Research articles

Using plasma proteome profiles from over 53,000 UK Biobank participants, Zhang et al. examined proteins associated with suicidal behavior and investigated pathways that could explain the association

Wang et al. analyzed data from the Detroit Neighborhood Health Study to explore links between social adversity, post-traumatic stress and epigenetic regulation through blood-derived micro-RNA profiles.

This study investigates whether genetic predisposition to systemic inflammation affects cortical thinning trajectories and the risk for psychopathology in adolescents, using data from the Adolescent Brain Cognitive Development Study.

This research identifies two neural factors linked to externalizing and internalizing symptoms through a longitudinal imaging-genetic cohort. Distinct neural configurations and cognitive-behavioral relevance highlight the need for tailored therapeutic strategies addressing psychiatric comorbidity across developmental stages.

In this study, the authors perform a comprehensive series of analyses to assess the role of disruptions in brain state dynamics in major depressive disorder, using data from the UK Biobank.

This study utilized a longitudinal cohort of adolescents to identify distinct brain signatures linked to ADHD symptom trajectories, revealing that specific cortical and subcortical changes correlate with symptom persistence, remission and emergence, enhancing predictive capabilities for ADHD outcomes.

Chen et al. assessed the prevalence and risk factors for treatment-resistant postpartum depression using nationwide data from the Swedish National Medical Birth Register.

Zhang et al. examine associations between wildfire-specific PM_2.5 and emergency department visits for mental disorders in children across 845 communities in Australia, Brazil and Canada over a 15-year period.

Nuzum et al. used cross-sectional data from the UK PROTECT study to evaluate the relationship between high autistic traits and suicidality in older adults.

This systematic review and meta-analysis synthesizes the latest evidence on diffusion tensor imaging metrics of fractional anisotropy and mean diffusivity across psychosis spectrum disorders. The authors highlight a consistent pattern of fractional anisotropy reduction in the corpus callosum, with age and gender further strengthening these results.

Pender et al. evaluated depression and anxiety symptom trajectories in autistic adults receiving routine therapy in UK primary care.

In this longitudinal cohort study, Wang et al. examine how depression and recall of adverse childhood experiences interact over time among Chinese university students.

This study identifies key neurocognitive domains that distinguish patients with schizophrenia from healthy individuals using machine learning. Analyzing data from 1,304 participants, it demonstrates that verbal learning and emotion identification effectively classify conditions, promoting efficient neurocognitive profiling strategies.

Disease heterogeneity complicates precision medicine, which focuses on single conditions and ignores shared mechanisms. Here the authors introduce ‘pan-disease’ analysis using a deep learning model on multi-organ data, identifying 11 AI-derived biomarkers that reveal new therapeutic targets and pathways, enhancing patient stratification for disease risk monitoring and drug discovery.

The authors conducted a systematic review and meta-analysis of 62 studies, including more than 4,400 participants across 21 countries, to investigate the effects of nature exposure on self-reported pain.

This study addresses opioid misuse prediction by integrating physiological data and electronic health records. Utilizing personalized deep-learning models, it achieves a high accuracy in risk assessment through entropy feature extraction and relevance-based temporal fusion, demonstrating effective intervention potential.

Chen et al. examined how genetic risk interacts with neighborhood environmental exposures to influence psychotic-like experiences in children from the ABCD cohort study.

This research developed and compared firearm-specific and method-agnostic machine-learning models using data from 800,579 Army veterans, revealing that model choice and intervention thresholds impact predictive accuracy and fairness, guiding tailored suicide prevention efforts.

In this two-nation administrative register study (~5 million individuals), mental health conditions were linked to subsequent unintentional, self-harm and assault injuries. These results highlight the need for targeted injury prevention strategies.

In this study analyzing data from 8,690,286 individuals in the Netherlands, autism significantly increased the risks for various cardiometabolic conditions. Cox proportional hazards models demonstrated heightened hazard ratios, emphasizing the importance of monitoring these health issues in people with autism.