Journal Volume: 10
Journal Issue: 1
Journal Year: 2020
Articles in SafetyLit: 18

Altered anterior cingulate glutamatergic metabolism in depressed adolescents with current suicidal ideation

Childhood trauma, suicide risk and inflammatory phenotypes of depression: insights from monocyte gene expression

Computational causal discovery for post-traumatic stress in police officers

Development of an early-warning system for high-risk patients for suicide attempt using deep learning and electronic health records

Do AKT1, COMT and FAAH influence reports of acute cannabis intoxication experiences in patients with first episode psychosis, controls and young adult cannabis users?

Dopamine: from prediction error to psychotherapy

Educational attainment reduces the risk of suicide attempt among individuals with and without psychiatric disorders independent of cognition: a bidirectional and multivariable Mendelian randomization study with more than 815,000 participants

Electroconvulsive therapy modulates functional interactions between submodules of the emotion regulation network in major depressive disorder

Emotional remodeling with oxytocin durably rescues trauma-induced behavioral and neuro-morphological changes in rats: a promising treatment for PTSD

Genome-wide DNA methylation meta-analysis in the brains of suicide completers

Genomic influences on self-reported childhood maltreatment

Identifying mental health status using deep neural network trained by visual metrics

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

Intrinsic connectomes underlying response to trauma-focused psychotherapy in post-traumatic stress disorder

Machine learning for suicide risk prediction in children and adolescents with electronic health records

Polygenic risk for anxiety influences anxiety comorbidity and suicidal behavior in bipolar disorder

The intergenerational transmission of suicidal behavior: an offspring of siblings study

Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples