Comparing Data-Driven Subtypes of Depression Informed by Clinical and Neuroimaging Data: A Registered Report

dc.contributor.authorHannon K.
dc.contributor.authorJarukasemkit S.
dc.contributor.authorBalogh L.
dc.contributor.authorAhmad F.
dc.contributor.authorLenzini P.
dc.contributor.authorSotiras A.
dc.contributor.authorBijsterbosch J.D.
dc.contributor.correspondenceHannon K.
dc.contributor.otherMahidol University
dc.date.accessioned2025-04-08T18:14:07Z
dc.date.available2025-04-08T18:14:07Z
dc.date.issued2025-05-01
dc.description.abstractBackground: Efforts to elucidate subtypes within depression have yet to establish a consensus. In this study, we aimed to rigorously compare different subtyping approaches in the same participant space to quantitatively test agreement across subtyping approaches and determine whether the different approaches are sensitive to different sources of heterogeneity in depression. Methods: We implemented 6 different data-driven subtyping methods developed in previous work using the same UK Biobank participants (n = 2276 participants with depression, n = 1595 healthy control participants). The 6 approaches include 2 symptom-based, 2 structural neuroimaging–based, and 2 functional neuroimaging–based techniques. The resulting subtypes were compared based on participant assignment, stability, and sensitivity to subtype differences in demographics, general health, clinical characteristics, neuroimaging, trauma, cognition, genetics, and inflammation markers. Results: We found almost no agreement between the resulting subtypes of the 6 approaches (mean adjusted Rand index [ARI] = 0.006), even within data domains. This finding was largely driven by differences in input feature set (mean ARI = 0.005) rather than clustering algorithm (mean ARI = 0.23). However, each approach had relatively high internal stability across bootstraps (ARI = 0.36–0.89); most approaches performed above null; and most approaches were sensitive to relevant phenotypes within their data domain. Conclusions: Despite marginal overlap between approaches, we found the subtyping approaches to be internally consistent. These results explain why previous studies found strong evidence for subtypes within their analysis but with very little convergence between studies. We recommend that in future work, investigators incorporate systematic comparisons between their approach and alternative/previous approaches to facilitate consensus on depression subtypes.
dc.identifier.citationBiological Psychiatry Global Open Science Vol.5 No.3 (2025)
dc.identifier.doi10.1016/j.bpsgos.2025.100473
dc.identifier.eissn26671743
dc.identifier.scopus2-s2.0-105001471836
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/109363
dc.rights.holderSCOPUS
dc.subjectNursing
dc.subjectNeuroscience
dc.subjectMedicine
dc.titleComparing Data-Driven Subtypes of Depression Informed by Clinical and Neuroimaging Data: A Registered Report
dc.typeArticle
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105001471836&origin=inward
oaire.citation.issue3
oaire.citation.titleBiological Psychiatry Global Open Science
oaire.citation.volume5
oairecerif.author.affiliationUniversity of Minnesota Twin Cities
oairecerif.author.affiliationWashington University School of Medicine in St. Louis
oairecerif.author.affiliationFaculty of Medicine Ramathibodi Hospital, Mahidol University
oairecerif.author.affiliationWashington University in St. Louis
oairecerif.author.affiliationUniversiteit van Amsterdam

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