Translational Psychiatry最新文献

Importance: As psychedelic-assisted therapies gain clinical legitimacy, their cultural portability remains underexamined. While promising results have emerged from trials on existential distress in life-limiting illness, most protocols reflect Euro-American values. The expansion of these therapies calls for a pivot toward cultural humility and renewed attention to what constitutes a "good death" in cultural worlds. This commentary explores these questions through the case of Chinese palliative care.

Observations: Clinical trials of psilocybin and LSD assisted therapy demonstrate significant reductions in depression, anxiety, and demoralization among patients with life-limiting illness. However, psychosocial and cultural factors-including family-centered decision-making, spiritual beliefs, and stigma-will likely impact treatments in complex ways. The evolving healthcare framework of cultural humility emphasizes ongoing self-reflection, relational sensitivity towards power sharing, and openness to diverse worldviews and lived experiences of patients. This posture is particularly important in psychedelic therapy where patient experiences are sensitive to settings, relational processes, and meaning-making.

Relevance and conclusion: Regulatory openings in Australia and several European and U.S. jurisdictions are accelerating clinical interest in psychedelic therapy, yet the cultural life of these therapies present important challenges. The Chinese example illustrates how stigma, trust, and relational dimensions will likely crystallize differently, reminding researchers and clinicians that efficacy is not solely a biochemical or therapeutic question but also a cultural one. Addressing the translational gap of cultural diversity in psychedelic therapy can benefit from a stance of humility towards how situated beliefs, social norms, and clinical practices interact with psychedelic pharmacology.

Patient-derived brain organoids have emerged as a powerful model for investigating the mechanisms underlying neurological and psychiatric disorders. They provide novel insights into autism spectrum disorder (ASD), a heterogeneous neurodevelopmental condition whose underlying mechanisms remain poorly understood. Recent advancements in generating electrophysiological functional 3D brain organoids enable the study of molecular and network-level neuronal activity. Here, we aimed to characterize the neurophysiological underpinnings of ASD by comparing electrophysiological properties of brain organoids derived from eleven individuals diagnosed with autism spectrum disorder, 10 with monogenic syndromic ASD across five genetic subtypes, and 1 with idiopathic ASD, to organoids derived from 4 neurotypical control individuals. We identified distinct differences in baseline activity (resting state) and evoked responses (synaptic plasticity and network dynamics) across ASD subgroups. To comprehensively assess these differences, we applied dimensionality reduction (principal component analysis, PCA) to integrate multiple electrophysiological features into a unified framework. Our findings reveal subtype-specific neurophysiological alterations in ASD brain organoids, offering mechanistic insights into ASD heterogeneity and potential applications for early diagnostics, drug screening, and therapeutic development.

Background: According to the diathesis-stress model, genetic liability and environmental exposures interact in the pathogenesis of depression. Polygenic risk scores for depression (PRS_D) based on large-scale genome-wide association studies have opened new avenues for investigating gene-environment interaction (GxE) beyond candidate gene studies. To the best of our knowledge, this is the first systematic review of studies that have taken a polygenic score approach to study GxE interaction effects on depression phenotypes.

Methods: Based on a preregistered, systematic literature search according to PRISMA guidelines, 56 studies were considered for qualitative analysis. Respective studies investigated a broad range of adverse and protective environmental exposures across the lifespan, e.g., trauma, stressful life events, social environments and (un)healthy lifestyle factors, using cross-sectional and longitudinal designs.

Results: While most studies reported significant main effects of an individual's PRS_D and different environmental exposures on depression phenotypes, the overall evidence for GxE interactions was considerably heterogeneous. Findings of significant PRS_DxE interactions mostly stem from large cohort studies comprising > 40000 participants, in particular, when recent environmental exposures were considered.

Conclusion: Two general conclusions can be drawn from this review. First, PRS_DxE interactions, if at all, add a small amount of explained variance in depression phenotypes to the corresponding additive model and may thus require large samples to be reliably detected. Second, in a considerable number of studies, different environmental exposures were found to depend on an individual's PRS_D, indicating significant gene-environment correlation. We further discuss limitations, future directions and potential clinical relevance of PRSxE research in depression.

Gamma-band neural oscillations are critically involved in working memory and are disrupted in schizophrenia. Transcranial alternating current stimulation (tACS) at gamma frequency is a promising noninvasive approach to restore oscillatory synchrony and enhance cognition. This randomized, double-blind trial tested whether 40 Hz tACS targeting frontoparietal networks modulates gamma-band activity and connectivity during working memory, and whether these electrophysiological changes relate to cognition in schizophrenia. Patients with schizophrenia (n = 33) were randomized to 10 sessions of active or sham tACS over the left dorsolateral prefrontal cortex (F3) and right parietal cortex (P4), with cognition assessed using standardized neurocognitive measures (MATRICS Consensus Cognitive Battery, MCCB) and an n-back working-memory task. EEG during an n-back task was recorded pre- and post-intervention to assess gamma power, phase-locking value (PLV), and phase-amplitude coupling (PAC). A significant Group × Time interaction indicated that 1-back minus 0-back PLV increased in the active group but not in sham (P = 0.048, Cohen's d = 1.08). For PAC, a significant interaction showed that delta-high gamma coupling at F3 remained stable in the active group but declined in sham (P = 0.036, Cohen's d = 1.00). There was no significant correlation with n-back measures of working memory, but an exploratory significant finding linking this modulation to visual learning at 4-week follow-up. No significant group differences were found for MCCB total scores; however, a significant Group × Time interaction emerged for 0-back accuracy during EEG recording (P = 0.029, Cohen's d = 1.19). These findings demonstrate that 40 Hz tACS can enhance and preserve gamma synchrony in frontoparietal circuits during working memory. The maintained delta-gamma coupling in our exploratory findings on visual learning may suggest a relationship to sustained improvements in cognition over time, but needs additional confirmation.

The accuracy of grey-matter predictors of depression has remained limited. In this study, brain-based predictors of major depressive disorder (MDD) were trained using machine-learning (Best Linear Unbiased Predictors [BLUP]) and deep-learning (ResNet3D) techniques applied to high-dimensional (voxel-wise) grey-matter structure extracted from T1-weighted structural MRI. The training sample comprised 987 MDD cases and 3934 controls from the UK Biobank. Predictors were evaluated in an independent sub-cohort of 483 MDD cases and 1939 controls from the UK Biobank and replicated in a clinical cohort (DEP-ARREST CLIN) of 64 cases and 32 controls. In the UK Biobank, the BLUP predictor showed a significant association with MDD status (AUC = 0.57; OR = 1.28 [1.15-1.43]; p-value = 1.1×10^-5), which was confirmed in both males and females. By partitioning the BLUP predictor by brain regions of interest (ROI), we found nominal significance supporting the contribution of previously identified MDD-related ROIs (e.g. hippocampus and amygdala), though none passed multiple testing correction. The BLUP predictor overlapped partially with a polygenic score (PGS) of major depression (AUC = 0.65) but also captured a nominally significant signal that was not captured by the genetic score (combined AUC = 0.66, p-value = 0.024 when compared to PGS alone). No association passed multiple testing correction in the DEP-ARREST CLIN cohort, likely due to the small sample size. In contrast, the deep-learning predictor was not associated with MDD after multiple testing corrections. We estimated the morphometricity of MDD to be 0.061, implying limited potential of a brain-based predictor based on grey-matter structure (maximal AUC = 0.64). While the modest AUC values reiterate the challenge of developing brain-based MDD predictors for clinical applications, our predictors inform future research to explore brain-based relationships between MDD and comorbidities.

Addictive drugs, notably opioid drugs, have significant societal implications, yet the cellular and molecular mechanisms underpinning rewarding effects remain largely elusive. Noncoding transcripts, including the microRNAs (miRNAs), are pivotal regulators of diverse biological functions. Notably, the microRNAs miR-132 and miR-212, arising from a shared noncoding transcript, have links to several psychiatric conditions, including cocaine addiction. However, their contribution to opiate rewarding remains speculative. In this study, we discovered that repeated morphine administration decreases the expression of miR-132/212 in the ventral tegmental area (VTA) and induces a concurrent upregulation of the dopamine transporter (DAT). Using a luciferase reporter assay, we found that the DAT coding gene, SLC6A3 mRNA 3'UTR, is a direct target of miR-132/212. These miRNAs negatively regulated both mRNA expression and protein levels of DAT in vitro. This was corroborated by in vivo studies which revealed that behavioral experiments using a morphine-induced conditioned place preference (CPP) paradigm showed that suppression of miR-132/212 in the VTA facilitated the formation of morphine-induced CPP. Conversely, overexpression of miR-132 attenuated morphine preference in male and female adult rats, as well as adolescents. In sum, our findings uncover a regulatory mechanism wherein miR-132/212 modulates morphine induced reward behavior by fine-tuning DAT expression at the posttranscriptional level, providing a potential therapeutic target of rewarding effects.

Suicide is a significant public health problem that usually co-occurs with major psychiatric disorders. Suicidal behaviors have heritability of 30-50%, and the largest genome-wide association studies identified 12 loci linked to suicide attempts (SA). These findings indicate shared genetic architecture among SA and psychiatric disorders. We analyzed public GWAS summary statistics of SA, major depressive disorder (MDD), bipolar disorder (BIP), schizophrenia (SCZ), and attention deficit hyperactivity disorder (ADHD) to quantify genetic overlap using statistical genetics methods: MiXeR for polygenic overlap, LAVA for locus-specific genetic correlations, and HyPrColoc for multi-trait colocalization. MTAG and conditional false discovery rate (condFDR) identified SA- associated loci, while conjunctional false discovery rate (conjFDR) identified shared loci with psychiatric disorders. Additionally, we used polygenic risk scores (PRS) calculated in the UK Biobank to validate associations between genetic liabilities of psychiatric disorders and SA. SA involve approximately 6.9k (SD = 1.5k) risk variants with substantial overlap between psychiatric disorders, ranging from 53.2% with SCZ to 82.1% with BIP. Using MTAG, condFDR, and conjFDR, we identified 14 and 48 novel risk loci for SA, respectively. Through conjFDR, we identified 78 loci associated with SA and major psychiatric disorders, including one locus shared across all five traits. Genes linked to SA were enriched in synaptic components and signaling pathways. Despite significant genetic overlap, the SA PRS was the single strongest predictor of SA, followed by the MDD and ADHD PRS. The genetic overlap reflects potential common comorbidities complicating the identification of biological processes unique to SA, instead reflecting a complex genetic framework shared with psychiatric disorders.

Conventional functional connectivity of blood oxygenation level-dependent (BOLD) signals varies with Alzheimer's disease (AD) progression. However, it is unable to describe how white matter (WM) is engaged in brain networks. In this study, we introduced a novel communication connectivity metric, which was defined as the triple-wise correlation coefficient between BOLD signals from pairs of gray matter volume and white matter bundles, to investigate the change of signal transition through WM bundles. A total of 169 participants with longitudinal resting-state fMRI data from the ADNI dataset were included, which consisted of 44 cognitively normal (CN), 58 early mild cognitive impairment (EMCI), 45 late MCI (LMCI), and 22 AD. Cross-sectional analyses at baseline and longitudinal within-group comparisons were conducted to examine changes in pattern correlation coefficients (CC) between 2D graphs across the AD continuum. In the cross-sectional study, the 2D graph of the CN group showed moderate correlation with those of the EMCI and LMCI groups, whereas these associations generally declined in the AD dementia group. Bootstrapping test showed that the pattern CC for the right retrolenticular part of internal capsule (RLIC.R) and posterior corona radiata (PCR.R) significantly declined in the EMCI, LMCI, and AD groups for both cross-sectional and longitudinal studies. These results demonstrated that signal transmission in RLIC.R and PCR.R has great potential to be markers in the early diagnosis of AD and tracking the progression of AD. Communication connectivity based on rs-fMRI is a promising tool for investigating WM signal transmission alterations in AD.

Traditional subjective measures are limited in the insight they provide into underlying behavioral differences associated with autism and, accordingly, their ability to predict diagnosis. Performance-based measures offer an attractive alternative, being designed to capture neuropsychological constructs more directly and objectively. However, due to the heterogeneity of autism, differences in any one specific neuropsychological domain are inconsistently detected. Meanwhile, protracted wait times for diagnostic interviews delay access to care, highlighting the importance of developing better methods for identifying individuals likely to be autistic and understanding the associated behavioral differences. We administered a battery of online tasks measuring multisensory perception, emotion recognition, and executive function to a large group of autistic and non-autistic adults. We then used machine learning to classify participants and reveal which factors from the resulting dataset were most predictive of diagnosis. Not only were these measures able to predict autism in a late-diagnosed population known to be particularly difficult to identify, their combination with the most popular screening questionnaire enhanced its predictive accuracy (reaching 92% together). This indicates that performance-based measures may be a promising means of predicting autism, providing complementary information to existing screening questionnaires. Many variables in which significant group differences were not detected had predictive value in combination, suggesting complex latent relationships associated with autism. Machine learning's ability to harness these connections and pinpoint the most crucial features for prediction could allow optimization of a screening tool that offers a unique marriage of predictive accuracy and accessibility.

Major depressive disorder remains a debilitating mental health disorder affecting millions worldwide, with growing prevalence among adolescents. Recent studies highlight the critical role of the somatosensory cortex in the neuropathology of depression, including structural alterations that impair cortical function. This study investigates the prophylactic effects of ayahuasca, a classic psychedelic brew, on morphological changes in the somatosensory cortex induced by chronic stress in juvenile male non-human primates (Callithrix jacchus). Using a model of social isolation to simulate chronic stress, we employed stereological techniques to assess neuronal volume, density, and cortical organization in three groups: a family group (FG), an isolated group (IG), and an ayahuasca-treated group (AG). Ayahuasca was administered before and during the isolation period. Results revealed a significant reduction in neuronal volume in the IG compared to the FG, while the AG exhibited neuronal volumes comparable to FG, suggesting a prophylactic effect of ayahuasca. Although differences in neuronal density and cortical volume could not be statistically confirmed, trends indicated potential preservation of cortical structure in the AG. These preliminary findings underscore ayahuasca's potential to mitigate stress-induced cortical atrophy and highlight its influence on neural plasticity. Future research should expand sample sizes, incorporate female subjects, and investigate molecular mechanisms underlying these structural changes. This work provides foundational evidence for exploring ayahuasca as a novel therapeutic strategy for stress-related psychiatric disorders, particularly in adolescent populations.