Uncovering Affective Dynamic Mechanisms in Mental Health: a bio-behavioural data driven approach

Mental health disorders like depression affect one in four individuals and cost the UK alone over £105 billion annually. Atypical emotional function is a key transdiagnostic factor in mental health, with conditions like depression characterised by shifts in habitual patterns of emotion experience and expression. Despite this, we lack a comprehensive understanding of the underlying emotional mechanisms, which is crucial for improving diagnosis and treatment. Traditional methods for studying emotional function rely on self-reports which are inadequate for capturing the dynamic nature of emotional experiences and how they vary in health and disease. This research will explore a novel approach to uncover objective bio-behavioural markers of typical and abnormal emotional function in depression, leveraging advances in wearable biosensors and machine learning to generate and analyse novel datasets in both laboratory and naturalistic contexts. These insights will enhance our understanding of emotional mechanisms in mental health and inform new approaches for research, diagnostic and treatment. The project will also stimulate interdisciplinary synergies with academic and industry partners at the forefront of wearable bio-behavioural sensing and data-driven mental-health research. These collaborations will build research capacity to accelerate the understanding and discovery of bio-behavioural mechanisms for mental health research.