Multilevel Risk Profiles in Early-Stage Psychiatric Disorders Predictive of Functioning Trajectories

Brisbane Convention & Exhibition Centre 

Psychiatric disorders often emerge during adolescence and early adulthood, a critical neurodevelopmental period characterized by heightened vulnerability to environmental influences. Early identification of individual risk profiles and timely interventions are therefore essential to improve long-term psychosocial and occupational functioning. However, the heterogeneity of risk and protective factors and lack of reliable biomarkers complicate this task. Neurodevelopmental pathways, driven by genetic predisposition and environmental stressors, contribute to clinical symptoms, cognitive deficits, and psychosocial dysfunction.
This study introduces a novel unsupervised machine learning method, multiblock sparse partial least squares (MB-SPLS), to integrate genetic, neuroanatomical, and clinical data. The multimodal approach provides a more comprehensive understanding of the multi-layered complexity of early-stage psychiatric disorders. Here, we aim to identify clinically relevant risk signatures to predict longitudinal functioning trajectories.