Abstract
Adolescence is a period of significant anatomical and functional brain changes, and complex interactions occur between mental health risk factors. The Longitudinal Adolescent Brain Study commenced in 2018, to monitor environmental and psychosocial factors influencing mental health in 500 adolescents, for 5 years. Participants are recruited at age 12 from the community in Australia’s Sunshine Coast region. In this baseline, cross-sectional study of N = 64 participants, we draw on the network perspective, conceptualising mental disorders as causal systems of interacting entities, to propose a Bayesian network (BN) model of lifestyle and psychosocial variables influencing chances of individuals being psychologically well or experiencing psychological distress. Sensitivity analysis of network priors revealed that psychological distress (Kessler-10) was most affected by eating behaviour. Unhealthy eating increased the chance of moderate psychological distress by 600%. Low social connectedness increased the chance of severe psychological disorder by 200%. Certainty for psychological wellness required 33% decrease in unhealthy eating behaviours, 11% decrease in low social connectedness, and 9% reduction in less physical activity. BN can augment clinician judgement in mental disorders as probabilistic decision support systems. The full potential of BN methodology in a complex systems approach to psychopathology has yet to be realised.
Highlights
Adolescence is a period of significant anatomical and functional brain changes, and complex interactions occur between mental health risk factors
Seven participants had formal diagnoses for a developmental disorder—four participants had a diagnosis of autism spectrum disorder and three participants had been diagnosed as having ADHD
Using Bayesian analyses, this study sought to explicate the complex interactions between influences on mental health
Summary
Adolescence is a period of significant anatomical and functional brain changes, and complex interactions occur between mental health risk factors. Participants are recruited at age 12 from the community in Australia’s Sunshine Coast region In this baseline, cross-sectional study of N = 64 participants, we draw on the network perspective, conceptualising mental disorders as causal systems of interacting entities, to propose a Bayesian network (BN) model of lifestyle and psychosocial variables influencing chances of individuals being psychologically well or experiencing psychological distress. Bayesian networks (BN) offer a systems approach to modelling risk and supporting decision making in complex domains, such as mental health trajectories[5,6]. The approach is based on the view that mental disorders arise as a result of complex interactions between psychological, biological and sociological elements, in conjunction with risk factors and symptoms. Bayesian methods are able to produce reasonable results even with small to moderate sample sizes, when robust prior information is a vailable[14,15]
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