the Patient Health Questionnaire (PHQ-9) and the Pittsburgh Sleep Quality Index (PSQI). The primary analysis utilized the average levels of depression-related symptoms and sleep quality by averages of PSQI score and PHQ score between pre-diagnosis and post-diagnosis. Deaths were ascertained until February 16, 2023, via medical records and active follow-up. Cox proportional hazard regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of the joint and individual associations of sleep quality and depression-related symptoms with OC survival.

Result: A total of 205 deaths occurred during a median follow-up of 44.6 months. The average level of depression-related symptoms and sleep quality were independently associated with OS (HR_{depression vs. no depression} = 1.55, 95% CI = 1.10–2.17; HR_{poor sleep vs. good sleep} = 2.05, 95% CI = 1.51–2.75). We documented multiplicative and additive interactions between these two factors in relation to OS (P_interaction < 0.01; relative excess risk due to interaction = 0.16, 95% CI = 0.01–0.31). Combinations of depression-related symptoms and poor sleep quality were positively associated with OC mortality (HR = 2.61, 95% CI = 1.70–4.00), compared to patients with good sleep quality and without depression-related symptoms.

Conclusion: Poor OC survival was observed among patients with poor sleep quality and depression-related symptoms, independently and jointly.

The escalating global prevalence of type 2 diabetes mellitus (T2DM) and mental disorder (MD) including schizophrenia, bipolar disorder, major depressive disorder, and anxiety highlights the urgency for comprehensive therapeutic strategies. Aerobic exercise (AE) is a viable adjunct therapy, providing significant benefits for individuals dealing with both T2DM and MD. This review consolidates evidence on AE’s role in alleviating the physiological and psychological effects of these comorbid conditions. It delves into the pathophysiological connections between T2DM and various MD, including depression, schizophrenia, anxiety, and bipolar disorder—emphasizing their reciprocal exacerbation. Key neurophysiological mechanisms through which AE confers benefits are explored, including neuroinflammation modulation, brain structure and neuroplasticity enhancement, growth factor expression regulation, and hypothalamic–pituitary–adrenal (HPA)/microbiota–gut–brain (MGB) axis normalization. Clinical results indicate that AE significantly improves both metabolic and psychological parameters in patients with T2DM and MD, providing a substantial argument for integrating AE into comprehensive treatment plans. Future research should aim to establish detailed, personalized exercise prescriptions and explore the long-term benefits of AE in this population. This review underscores the potential of AE to complement existing therapeutic modalities and enhance the management of patients with T2DM and MD.