Psychological Medicine最新文献

Background: Elucidation of transphasic mechanisms (i.e., mechanisms that occur across illness phases) underlying negative symptoms could inform early intervention and prevention efforts and additionally identify treatment targets that could be effective regardless of illness stage. This study examined whether a key reinforcement learning behavioral pattern characterized by reduced difficulty learning from rewards that have been found to underlie negative symptoms in those with a schizophrenia diagnosis also contributes to negative symptoms in those at clinical high-risk (CHR) for psychosis.

Methods: CHR youth (n = 46) and 51 healthy controls (CN) completed an explicit reinforcement learning task with two phases. During the acquisition phase, participants learned to select between pairs of stimuli probabilistically reinforced with feedback indicating receipt of monetary gains or avoidance of losses. Following training, the transfer phase required participants to select between pairs of previously presented stimuli during the acquisition phase and novel stimuli without receiving feedback. These test phase pairings allowed for inferences about the contributions of prediction error and value representation mechanisms to reinforcement learning deficits.

Results: In acquisition, CHR participants displayed impaired learning from gains specifically that were associated with greater negative symptom severity. Transfer performance indicated these acquisition deficits were largely driven by value representation deficits. In addition to negative symptoms, this profile of deficits was associated with a greater risk of conversion to psychosis and lower functioning.

Conclusions: Impairments in positive reinforcement learning, specifically effectively representing reward value, may be an important transphasic mechanism of negative symptoms and a marker of psychosis liability.

Background: Improving patient outcomes will be enhanced by understanding "what works, for whom?" enabling better matching of patients to available treatments. However, answering this "what works, for whom?" question requires sample sizes that exceed those of most individual trials. Conventional methods for combining data across trials, including aggregate-data meta-analysis, suffer from key limitations including difficulty accounting for differences across trials (e.g., comparing "apples to oranges"). Causally interpretable meta-analysis (CI-MA) addresses these limitations by pairing individual-participant-data (IPD) across trials using advancements in transportability methods to extend causal inferences to clinical "target" populations of interest. Combining IPD across trials also requires careful acquisition and harmonization of data, a challenging process for which practical guidance is not well-described in the literature.

Methods: We describe methods and work to date for a large harmonization project in pediatric obsessive-compulsive disorder (OCD) that employs CI-MA.

Results: We review the data acquisition, harmonization, meta-data coding, and IPD analysis processes for Project Harmony, a study that (1) harmonizes 28 randomized controlled trials, along with target data from a clinical sample of treatment-seeking youth ages 4-20 with OCD, and (2) applies CI-MA to examine "what works, for whom?" We also detail dissemination strategies and partner involvement planned throughout the project to enhance the future clinical utility of CI-MA findings. Data harmonization took approximately 125 hours per trial (3,000 hours total), which was considerably higher than preliminary projections.

Conclusions: Applying CI-MA to harmonize data has the potential to answer "what works for whom?" in pediatric OCD.

Artificial intelligence (AI) has been recently applied to different mental health illnesses and healthcare domains. This systematic review presents the application of AI in mental health in the domains of diagnosis, monitoring, and intervention. A database search (CCTR, CINAHL, PsycINFO, PubMed, and Scopus) was conducted from inception to February 2024, and a total of 85 relevant studies were included according to preestablished inclusion criteria. The AI methods most frequently used were support vector machine and random forest for diagnosis, machine learning for monitoring, and AI chatbot for intervention. AI tools appeared to be accurate in detecting, classifying, and predicting the risk of mental health conditions as well as predicting treatment response and monitoring the ongoing prognosis of mental health disorders. Future directions should focus on developing more diverse and robust datasets and on enhancing the transparency and interpretability of AI models to improve clinical practice.

Background: Few studies have examined the long-term outcomes of first-episode psychosis (FEP) among patients beyond symptomatic and functional remission. This study aimed to broaden the scope of outcome indicators by examining the relationships between 12 outcomes of FEP patients at 20.9 years after their initial diagnosis.

Methods: At follow-up, 220 out of 550 original patients underwent a new assessment. Twelve outcomes were assessed via semistructured interviews and complementary scales: symptom severity, functional impairment, personal recovery, social disadvantage, physical health, number of suicide attempts, number of episodes, current drug use, dose-years of antipsychotics (DYAps), cognitive impairment, motor abnormalities, and DSM-5 final diagnosis. The relationships between these outcome measures were investigated using Spearman's correlation analysis and exploratory factor analysis, while the specific connections between outcomes were ascertained using network analysis.

Results: The outcomes were significantly correlated; specifically, symptom severity, functioning, and personal recovery showed the strongest correlations. Exploratory factor analysis of the 12 outcomes revealed two factors, with 11 of the 12 outcomes loading on the first factor. Network analysis revealed that symptom severity, functioning, social disadvantage, diagnosis, cognitive impairment, DYAps, and number of episodes were the most interconnected outcomes.

Conclusion: Network analysis provided new insights into the heterogeneity between outcomes among patients with FEP. By considering outcomes beyond symptom severity, the rich net of interconnections elucidated herein can facilitate the development of interventions that target potentially modifiable outcomes and generalize their impact on the most interconnected outcomes.

Major depressive disorder (MDD) is characterized by changes in appetite and body weight as well as blunted reward sensitivity (‘anhedonia’). However, it is not well understood which mechanisms are driving changes in reward sensitivity, specifically regarding food. Here, we used a sample of 117 participants (54 patients with MDD and 63 healthy control participants [HCPs]) who completed a food cue reactivity task with ratings of wanting and liking for 60 food and 20 non-food items. To evaluate which components of the food may contribute to altered ratings in depression, we tested for associations with macronutrients of the depicted items. In line with previous studies, we found reduced ratings of food wanting (p = .003) but not liking (p = .23) in patients with MDD compared to matched HCPs. Adding macronutrient composition to the models of wanting and liking substantially improved their fit (ps < .001). Compared to carbohydrate-rich foods, patients with MDD reported lower liking and wanting ratings for high-fat and high-protein foods. Moreover, patients with MDD showed weaker correlations in their preferences for carbohydrate- versus fat- or protein-rich foods (ps < .001), pointing to potential disturbances in metabolic signaling. To conclude, our results suggest that depression-related alterations in food reward ratings are more specific to the macronutrient composition of the food than previously anticipated, hinting at disturbances in gut–brain signaling. These findings raise the intriguing question of whether interventions targeting the gut could help normalize aberrant reward signals for foods rich in fat or protein.

Background: Psychotic disorders are characterized by emotion regulation abnormalities that predict greater symptom severity and poor functional outcomes. However, it is unclear whether these abnormalities also occur in individuals at clinically high risk for psychosis (CHR). The current study used ecological momentary assessment (EMA) to address this question and examined the nature of abnormalities at three stages of emotion regulation (identification, selection, implementation).

Methods: Participants included 120 CHR and 59 CN who completed 1 week of EMA surveys evaluating emotional experience, emotion regulation, context, and symptoms. Multi-level models examined concurrent and time-lagged effects.

Results: CHR evidenced elevated state negative affect and abnormalities at all three stages of emotion regulation. At the identification stage (i.e., determining the need to regulate), regulatory attempts were made too frequently and with too much effort at low levels of negative affect and not frequently enough and with insufficient effort at high levels of negative affect. Selection stage abnormalities (i.e., choosing the exact strategy to attempt based on context) were characterized by increased frequency of selecting individual strategies and greater polyregulation (i.e., use of multiple strategies concurrently). Implementation stage (i.e., executing the selected strategy) abnormalities were indicated by being less effective at decreasing the intensity of negative affect from time t to t + 1.

Conclusions: It is not only heightened stress reactivity that confers risk for psychosis, but also abnormalities in applying emotion regulation strategies to control the stress response. The profile of abnormalities observed in CHR is similar to schizophrenia, suggesting treatment targets that transcend phases of psychotic illness.

Background: Depressive disorders are the most common diagnosis among individuals who die by suicide, and intermittent theta-burst stimulation (iTBS) is a noninvasive treatment for those with difficult-to-treat depression who are at higher risk for suicide. Previous data suggests that pairing iTBS with D-cycloserine, a partial N-methyl-D-aspartate (NMDA) receptor agonist, improves antidepressant outcomes. However, its impact on suicide risk is not known.

Methods: We examine suicidal ideation and implicit suicide risk after iTBS+D-cycloserine in two clinical trials (open-label trial [n = 12] and randomized placebo-controlled trial [RCT, n = 50]) involving adults with major depressive disorder and the acute effects of D-cycloserine on implicit suicide risk in a crossover trial (n = 18). Implicit suicide risk was assessed using the computerized death/suicide implicit association test (IAT), and depressive symptoms and suicidal ideation were assessed using the clinician-rated Montgomery-Asberg Depression Rating Scale (MADRS).

Results: Open-label iTBS+D-cycloserine was associated with a rapid reduction in suicidal ideation, and iTBS+D-cycloserine was superior to iTBS+placebo in reducing suicidal ideation. Similarly, open-label iTBS+D-cycloserine was associated with decreased implicit suicide risk as measured by the death/suicide IAT, and iTBS+D-cycloserine was associated with greater decreases in death/suicide IAT scores compared to iTBS+placebo. A single acute dose of D-cycloserine in the absence of iTBS had no effect on implicit suicide risk.

Conclusions: Adjunctive D-cycloserine with iTBS is a promising strategy to reduce suicidal ideation and implicit suicide risk in depression.

Background: The hypothesized cognitive model of negative symptoms, proposed nearly twenty years ago, is the most prevalent psychological framework for conceptualizing negative symptoms in schizophrenia spectrum disorders (SSDs). The aim of this study was to comprehensively validate the model for the first time, specifically by quantifying the relationships between negative symptom severity and all related dysfunctional beliefs.

Methods: A systematic search was conducted using MEDLINE and PsychINFO, supplemented by manual reviews of reference lists and Google Scholar. Eligible studies were peer-reviewed with data on the direct cross-sectional association between negative symptoms and at least one relevant dysfunctional belief in SSD patients. Screening and data extraction were completed by independent reviewers. Random-effects meta-analyses were performed to pool effect size estimates of z-transformed Pearson's r correlations. Moderators of these relationships, as well as subset analyses for negative symptom domains and measurement instruments, were also assessed.

Results: Significant effects emerged for the relationships between negative symptoms and defeatist performance beliefs (k = 38, n = 2808), r = 0.23 (95% CI, 0.18-0.27), asocial beliefs (k = 8, n = 578), r = 0.21 (95% CI, 0.12-0.28), low expectancies for success (k = 55, n = 5664), r = -0.21 (95% CI, -0.15 - -0.26), low expectancies for pleasure (k = 5, n = 249), r = -0.19 (95% CI, -0.06 - -0.31), and internalized stigma (k = 81, n = 9766), r = 0.17 (95% CI, 0.12-0.22), but not perception of limited resources (k = 10, n = 463), r = 0.08 (95% CI, -0.13 - 0.27).

Conclusions: This meta-analysis provides support for the cognitive model of negative symptoms. The identification of specific dysfunctional beliefs associated with negative symptoms is essential for the development of precision-based cognitive-behavioral interventions.

Background: Maternal depression is associated with difficulties in understanding and adequately responding to children's emotional signals. Consequently, the interaction between mother and child is often disturbed. However, little is known about the neural correlates of these parenting difficulties. Motivated by increasing evidence of the amygdala's important role in mediating maternal behavior, we investigated amygdala responses to child sad and happy faces in mothers with remitted major depression disorder (rMDD) relative to healthy controls.

Methods: We used the sensitivity subscale of the emotional availability scales and functional magnetic resonance imaging in 61 rMDD and 27 healthy mothers to examine the effect of maternal sensitivity on mothers' amygdala responses to their children's affective facial expressions.

Results: For mothers with rMDD relative to controls, we observed decreased maternal sensitivity when interacting with their child. They also showed reduced amygdala responses to child affective faces that were associated with lower maternal sensitivity. Connectivity analysis revealed that this blunted amygdala response in rMDD mothers was functionally correlated with reduced activation in higher-order medial prefrontal areas.

Conclusions: Our results contribute toward a better understanding of the detrimental effects of lifetime depression on maternal sensitivity and associated brain responses. By targeting region-specific neural activation patterns, these results are a first step toward improving the prediction, prevention, and treatment of depression-related negative effects on mother-child interaction.

Background: The emotion regulation network (ERN) in the brain provides a framework for understanding the neuropathology of affective disorders. Although previous neuroimaging studies have investigated the neurobiological correlates of the ERN in major depressive disorder (MDD), whether patients with MDD exhibit abnormal functional connectivity (FC) patterns in the ERN and whether the abnormal FC in the ERN can serve as a therapeutic response signature remain unclear.

Methods: A large functional magnetic resonance imaging dataset comprising 709 patients with MDD and 725 healthy controls (HCs) recruited across five sites was analyzed. Using a seed-based FC approach, we first investigated the group differences in whole-brain resting-state FC of the 14 ERN seeds between participants with and without MDD. Furthermore, an independent sample (45 MDD patients) was used to evaluate the relationship between the aforementioned abnormal FC in the ERN and symptom improvement after 8 weeks of antidepressant monotherapy.

Results: Compared to the HCs, patients with MDD exhibited aberrant FC between 7 ERN seeds and several cortical and subcortical areas, including the bilateral middle temporal gyrus, bilateral occipital gyrus, right thalamus, calcarine cortex, middle frontal gyrus, and the bilateral superior temporal gyrus. In an independent sample, these aberrant FCs in the ERN were negatively correlated with the reduction rate of the HAMD₁₇ score among MDD patients.

Conclusions: These results might extend our understanding of the neurobiological underpinnings underlying unadaptable or inflexible emotional processing in MDD patients and help to elucidate the mechanisms of therapeutic response.