Semi-supervised ICA of functional connectivity for psychiatric nosology

Thomas M. Menino Convention & Exhibition Center 

A fundamental challenge in psychiatric research is the lack of a coherent, empirically grounded framework for psychiatric nosology. Even within a single diagnostic category such as autism spectrum disorder (ASD), individuals exhibit substantial heterogeneity in symptoms, developmental trajectories, and neurobiological profiles. Traditional categorical definitions often fail to capture the continuous nature of underlying neurodevelopmental variation, motivating ongoing shifts toward dimensional classification systems. However, conventional statistical approaches typically assume multivariate normality, which may obscure complex, non-normal latent structures that characterize ASD-related variation.
To address this issue, we propose a semi-supervised independent component analysis (ICA)–based projection pursuit framework to identify latent axes of psychopathology based on patterns of statistical non-normality, such as skewness, multimodality, or U-shaped distributions. By integrating clinical diagnostic labels with functional connectivity data, the method aims to guide the ICA-based projections toward components that are both statistically non-Gaussian (due to psychopathology heterogeneity) and diagnostic-informative. The framework will be evaluated through simulation studies and applied to the ABIDE dataset to assess its utility in ASD nosology.