Preliminary testing of a prespecified liability architecture for autism: theory-guided pathogenetic triad models outperform strength-matched alternatives.

Background: Neuropsychiatric conditions are heterogeneous and mechanistically diverse, yet predictive modeling studies commonly aggregate features without evaluating prespecified liability architectures. The Pathogenetic Triad (PT) is a multilevel framework proposing that diagnostic outcomes reflect the joint configuration of a trait-related domain, cognitive capacity (CC), and neuropathological burden (NB). In autism, the trait-related domain corresponds to autistic personality (AP). We evaluated this framework using out-of-sample prediction as a structured empirical test of architectural coherence rather than as a purely data-driven exercise in classification. Methods: We analyzed a case-comparison cohort ( Results: Across multiverse specifications, low-dimensional PT models ranked among the strongest models of comparable size and achieved discrimination broadly comparable to higher-dimensional models within this dataset. Matched comparisons indicated that including all three PT domains was associated with systematic advantages relative to alternatives with similar univariate input strength, consistent with complementary configurational information relating to case status. Conclusions: These findings provide preliminary evidence supporting the Pathogenetic Triad as a multilevel architecture of autism liability. Although based on a small and demographically restricted cohort, this densely characterized dataset permitted explicit comparison of theory-guided and atheoretical model spaces. The results illustrate how prespecified multilevel frameworks can be operationalized and empirically evaluated in neuropsychiatric samples.