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dc.contributor.authorRichards, Derek
dc.date.accessioned2023-11-28T10:46:48Z
dc.date.available2023-11-28T10:46:48Z
dc.date.issued2023
dc.date.submitted2023en
dc.identifier.citationLee, Chi Tak, Kelley, Sean W., Palacios, Jorge, Richards, Derek, Gillan, Claire M., Estimating the prognostic value of cross-sectional network connectivity for treatment response in depression, Psychological Medicine, 2023 Jun 7:1-10en
dc.identifier.issn0033-2917
dc.identifier.otherY
dc.descriptionPUBLISHEDen
dc.description.abstractBackground: Tightly connected symptom networks have previously been linked to treatment resistance, but most findings come from small-sample studies comparing single responder v. non-responder networks. We aimed to estimate the association between baseline network connectivity and treatment response in a large sample and benchmark its prognostic value against baseline symptom severity and variance. Methods: N = 40 518 patients receiving treatment for depression in routine care in England from 2015-2020 were analysed. Cross-sectional networks were constructed using the Patient Health Questionnaire-9 (PHQ-9) for responders and non-responders (N = 20 259 each). To conduct parametric tests investigating the contribution of PHQ-9 sum score mean and variance to connectivity differences, networks were constructed for 160 independent subsamples of responders and non-responders (80 each, n = 250 per sample). Results: The baseline non-responder network was more connected than responders (3.15 v. 2.70, S = 0.44, p < 0.001), but effects were small, requiring n = 750 per group to have 85% power. Parametric analyses revealed baseline network connectivity, PHQ-9 sum score mean, and PHQ-9 sum score variance were correlated (r = 0.20-0.58, all p < 0.001). Both PHQ-9 sum score mean (β = -1.79, s.e. = 0.07, p < 0.001), and PHQ-9 sum score variance (β = -1.67, s.e. = 0.09, p < 0.001) had larger effect sizes for predicting response than connectivity (β = -1.35, s.e. = 0.12, p < 0.001). The association between connectivity and response disappeared when PHQ-9 sum score variance was accounted for (β = -0.28, s.e. = 0.19, p = 0.14). We replicated these results in patients completing longer treatment (8-12 weeks, N = 22 952) and using anxiety symptom networks (N = 70 620). Conclusions: The association between baseline network connectivity and treatment response may be largely due to differences in baseline score variance.en
dc.format.extent1-10en
dc.language.isoenen
dc.relation.ispartofseriesPsychological Medicine;
dc.rightsYen
dc.subjectTreatment responseen
dc.subjectTreatment prognosisen
dc.subjectTreatment predictionen
dc.subjectReal-worlden
dc.subjectNetwork connectivityen
dc.subjectAnxietyen
dc.subjectCentralityen
dc.subjectDepressionen
dc.subjectInternet-delivered cognitive behavioural therapyen
dc.subjectNetwork analysisen
dc.titleEstimating the prognostic value of cross-sectional network connectivity for treatment response in depressionen
dc.typeJournal Articleen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/drichard
dc.identifier.rssinternalid259105
dc.identifier.doihttp://dx.doi.org/10.1017/S0033291723001368
dc.rights.ecaccessrightsopenAccess
dc.subject.TCDTagClinical Psychologyen
dc.subject.TCDTagPsychologyen
dc.identifier.orcid_id0000-0003-0871-4078
dc.status.accessibleNen
dc.identifier.urihttp://hdl.handle.net/2262/104209


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