{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T21:58:07Z","timestamp":1780437487773,"version":"3.54.1"},"reference-count":54,"publisher":"Ubiquity Press, Ltd.","issue":"1","license":[{"start":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T00:00:00Z","timestamp":1693180800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CPSY"],"abstract":"<jats:p>Introduction. Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed an application of Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modelling approach and evaluates its validity using empirical and simulated data. Methods. A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of 9 patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation. Results. Analyses of empirical data showed that DCM accurately captured symptom trajectories for all 9 subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters &gt;= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for 8 of 9 course patterns. When testing two specific course patterns, leave-one-out cross-validation correctly assigned 23 out of 24 virtual subjects. Conclusion. DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment.<\/jats:p>","DOI":"10.5334\/cpsy.94","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T10:30:05Z","timestamp":1693218605000},"source":"Crossref","is-referenced-by-count":4,"title":["Inferring trajectories of psychotic disorders using dynamic causal modeling"],"prefix":"10.5334","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9227-6837","authenticated-orcid":false,"given":"Jingwen","family":"Jin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3610-6619","authenticated-orcid":false,"given":"Peter","family":"Zeidman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7984-8909","authenticated-orcid":false,"given":"Karl J.","family":"Friston","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9569-8381","authenticated-orcid":false,"given":"Roman","family":"Kotov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3285","published-online":{"date-parts":[[2023,8,28]]},"reference":[{"issue":"1\u20132","key":"key20230828062957_B1","first-page":"133","article-title":"Toward a re-definition of subthreshold bipolarity: epidemiology and proposed criteria for bipolar-II, minor bipolar disorders and hypomania","volume":"73","year":"2003","journal-title":"Journal of affective disorders"},{"issue":"3","key":"key20230828062957_B2","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1080\/10705511.2017.1406803","article-title":"Dynamic structural equation models","volume":"25","year":"2018","journal-title":"Structural Equation Modeling: A Multidisciplinary Journal"},{"key":"key20230828062957_B3","volume-title":"Variational algorithms for approximate Bayesian inference","year":"2003"},{"issue":"6","key":"key20230828062957_B4","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1080\/00273171.2017.1373014","article-title":"Network mapping with GIMME","volume":"52","year":"2017","journal-title":"Multivariate behavioral research"},{"key":"key20230828062957_B5","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.nicl.2013.11.002","article-title":"Dissecting psychiatric spectrum disorders by generative embedding","volume":"4","year":"2014","journal-title":"Neuroimage Clin"},{"issue":"2","key":"key20230828062957_B6","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1093\/schbul\/18.2.243","article-title":"The Epidemiology of Psychosis: The Suffolk County Mental Health Project","volume":"18","year":"1992","journal-title":"Schizophrenia bulletin"},{"issue":"3","key":"key20230828062957_B7","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.neuroimage.2007.07.028","article-title":"A Metropolis\u2013Hastings algorithm for dynamic causal models","volume":"38","year":"2007","journal-title":"Neuroimage"},{"issue":"4","key":"key20230828062957_B8","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1093\/schbul\/6.4.606","article-title":"Catamnestic long-term study on the course of life and aging of schizophrenics","volume":"6","year":"1980","journal-title":"Schizophrenia bulletin"},{"issue":"1","key":"key20230828062957_B9","first-page":"1","article-title":"Toward the future of psychiatric diagnosis: the seven pillars of RDoC","volume":"11","year":"2013","journal-title":"BMC medicine"},{"issue":"2","key":"key20230828062957_B10","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.neuroimage.2009.11.062","article-title":"Dynamic causal modelling: a critical review of the biophysical and statistical foundations","volume":"58","year":"2011","journal-title":"Neuroimage"},{"key":"key20230828062957_B11","volume-title":"An introduction to latent variable growth curve modeling: concepts, issues, and applications","year":"2011","edition":"Second"},{"issue":"4","key":"key20230828062957_B12","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1080\/00273171.2018.1454823","article-title":"The Gaussian Graphical Model in Cross-Sectional and Time-Series Data","volume":"53","year":"2018","journal-title":"Multivariate Behav Res"},{"key":"key20230828062957_B13","volume-title":"Statistical mechanics: a set of lectures","year":"2018"},{"key":"key20230828062957_B14","doi-asserted-by":"crossref","unstructured":"First, M. 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