European Archives of Psychiatry and Clinical Neuroscience最新文献

Aim: This study examined the effects of two accelerated repetitive transcranial magnetic stimulation (rTMS) protocols in patients with major depressive disorder (MDD).

Methods: Twenty-two patients received accelerated high-frequency rTMS (HF-rTMS; 10 Hz, left prefrontal cortex), and 18 received accelerated low-frequency rTMS (LF-rTMS; 1 Hz, right prefrontal cortex). Each participant underwent 10 sessions over 2 consecutive days (5 sessions per day). Depression, anxiety, and sleep quality were assessed using the Hamilton Depression Rating Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), and Pittsburgh Sleep Quality Index (PSQI), respectively. Clinical outcomes were evaluated at baseline and at 1 day, 2 weeks, and 4 weeks after treatment.

Results: HAM-D scores significantly decreased from baseline after accelerated rTMS and remained improved over 4 weeks. Both HF-rTMS and LF-rTMS produced comparable early reductions in depression severity, with LF-rTMS showing a greater HAM-D reduction at 4 weeks. HAM-A and PSQI scores also improved at 2 and 4 weeks in both groups, with no significant between-group differences.

Conclusions: Both accelerated HF- and LF-rTMS protocols were associated with improvements in depressive, anxiety and sleep symptoms in patients with MDD, and LF-rTMS may confer more sustained antidepressant effects.

Background: The triglyceride-glucose (TyG) index is linked to metabolic dysfunction that may contribute to depressive symptoms, but its value for population risk stratification is unclear. We aimed to examine TyG-related indices and develop an internally validated prediction model for depressive symptoms in U.S. adults.

Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES). Survey-weighted logistic regression was used to evaluate associations between TyG-related indices, including the TyG-waist-to-height ratio (TyG-WHtR), and depressive symptoms. Predictors were selected using least absolute shrinkage and selection operator (LASSO) regression and entered into a multivariable logistic regression model to construct a nomogram. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and internally validated using bootstrap resampling and leave-one-out cross-validation.

Results: Among 3,870 adults, higher TyG-related indices were associated with greater odds of depressive symptoms, with TyG-WHtR showing the strongest association [highest versus lowest tertile: odds ratio (OR) = 2.37, 95% Confidence Intervals (CI) : 1.59-3.52]. Poverty income ratio, smoking status, and TyG-WHtR were retained in the final parsimonious model, which demonstrated acceptable discrimination (AUC = 0.715) and stable internal performance after bootstrap correction (optimism-corrected AUC = 0.711).

Conclusion: TyG-WHtR was independently associated with depressive symptoms and improved population-level risk stratification in U.S. adults. The internally validated model requires external validation before any clinical implementation.

Background: Altered intrinsic functional connectivity is a well-established marker of borderline personality disorder (BPD). However, recent research suggests that investigating brain dynamics may offer a more detailed perspective on the neural signatures of BPD-related symptoms.

Methods: Resting-state fMRI data were analyzed in female patients with BPD (n = 47) and healthy controls (n = 28) to derive dynamic functional network connectivity (dFNC) indices using both meta-state and cluster-state approaches. Between-group comparisons assessed BPD-related dFNC alterations, while dimensional analyses explored associations between network dynamics and distinct symptom dimensions.

Results: Both meta-state and cluster-state analyses revealed strong associations between symptom dimensions and dynamic range and fluidity. Meta-state analysis indicated that greater emotion regulation difficulties corresponded to an expanded state repertoire, reflecting increased variability in large-scale network configurations. Cluster-state analysis showed that fewer state transitions were associated with heightened borderline symptom severity, greater childhood trauma exposure, and increased dissociative symptoms. Furthermore, childhood trauma and emotion regulation difficulties moderated the relationship between time spent in specific cluster-states and borderline symptom severity.

Conclusion: These findings suggest that reduced dynamic flexibility but increased dynamic range in large-scale brain networks may contribute to core BPD symptoms, particularly in early trauma, emotion dysregulation as well as borderline symptom severity. The results highlight the association between aberrant dFNC and BPD and contribute to a more detailed characterization of neural dynamics relevant to the disorder.

The public health burden of cognitive decline escalates with population aging. While lipid species alterations are associated with cognitive function and Alzheimer's disease (AD), causal evidence remains limited. We applied two-sample Mendelian randomization (TSMR) and Bayesian-weighted MR (BWMR) to investigate the causal effects of lipid species on cognitive function and to assess the bidirectional causal relationships between lipid species and AD. We analyzed genome-wide association study (GWAS) data from large-scale cohorts: AD (East African Development Bank [EADB], N = 487,511); cognitive function (UK Biobank, N = 20,346); and lipid species (THL Biobank, N = 7,174). Analyses were conducted using multiple MR methods, including inverse variance weighting (IVW), BWMR, MR-Egger regression, and false discovery rate (FDR) correction. Sensitivity analyses-MR-Egger intercept test, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), Cochran's Q test, and leave-one-out analysis-were conducted to evaluate horizontal pleiotropy and heterogeneity. TSMR results identified 51 lipid species with causal associations for cognitive function and AD, among which 27 showed protective effects. Conversely, AD was found to influence 17 lipid species, with 14 exhibiting negative effects. These genetically supported findings highlight potential lipid-related targets for preventive and therapeutic interventions aimed at combating cognitive decline.

Background: Headache and brain fog are common after COVID-19 infection, and inflammation and vasculopathy might be the potential underlying mechanisms. This study aimed to delineate the extent and impact of cerebrovascular involvement in patients with persistent long-COVID syndrome in a large Asian cohort.

Methods: We prospectively collected data from patients with persistent (≥ 3 months) post-COVID headache or brain fog, and controls were free from neurological symptoms within 3 months after COVID-19 resolution. The anterior and posterior Focal Cerebral Arteriopathy Severity Score (FCASS) was used to assess the severity of arteriopathy and was modified to account for bilateral involvement (total score: anterior: 0-40; posterior: 0-52). Symptom severity was assessed using monthly headache days, Migraine Disability Assessment (MIDAS), and brain fog severity scale (0-10).

Results: A total of 108 patients (M: F = 27:81) were divided into four groups based on symptoms categories. The presence of more symptom categories was associated with higher anterior-FCASS (combined 6.6 [2.1] vs. headache 3.6 [1.6] vs. brain-fog 4.7 [1.7] vs. control 0.8 [0.6], p < 0.001 by one-way ANOVA) and a posterior-FCASS (combined 5.5 [2.2] vs. headache 4.2 [1.6] vs. brain-fog 4.4 [2.3] vs. control 0.8 [0.8], p < 0.001 by one-way ANOVA). Pure brain- fog was more likely have predominance of anterior circulation involvement than pure headache (80.8% vs. 48.0%, p = 0.020), but the severity of brain fog was correlated with only the posterior-FCASS (Pearson's r = 0.338, p = 0.009) rather than the anterior-FCASS (Pearson's r = 0.173, p = 0.195).

Conclusions: Anterior circulation arteriopathy and hypoperfusion may underlie persistent post-COVID brain fog. However, post-COVID headaches may not depend solely on changes in intracranial vessels.

Borderline Personality Disorder (BPD) is a complex and debilitating condition characterized by limited treatment response and high dropout rates across treatments, even in leading therapeutic approaches, such as Dialectical-Behavioral Therapy (DBT). Given the heterogeneity of BPD symptomatology and the current shift toward dimensional assessments of personality disorders, this study investigates whether dimensional measures according to the DSM-5's Alternative Model for Personality Disorders (AMPD) may serve as additional markers of treatment outcome and dropout. Eighty-five participants with BPD were recruited from a naturalistic inpatient DBT program. Associations between BPD symptoms (Borderline Symptom List, Borderline Personality Disorder Severity Index), personality functioning (Level of Personality Functioning Scale - Brief Form 2.0), and dysfunctional personality traits (Personality Inventory for DSM-5 and ICD-11 - Brief Form Plus) were examined at baseline, as well as their sensitivity to change after DBT. At baseline, both personality functioning and dysfunctional personality traits, except antagonism, were associated with BPD symptoms (all r ≥ 0.4). After 10 weeks of DBT, parallel improvements were observed in personality functioning and dysfunctional personality traits, with significant reductions in negative affectivity and detachment. Elevated baseline levels of negative affectivity and psychoticism were associated with less BPD symptom change. DBT dropout was linked to higher impairment in negative affectivity and anankastia at admission. Personality functioning showed greater sensitivity to change throughout DBT compared to dysfunctional personality traits. These findings emphasize how AMPD may yield benefits beyond categorical BPD diagnoses, potentially contributing to more personalized and effective treatment planning and preventing dropouts.