Psychological Medicine最新文献

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.

Background: Accurate and up-to-date epidemiological data on the prevalence and treatment of common mental disorders are essential for evidence-based healthcare policy and resource allocation. However, large-scale, representative epidemiological surveys on common mental disorders in China-particularly those incorporating insomnia disorder and applying the latest diagnostic criteria alongside validated assessment tools-remain notably lacking.

Methods: We conducted a population-based, cross-sectional epidemiological survey to assess the prevalence and treatment of common mental disorders among adults in Beijing, China, using a multistage clustered probability sampling design (n = 10,778). Licensed psychiatrists administered standardized diagnostic interviews based on DSM-5 criteria to assess both lifetime and current mental disorders through a single-stage assessment protocol.

Results: Among all lifetime mental disorders assessed, depressive disorders constituted the most prevalent diagnostic category (7.7%), with major depressive disorder representing the most common specific diagnosis (5.4%). Individuals aged 65 years and older exhibited significantly higher 1-month prevalence of both depressive disorders and insomnia disorder compared with younger age groups. Alcohol-related disorder was more prevalent in men than in women, and in urban residents than in rural residents. Help-seeking patterns revealed a predominant reliance on informal support over professional services among individuals with lifetime mental disorders. Only 13.4% sought help from mental health professionals, and 12.7% received mental health professional treatment.

Conclusions: The improved access to treatment did not translate into a reduction in population-level mental disorder prevalence, which may be attributable to the low rate of professional mental health treatment. Governments must optimize mental healthcare access.

Background: Avoidant/restrictive food intake disorder (ARFID) leads to faltering growth and psychosocial impairment. Three phenotypes can co-occur: fear of aversive consequences of eating (ARFID-fear phenotype), sensory sensitivity, and lack of interest in eating/food. We hypothesized that youth with ARFID, especially ARFID-fear phenotype, would show hyperactivation of fear-related regions in response to ARFID-specific fear images, compared to healthy controls (HC), and activation of these regions would positively correlate with ARFID fear severity.

Methods: Youth (N=103: 76 ARFID, including 20 ARFID-fear phenotype; 27 HC) underwent functional MRI scanning while viewing ARFID-specific fear (e.g. vomiting, choking) versus neutral images. We compared blood-oxygen-level-dependent (BOLD) response in fear-related region of interests (ROI; e.g. amygdala, hippocampus, insula) between ARFID and ARFID-fear phenotype versus HC. We evaluated the association between brain response and ARFID fear severity in ARFID-fear phenotype.

Results: Across individuals, there was a robust bilateral amygdala response to ARFID-specific fear versus neutral images. Compared to HC, ARFID-fear phenotype showed a greater insula response to ARFID-specific fear versus neutral images (p=0.049). There were no other group differences and no significant relationships between BOLD response and ARFID fear severity in ARFID-fear phenotype.

Conclusions: ARFID-specific fear images elicit amygdala responses across individuals, with greater activation in the insula only in ARFID-fear phenotype versus HC. These findings validate the ARFID-specific fear paradigm and highlight the intriguing possibility that, in the ARFID-fear phenotype, universally feared experiences such as choking and vomiting serve as the unconditioned stimulus in developing ARFID and may partially be mediated by the insular cortex.

Background: Deficits in working memory (WM) and attention have a considerable functional impact on people with bipolar disorder (PBD). Understanding the neurocognitive underpinnings of these cognitive constructs might facilitate the discovery of more effective pro-cognitive interventions. Therefore, we employed a paradigm designed for jointly studying attentional control and WM encoding.

Methods: We used a visuospatial change-detection task using four Gabor Patches with differing orientations in 63 euthymic PBD and 76 healthy controls (HCS), which investigated attentional competition during WM encoding. To manipulate bottom-up attention using stimulus salience, two Gabor patches flickered, which were designated as either targets or distractors. To manipulate top-down attention, the Gabor patches were preceded by either a predictive or a non-predictive cue for the target locations.

Results: Across all task conditions, PBD stored significantly less information in visual WM than HCS (significant effect of group). However, we observed no significant group-by-salience or group-by-cue interactions. This indicates that impaired WM was not caused by deficits in attentional control.

Conclusions: While WM was disturbed in PBD, attentional prioritization of salient targets and distractors, as well as the utilization of external top-down cues, were not compromised. Thus, the control of attentional selection appears to be intact at least for our specific manipulation of this cognitive construct. These findings provide valuable clues for models of WM dysfunction in PBD by suggesting that later stages of WM encoding, such as WM consolidation, are likely primarily impaired, while selective attention is not a main source of impairment.

Schizophrenia (SZ), bipolar disorder (BD), and depressive disorder (DEP) are disabling diseases influenced by genetic and environmental factors. Several risk factors have been identified for these disorders in various systematic reviews, meta-analyses, and umbrella reviews. Identifying risk factors for these disorders is essential to be able to target disorder-specific or transdiagnostic interventions. We aimed to systematically review existing meta-analyses on selected risk factors for SZ, BD, and DEP. We systematically searched for meta-analyses of risk factors relating to pregnancy and birth, childhood and adolescence, lifestyle, somatic conditions, infectious agents, and environmental exposures published since 2000. The transdiagnostic comparison included 70 meta-analyses, encompassing results for 55 risk factors that were studied across at least two of the three disorders. In our extensive transdiagnostic umbrella, 74% of reported effect sizes for the risk factors from meta-analyses were statistically significant. Childhood maltreatment was a robust transdiagnostic risk factor for all three disorders. We also found differences in risk factors, for example, pregnancy and birth complications associated strongly with SZ risk, and several somatic conditions were associated with DEP. It should be noted that many meta-analyses were low quality and based on a small number of original studies. More high-quality longitudinal research is needed on many risk factors to be able to evaluate their validity in single outcomes and their potential specificity or non-specificity.

Background: Major depressive disorder (MDD) is closely associated with suicide, which often begins with suicidal ideation (SI). However, the underlying neural mechanisms remain unclear.

Methods: We included 73 MDD patients with SI (MDD-SI), 44 MDD patients without SI (MDD-NSI) and 78 healthy controls (HCs), then compared the amplitude of low-frequency fluctuations (ALFF), functional connectivity (FC), and effective connectivity (EC) differences across groups and analyzed their relationship with SI severity. FC and EC analyses used brain regions with ALFF differences between MDD-SI and MDD-NSI as seed points. ALFF findings were validated using the REST-meta-MDD consortium dataset (N = 1 596, 24 sites). Additionally, we explored the trend of changes in abnormal activity and connectivity of SI and suicidal behavior (SB) in MDD-SI.

Results: Compared to MDD-NSI, MDD-SI showed increased ALFF in the right anterior cingulate cortex (ACC), validated by the REST-meta-MDD consortium dataset. MDD-SI also exhibited reduced FC between the right ACC and the left inferior frontal gyrus and decreased EC from the right ACC to the right fusiform gyrus, which were negatively correlated with the Hamilton Depression Rating Scale (HAMD)-suicidality item scores. Increased EC was observed in MDD-SI from the right ACC to the right cerebellar tonsil and from the left inferior parietal lobule (IPL) to the right ACC, following a progressive increase pattern (HC < MDD-NSI < MDD-SI without SB < MDD-SI with SB).

Conclusions: Increased activity and aberrant connectivity of the ACC may be associated with SI in MDD patients and potentially serve as biomarkers for suicide risk.

Background: Alterations in reward responsiveness represent a key mechanism implicated in youth depression risk. However, not all youth with these alterations develop depression, suggesting the presence of factors that may moderate risk patterns. As socioeconomic disadvantage is also related to youth depression risk, particularly for youth exhibiting altered reward function, this study examined whether indices of family- and neighborhood-level disadvantage interacted with electrocortical reward responsivity to predict depression symptom trajectories across childhood and adolescence.

Methods: Participants included 76 youth (ages 9-16 years) at low and high risk for depression based on maternal history of depression. At baseline, youth completed a monetary reward-guessing task while electroencephalography was recorded to measure the reward positivity (RewP), an event-related potential indexing reward responsiveness. Family and neighborhood disadvantage were assessed using the income-to-needs (ITN) ratio and Area Deprivation Index (ADI), respectively. Self-reported and clinician-rated depression symptoms were assessed across a multiwave, 18-month follow-up.

Results: RewP interacted with family- and neighborhood-level disadvantage to predict self-reported depression symptom trajectories. Specifically, blunted RewP predicted self-reported depression symptom increases for youth with a lower ITN ratio and higher ADI score. A blunted RewP also predicted clinician-rated depression symptom increases for youth living in neighborhoods with higher ADI scores.

Conclusions: Findings suggest that reduced reward responsiveness is a mechanism implicated in future depression risk among youth, specifically in the context of family- and neighborhood-level socioeconomic disadvantage. Interventions that enhance reward response among youth exposed to higher levels of socioeconomic disadvantage may be particularly effective in preventing depression emergence.

Background: The distinction between passive and active suicidal ideation (SI) and their underlying etiologies remains poorly understood. The Interpersonal Theory of Suicide implicates guilt, loneliness, and hopelessness in these SI subtypes, but there is minimal work testing these relationships in real time, capturing clinically meaningful fluctuations in SI. We conducted the first ecological momentary assessment (EMA) study to distinguish between passive and active SI in adolescents, and the first study to evaluate moment-to-moment etiological factors and mediators of passive and active SI in this age group.

Methods: Participants (N = 104) were adolescent psychiatric inpatients (M_age = 15.1; 72.12% female). They completed an EMA protocol including measures of guilt, loneliness, hopelessness, and passive and active SI for four weeks post-discharge. Multilevel modeling was used to evaluate guilt and loneliness, respectively, as predictors of prospective passive and active SI, respectively. We also evaluated whether hopelessness mediated the interaction between guilt and loneliness in predicting future SI. Hopelessness was also evaluated as a mediator between passive and active SI.

Results: Guilt predicted prospective passive and active SI, respectively, whereas loneliness only predicted prospective passive SI. The interaction between guilt and loneliness did not predict active SI, and hopelessness did not mediate the association between guilt and active SI. Passive SI prospectively predicted active SI, but hopelessness did not mediate this association.

Conclusions: Findings suggest that passive and active SI may share overlap but also differences in their etiologies. Their relationship with etiological factors and mediators may differ as a function of temporal scale.

Background: One of the challenges of psychological research is obtaining a sample representative of the general population. One largely overlooked participant characteristic is sub-clinical levels of psychiatric symptoms.

Methods: A series of studies were conducted to assess (i) whether typical psychology study participants had more psychiatric symptoms than the general population, (ii) whether there are sub-groups defined by psychiatric symptoms within the no-diagnosis, no-medication participant pool, and (iii) whether sub-clinical levels of psychiatric symptoms have an effect on standard behavioral tasks. Five UK national datasets (N > 10,000) were compared to data from psychology study participants (Study 1: n = 872; Study 2: n = 43,094; Study 3: n = 267).

Results: Psychology study participants showed significantly higher levels of anxiety and depression and lower well-being, according to four commonly used mental health measures (GHQ-12, PHQ-8, WEMWBS, and WHO-5). Five sub-groups within the psychology study participant group were identified based on symptom levels, ranging from none to significant psychiatric symptoms. These groupings predicted performance on tests of executive function, including the Stroop task and the n-back task, as well as measures of intelligence.

Conclusions: This study demonstrates that standard psychology participant pools are unrepresentative and suggests that a failure to account for psychiatric symptoms when recruiting for any psychological study is likely to negatively impact the reproducibility and generalizability of psychological science.