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@ARTICLE{Gell:1052292,
      author       = {Gell, Martin and Hoffmann, Mauricio S. and Moore, Tyler M.
                      and Nikolaidis, Aki and Gur, Ruben C. and Salum, Giovanni A.
                      and Milham, Michael P. and Langner, Robert and Müller,
                      Veronika I. and Eickhoff, Simon B. and Satterthwaite,
                      Theodore D. and Tervo-Clemmens, Brenden},
      title        = {{D}isentangling {B}rain-{P}sychopathology {A}ssociations:
                      {A} {S}ystematic {E}valuation of {T}ransdiagnostic
                      {B}ifactor {M}odels},
      reportid     = {FZJ-2026-00909},
      year         = {2025},
      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.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.64898/2025.12.21.695029},
      url          = {https://juser.fz-juelich.de/record/1052292},
}