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Gupta and colleagues use multi-omic analyses of neuroimaging and fecal metabolites to identify brain–gut–microbiome disruptions in response to discrimination that may affect preference for unhealthy eating patterns and predispose individuals experiencing discrimination-related stress to obesity.
A study drawing on the expansive UK Biobank dataset to explore the lifestyle, genetic and biological underpinnings of mental health highlights the influence of lifestyle on depression risk and suggests that detailed investigations into lifestyle factors, in conjunction with psychological aspects and advanced analysis methods, may reveal a deeper understanding of the complexities of mental health.
A large-scale prospective cohort study with 287,282 participants revealed that the integration of multiple healthy lifestyle factors (a favorable lifestyle) was associated with a lower risk of depression. Categorizing participants on the basis of their polygenic risk scores for depression demonstrated that the effect of this favorable lifestyle persisted even among participants with high genetic susceptibility.
In this study Stubbs et al. find that neuroimaging neuroimaging abnormalities across substance use disorders map to a common brain network that is similar across imaging modalities and substance use categories.
In this exposome-wide association study using the FinnTwin12 cohort, Wang et al. show that familial component of social exposure has a significant association with depressive symptoms in late adolescence and early adulthood.
The authors conducted a systematic review and meta-analysis to identify predictors of placebo response in repetitive transcranial magnetic stimulation treatment for depression.
In this retrospective study, Wen and colleagues used data from the Adolescent Brain and Cognitive Development cohort study to investigate the neurobiological, psychological and social characteristics that are associated with self-injurious thoughts and behaviours transitions in children.
The role of personality in brain health remains unclear. A systematic search demonstrated that lower levels of warmth, diligence, sociability and curiosity, and a higher level of nervousness, were predictive of increased risk of brain disorders. Brain structure, inflammation and lipid metabolism partially explain the associations between personality and brain disorders.
Zhao, Yang et al. used UK Biobank data to examine the role of lifestyle factors in depression. They conducted multiple analyses and estimated underlying mechanisms related to brain structure, immunometabolism and genetics.
The authors used region of interest (ROI)-to-ROI analysis to investigate functional connectivity patterns in patients with post-traumatic stress disorder (PTSD) and its dissociative subtype compared with healthy controls.
Personalized medicine has made substantial strides in treating cancer and rare genetic disorders by leveraging advances in genomics, yet psychiatry has lagged behind. The complexity of psychiatric disorders, owing to heterogeneity, polygenicity and environmental and epigenetic effects, calls for varied approaches in achieving personalization.
In this cross-sectional study, Blain and colleagues show that sensitivity to diverse visual, cognitive and social, putatively intrinsic rewarding stimuli is partly domain general and is linked to affective aspects of mental health unlike sensitivity to monetary rewards.
Using a large dataset of approximately 300,000 individuals from the UK Biobank, Zhang, Deng, Li et al. investigated the relationships between personality traits, brain health, inflammation and metabolites.
Using resting-state functional MRI, we revealed a dysfunction pattern in the white matter of people with four major psychiatric disorders. This pattern is distinct from structural changes and has specific molecular and genetic bases.
Catarino et al. used real-world data from the UK healthcare system to perform an economic evaluation comparing the cost-effectiveness of in-person and internet-delivered cognitive behavioral therapy for mood and anxiety disorders.
In this Article, Singh and coauthors put forth a new machine-learning approach to evaluate inclusion and exclusion criteria from psychiatry abstracts to automate systematic reviews.
The authors use multimodal magnetic resonance imaging to investigate microstructural alterations and functional deficits in white matter in a multi-disorder sample (patients with schizophrenia, major depressive disorder, bipolar disorder or obsessive–compulsive disorder) when compared with a healthy control sample.
By integrating neuroimaging and multi-omics data, we established links between individual genetic variations and macro- to microscale brain circuit dysfunctions, specifically in corticocortical and corticostriatal circuits, that contribute to the diverse clinical manifestations of schizophrenia. These findings advance our understanding of the disease’s heterogeneity and offer potential treatment insights.
Using an integrated analysis on three independent large human datasets, Wang et al. map macroscale dysconnectivity in schizophrenia onto layer- and cell-type-specific microscale alterations. The authors identify different alterations of corticocortical and corticostriatal connectivity in schizophrenia and their relationship to different symptom dimensions and functional domains.