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| Hauptseite > Publikationsdatenbank > Disentangling Brain-Psychopathology Associations: A Systematic Evaluation of Transdiagnostic Bifactor Models |
| Hauptseite > Publikationsdatenbank > Disentangling Brain-Psychopathology Associations: A Systematic Evaluation of Transdiagnostic Bifactor Models |
| Preprint | FZJ-2026-00909 |
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2025
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Please use a persistent id in citations: doi:10.64898/2025.12.21.695029 doi:10.34734/FZJ-2026-00909
Abstract: Understanding the neurobiological basis of mental health disorders remains a central goal in psychiatry. However, identifying robust brain-psychopathology associations with neuroimaging has proven difficult, in part due to substantial heterogeneity within and comorbidity between diagnostic categories. Transdiagnostic bifactor models aim to characterise this structure by separating shared from unique symptom variance, yielding more reliable and potentially more accurate latent dimensions of psychopathology. However, the extent to which these behavioural models improve brain-psychopathology associations remains largely uncharacterised. Using two large developmental cohorts, we compared 11 previously published bifactor models applied to the Child Behaviour Checklist (CBCL) to traditional CBCL summary scores. For both symptom-scoring approaches, we systematically evaluated their reliability and multivariate associations with whole-brain structure (MRI) and function (resting-state fMRI). We found no consistent evidence that bifactor-derived factor scores strengthened reliability or brain-psychopathology associations, relative to summary scores. Whole-brain predictive models revealed broadly distributed neural signatures that were highly similar between corresponding factor and summary score constructs, with general factors and total problems approaching numerical equivalence. Nevertheless, factor scores displayed more distinct neural signatures between general, internalising, and externalising dimensions than did summary scores. Together, these findings suggest that existing CBCL bifactor models of psychopathology do not systematically strengthen the predictive utility of psychiatric neuroimaging, possibly reflecting fundamental limits on the proportion of CBCL symptom variance captured by brain features. While bifactor models may aid in separating neural correlates across constructs, improving phenotypic assessment depth, rather than alternative phenotypic modelling, may provide more tangible improvements to association strength moving forward.
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