Neurobiology in Mental Health publishes research that advances a mechanistic and translational understanding of psychiatric and mental health conditions. The journal is centred on uncovering the biological basis of mental health and leveraging these insights to inform diagnosis, stratification, and intervention. We prioritise work that integrates across levels of analysis and bridges experimental systems with clinical application. Areas of interest include, but are not limited to: Biological mechanisms of mental health and disease, spanning molecular, cellular, genetic, cognitive, computational, and systems neuroscience. This includes studies of pathophysiology, immune and metabolic processes, biomarkers, and neurobiological, computational, or microbiome-related pathways that shape risk, resilience, and clinical outcomes. Translational neuroscience, linking mechanistic discovery to clinical impact. Relevant approaches include experimental medicine, biomarker development and validation, and early-phase clinical trials. We particularly encourage studies with a defined path from discovery to application, including those using advanced animal models, parallel human paradigms, or digital twin frameworks. Psychopharmacology, encompassing preclinical and clinical investigations of therapeutic mechanisms, efficacy, safety, and treatment response, alongside neurochemical, circuit-level, and physiological correlates of pharmacological interventions, including rapid-acting therapies. Neuromodulation and interventional psychiatry, encompassing preclinical and clinical investigations of therapeutic mechanisms, efficacy, safety, and treatment response. We welcome studies that interrogate mechanisms of action, optimise therapeutic targeting, and advance precision interventions, including adaptive stimulation approaches. Work integrating mechanistic insights from stimulation or leveraging direct biological measures of the human brain to inform causal models of brain–behaviour relationships is encouraged. Computational psychiatry and artificial intelligence, focused on the development of quantitative and mechanistic models of brain, behaviour, and clinical phenomena. We prioritise work that generates testable, biologically grounded hypotheses and links computational constructs to neural systems and clinical outcomes. We are also interested in novel uses of artificial intelligence in the modelling, mechanisms, therapy development, or consequences of mental health. Psychosocial and behavioural interventions with biological grounding, including psychotherapy, cognitive and behavioural therapies, digital interventions and exercise studied alongside neurobiological measures to characterise mechanisms of action and effects on brain function. Clinical and population-level studies that inform diagnosis, prognosis, prevention, and treatment. This includes longitudinal, stratified, and real-world investigations, particularly those integrating biological, computational, and clinical data to address heterogeneity, treatment response, and health disparities.