Psychological Medicine最新文献

Background: Although the short-term preventive effects of mHealth consultation intervention on postpartum depressive symptoms have been demonstrated, the long-term effects and role of alleviating loneliness on depressive symptoms remain unclear.

Methods: This follow-up study extended our previous trial, which ended at three months postpartum, by continuing observation to 12 months. Participants in the original trial were randomized to the mHealth group (n = 365) or the usual care group (n = 369). Women in the mHealth group had access to free, unlimited mHealth consultation services with healthcare professionals from enrollment through four months postpartum. The primary outcome of this study was the risk of elevated postpartum depressive symptoms at 12 months post-delivery (Edinburgh Postnatal Depression Scale score of ≥9). The mediation effect of alleviating loneliness on the primary outcome was also evaluated, using the UCLA loneliness scale at three months postpartum.

Results: A total of 515 women completed the follow-up questionnaires (mHealth group, 253/365; usual care group, 262/369; 70.2% of the original participants). Compared to the usual care group, the mHealth group had a lower risk of elevated postpartum depressive symptoms at 12 months post-delivery (36/253 [14.2%] vs. 55/262 [21.0%], risk ratio: 0.68 [95% confidence interval: 0.46-0.99]). Mediation analysis showed that reducing loneliness at three months post-delivery mediated approximately 20% of the total effect of the intervention on depressive symptoms 12 months post-delivery.

Conclusions: mHealth consultation services provided during the early perinatal period may help alleviate depressive symptoms at 12 months postpartum.

Background: Low self-esteem is an important and potentially modifiable risk factor for the development and outcome of psychotic disorders. The factors involved in low self-esteem in psychotic disorders are not yet fully understood. The current study aims to investigate the cross-sectional and longitudinal associations between (changes in) self-esteem and severity of psychotic symptoms, internalized stigma, negative reaction to antipsychotics, personal recovery, childhood bullying, childhood trauma, and social support in symptomatically remitted first-episode psychosis (FEP) patients.

Methods: Data from the ongoing longitudinal Handling Antipsychotic Medication: Long-term Evaluation of Targeted Treatment study were used. Participants were in symptomatic remission for 3-6 months after the FEP. Cross-sectional associations (N = 299) were investigated through Pearson's correlations, and longitudinal changes (N = 238) were investigated via linear regressions with inverse probability weighting.

Results: Cross-sectionally, we found that lower self-esteem was related to higher severity of symptoms, higher internalized stigma, higher childhood trauma (specifically emotional neglect), higher childhood bullying, more negative side effects of antipsychotic medication, lower personal recovery, and lower social support. Longitudinally, contrary to our hypothesis, we found that higher baseline internalized stigma, higher childhood trauma (specifically emotional abuse), and a higher baseline negative subjective reaction to antipsychotics predicted an increase in self-esteem after 6 months. Furthermore, a decrease in psychotic symptoms, internalized stigma, and negative subjective reaction to antipsychotics, and an increase in social support predicted an increase in self-esteem.

Conclusions: Early intervention programs for psychotic disorders should target factors related to changes in self-esteem. This might improve self-esteem and thereby promote recovery.

Background: Early life adversity (ELA) is common and cross-sectionally associated with brain gray matter structure, including cortical thickness, cortical surface area, and subcortical volumes in childhood. However, to which degree ELA influences the trajectory of gray matter macrostructural and microstructural development during childhood and adolescence remains largely unexplored.

Methods: We included 6414 participants from the Adolescent Brain Cognitive Development study at ages 9-11, where 1923 were followed to ages 11-13. We used linear mixed-effects models to test for associations between MRI-derived longitudinal measures of gray matter macro- (cortical thickness, surface area, subcortical volume) or microstructure (T1w/T2w ratio) and trauma exposure, parental acceptance, household abuse, and being resilient or susceptible to trauma in terms of developing an internalizing disorder.

Results: At ages 9-11, higher levels of parental acceptance, trauma exposure, and being trauma resilient were associated with lower levels of cortical thickness. In contrast, being trauma susceptible was negatively related to hippocampal volume and cortical surface area. Longitudinally, more parental acceptance at baseline was associated with more cortical thinning between ages 9-11 and 11-13, while more household abuse was associated with less change in T1w/T2w ratio over time.

Conclusions: Parental acceptance and trauma resilience are linked to accelerated pace of apparent cortical thinning in youth aged 9-13 years, while household abuse is associated with slower microstructural development, as reflected by smaller longitudinal changes in the T1w/T2w ratio. Threat and deprivation may be distinctly associated with gray matter developmental trajectories in late childhood.

Background: Structural brain alterations in bipolar disorder (BD) have been widely reported, yet the hierarchical organization of cortical morphometric networks and their molecular and cognitive underpinnings remain unclear.

Methods: We applied the morphometric inverse divergence (MIND) network approach to structural MRI data from 49 BD patients and 119 healthy controls. Principal MIND gradients were derived using diffusion map embedding, followed by multiscale analyses linking gradient alterations to neurotransmitter systems, cognitive-behavioral domains, and transcriptomic profiles from the Allen Human Brain Atlas. Validation was performed in three independent, cross-scanner, cross-race, and cross-age validation datasets.

Results: Bipolar disorder patients showed significant principal gradient alterations in the left rostral middle frontal and lateral occipital cortices, with network-level decreases in the ventral attention and motor networks and increases in frontoparietal and visual networks. Gradient alterations spatially correlated with acetylcholine (VAChT) and GABA (GABA_A/BZ) systems, and were associated with cognitive processes involving executive control and visual attention. Transcriptomic analyses identified gene sets enriched for BD-related GWAS loci, expressed predominantly in excitatory and inhibitory neurons, astrocytes, and oligodendrocytes, with preferential enrichment in cortical layers III-IV and developmental windows spanning early fetal to young adulthood.

Conclusions: These findings reveal disrupted hierarchical cortical organization in BD and link macroscale morphometric alterations to specific neurotransmitter systems and transcriptional architectures. The MIND gradient emerges as a potential biomarker bridging structural disruptions with molecular and cognitive mechanisms in BD.

Background: Decades of research have identified a strong association between heavy cannabis use and schizophrenia (SCZ), with evidence of correlated genetic factors. However, many studies on the genetic relationship between cannabis use and psychosis have lacked data on both phenotypes within the same individuals, creating challenges due to unmeasured confounding. We aimed to address this by using multimodal data from the All of Us Research Program, which contains genetic data as well as information on SCZ diagnosis and cannabis use.

Methods: We tested the association between cannabis use disorder (CUD) and SCZ polygenic scores (PGSs) with SCZ and heavy cannabis use. We tested models where both CUD and SCZ PGSs were included as joint predictors of heavy cannabis use and SCZ case status. We defined three sets of cases based on comorbidities: relaxed (assessing for only the primary condition), strict (excluding comorbidity), and dual-comorbidity.

Results: CUD and SCZ polygenic liability were independently associated with heavy cannabis use; the SCZ PGS effect was very modest. In contrast, both SCZ and CUD PGSs were independently associated with SCZ, with independent significant effects of CUD PGS. Polygenic liability to CUD was associated with SCZ in individuals without a documented history of cannabis use, suggesting widespread pleiotropy.

Conclusions: These findings underscore the need for comprehensive models that integrate genetic risk factors for heavy cannabis use to advance our understanding of SCZ etiology.

Background: Antidepressants are the primary treatment for major depressive disorder (MDD), yet their precise neurobiological mechanisms remain incompletely understood. This study aimed to elucidate neural differences between medicated and unmedicated MDD patients by analyzing resting-state functional magnetic resonance imaging data.

Methods: We conducted a coordinate-based meta-analysis, complemented by behavioral, genetic, and neurotransmitter-level evaluations to identify potential therapeutic targets and diagnostic biomarkers. Using seed-based d-mapping with permutation of subject images (SDM-PSI), we assessed brain activation changes associated with antidepressant treatment. The identified regions were further characterized using large-scale molecular and functional brain databases.

Results: A total of 59 studies on unmedicated MDD (2,618 patients, 2,486 controls) and 15 studies on medicated MDD (541 patients, 483 controls) were included. The meta-analysis revealed significantly increased activation in the left striatum among medicated patients, a region linked to cognitive functions such as memory and perception. Gene expression analysis highlighted SLC5A7 and prolactin (PRL) as key genes in this region, while neurotransmitter mapping showed associations with serotonin (5-HT1a, 5-HT2a) and dopamine (D1, D2) receptors. Additionally, reduced activation in the left middle occipital gyrus (MOG) was observed across both medicated and unmedicated groups. This region, implicated in recognition and face processing, showed high expression of TFAP2B and PRL and was associated with serotonin and norepinephrine transporter distributions.

Conclusions: These findings suggest that the left striatum may represent a core neurofunctional target of antidepressant treatment, while the left MOG may serve as a stable neurobiological marker for MDD diagnosis, independent of pharmacological status.

Background: People with severe mental illness (SMI) (schizophrenia-spectrum and bipolar disorders) experience a 15-20-year reduction in life expectancy. The role of social determinants, including that of social exclusion, in contributing to excess mortality in SMI remains underexplored.

Methods: Retrospective cohort study, comprising 8098 people with clinician-diagnosed SMI, matched to 581,209 population controls, followed for 5.7 years using person-level linked health/ census records. A social exclusion index was derived from census indicators: marital status, social isolation, economic inactivity, education, tenure, housing stability, and material assets.

Results: Social exclusion was more common in SMI than in controls and strongly associated with higher mortality. Relative to the least socially excluded controls, adjusted hazard ratios (aHR) for mortality in SMI were: 16-44 years: aHR 7.58 (95% CI: 2.75-20.86) in the least socially excluded, increasing to 12.34 (7.92-19.24) in the most excluded; 45-64 years: 3.34 (1.98-5.64) [least excluded] increasing to 6.58 (5.32-8.14) [most excluded]; 65+ years: 2.71 (1.90-3.86) [least excluded], increasing to 3.07 (2.48-3.80)[most excluded]. Excess mortality among those with SMI was pronounced at younger ages if never married; by mid-life if living alone or economically inactive; and at 65+ years in those with SMI living alone, renting, or with no car ownership. Economic inactivity and lack of qualifications accounted for 16-35% of SMI mortality.

Conclusions: Social exclusion is an under-recognized contributor to premature mortality in SMI. Targeting social determinants through novel socially-focused interventions could improve survival in people with SMI.

Background: High uncertainty in recent global health, geopolitical, and climate crises has been proposed as one important driver of the rise in youth mental health problems. This makes intolerance of uncertainty - a transdiagnostic risk factor for mental health problems - a promising target for intervention.

Methods: This study presents a novel single-session online training that took a synergistic mindset approach to promote uncertainty-as-adaptive and growth mindsets. The novel Uncertainty-Mindset Training was compared with Psychoeducation and No-Training control groups in 259 older adolescents/emerging adults (18-to-24-year-olds).

Results: The Uncertainty-Mindset Training reduced intolerance of uncertainty, anxiety symptoms, and depression symptoms 1 month later. Importantly, the clinical gains were mediated by reductions in intolerance of uncertainty.

Conclusions: Given that this ultra-brief training can be delivered at scale globally and at no cost to the users, it shows promise for significant public health impacts.

Background: Machine learning (ML) models show promise in predicting post-traumatic stress disorder (PTSD) treatment outcomes, but it is unknown how their predictions compare to those of clinicians. This study directly compared the accuracy of clinicians' predictions of patient treatment outcomes with those of three ML models.

Methods: Twenty clinicians providing cognitive processing therapy repeatedly predicted outcomes for 194 veterans. We compared their accuracy against three ML models on two key endpoints: clinically meaningful symptom reduction (≥10-point PCL-5 decrease) and posttreatment severity (final PCL-5 < 33). Clinician predictions were compared against a recurrent neural network, a mixed-effects random forest, and a generalized linear mixed-effects model. We analyzed prediction accuracy and the association between clinician confidence and accuracy using logistic mixed-effects models.

Results: ML models were significantly more accurate than clinicians at predicting whether a patient's posttreatment PCL-5 score would be below 33 (p < .001). However, no significant difference in accuracy was found for predicting a ≥10-point symptom reduction (p = .734). Clinician confidence increased throughout treatment and was significantly associated with greater prediction accuracy for both outcomes (ORs = 1.06, ps < .001).

Conclusions: ML models can outperform clinicians in predicting posttreatment symptom severity, particularly early in treatment, suggesting they could be a useful tool for identifying patients at risk for suboptimal outcomes. However, ML models were not superior in predicting symptom reduction, where clinicians also performed at a high level. Findings support the selective use of ML to enhance, rather than replace, clinical judgment in PTSD treatment.