2026 ECNP Congress

PS04-4026

Transdiagnostic dimensions of psychopathology and cognitive performance in a general population sample

Laila Rida — B. Goulding Mew, P.J. Hellyer, W. Trender, S.C.R. Williams, A. Hampshire · King's College London, Neuroimaging, London, United Kingdom

2026 ECNP Congress

The medical model of psychiatry has continuously been challenged by the diagnostic ambiguity and phenotypic overlap of psychiatric disorders [1, 2]. Altered cognitive profiles are believed to contribute to psychiatric disorders, yet whether these are disorder-specific or agnostic remains unclear. The Research Domain Criteria (RDoC) defines optimal transdiagnostic markers as continuous measures observable in the general population and approaching extremes in clinical conditions [3]. Examining individuals that fall within the right tail of psychiatric dimension distributions in the general population provides the necessary variability to detect subtle patterns that may be hidden in clinical samples. Focusing only on individuals who meet diagnostic criteria risks capturing only the most pronounced expressions of dysfunction, where underlying mechanisms are obscured by comorbidities, physical health, and lifestyle consequences that can accompany mental illness [4, 5].

In a large-scale online citizen science study, we examined the relationship between psychiatric disorders, transdiagnostic psychiatric dimensions, and cognitive function, particularly cognitive control and executive function. Participants completed a set of brief (2-5 minutes each) computerised cognitive tasks online in their own time. These captured several cognitive domains such as working memory, planning, cognitive flexibility, recall, and reasoning. We also examined transdiagnostic trait measures, including personality, repetitive thinking (rumination and worry), and compulsivity, as well as self-reported diagnostic labels and symptom severity across depression, anxiety, and positive psychosis dimensions through a series of questionnaires optimised for use in a general population sample.

We found that associations to diagnostic group means were inconsistent, with those with depression tending to perform better or no worse on average on several tasks. When examining individuals approaching the extremes of depression, anxiety, and positive psychosis symptom distributions, though, performance drop-offs were observed across a broad set of cognitive domains. Overall accuracy differences ranged from ~0.3-0.4 SDs between the highest and lowest symptom frequency groups; effects were smallest for depression and most pronounced for executive-demanding tasks in the psychosis dimension.

Trait measures, including personality, repetitive thinking, and compulsivity, were only weakly associated with performance in isolation. However, these traits moderated the relationship between task performance and psychiatric risk, with certain cognitive domain and trait combinations producing nonlinear effects. For example, greater planning ability paired with more frequent rumination was associated with a greater likelihood of having any psychiatric diagnosis. Moreover, associations between psychiatric vulnerability and cognitive deficits were distinct from those observed in neurological disorders. This suggests the specificity of psychiatric cognitive and trait profiles.

Together, these findings point to a nuanced pattern of cognitive impairment that reflects individual differences in symptom expression and transdiagnostic traits, rather than fitting cleanly within diagnostic categories. These results highlight the utility of dimensional, transdiagnostic approaches for characterising the cognitive correlates of psychiatric risk and emphasise the importance of large, heterogeneous population samples in advancing the mechanistic understanding of psychiatric disorders.

References

  1. Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: The seven pillars of RDoC. BMC Medicine, 11(1), 126. https://doi.org/10.1186/1741-7015-11-126
  2. Nesse, R. M., & Stein, D. J. (2012). Towards a genuinely medical model for psychiatric nosology. BMC Medicine, 10(1), 5. https://doi.org/10.1186/1741-7015-10-5
  3. Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010). Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of Psychiatry, 167(7), 748–751. https://doi.org/10.1176/appi.ajp.2010.09091379
  4. Cambridge, O. R., Knight, M. J., Mills, N., & Baune, B. T. (2018). The clinical relationship between cognitive impairment and psychosocial functioning in major depressive disorder: A systematic review. Psychiatry Research, 269, 157–171. https://doi.org/10.1016/J.PSYCHRES.2018.08.033
  5. De Hert, M., Correll, C. U., Bobes, J., Cetkcovich-Bakmas, M., Cohen, D., Asai, I., Detraux, J., Gautam, S., Moller, H.-J., Ndetei, D. M., Newcomer, J. W., Uwakwe, R., & LEUCHT, S. (2011). Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry, 10(1), 52–77. https://doi.org/10.1002/j.2051-5545.2011.tb00014.x