{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:34:27Z","timestamp":1771306467623,"version":"3.50.1"},"reference-count":62,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Psychosis is usually preceded by a prodromal phase in which patients are clinically identified as being at in an \u201cAt Risk Mental State\u201d (ARMS). A few studies have demonstrated the feasibility of predicting psychosis transition from an ARMS using structural magnetic resonance imaging (sMRI) data and machine learning (ML) methods. However, the reliability of these findings is unclear due to possible sampling bias. Moreover, the value of genetic and environmental data in predicting transition to psychosis from an ARMS is yet to be explored.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study we aimed to predict transition to psychosis from an ARMS using a combination of ML, sMRI, genome-wide genotypes, and environmental risk factors as predictors, in a sample drawn from a pool of 246 ARMS subjects (60 of whom later transitioned to psychosis). First, the modality-specific values in predicting transition to psychosis were evaluated using several: (a) feature types; (b) feature manipulation strategies; (c) ML algorithms; (d) cross-validation strategies, as well as sample balancing and bootstrapping. Subsequently, the modalities whose at least 60% of the classification models showed an balanced accuracy (BAC) statistically better than chance level were included in a multimodal classification model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results and discussion<\/jats:title><jats:p>Results showed that none of the modalities alone, i.e., neuroimaging, genetic or environmental data, could predict psychosis from an ARMS statistically better than chance and, as such, no multimodal classification model was trained\/tested. These results suggest that the value of structural MRI data and genome-wide genotypes in predicting psychosis from an ARMS, which has been fostered by previous evidence, should be reconsidered.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpsyt.2022.1086038","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T06:42:12Z","timestamp":1674110532000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data"],"prefix":"10.3389","volume":"13","author":[{"given":"V\u00e2nia","family":"Tavares","sequence":"first","affiliation":[]},{"given":"Evangelos","family":"Vassos","sequence":"additional","affiliation":[]},{"given":"Andre","family":"Marquand","sequence":"additional","affiliation":[]},{"given":"James","family":"Stone","sequence":"additional","affiliation":[]},{"given":"Isabel","family":"Valli","sequence":"additional","affiliation":[]},{"given":"Gareth J.","family":"Barker","sequence":"additional","affiliation":[]},{"given":"Hugo","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Diana","family":"Prata","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1212\/CON.0000000000000602","article-title":"Psychosis.","volume":"24","author":"Schrimpf","year":"2018","journal-title":"Contin Lifelong Learn Neurol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1001\/jamapsychiatry.2013.269","article-title":"The psychosis high-risk state.","volume":"70","author":"Fusar-Poli","year":"2013","journal-title":"JAMA Psychiatry."},{"key":"B3","doi-asserted-by":"publisher","first-page":"15067","DOI":"10.1038\/nrdp.2015.67","article-title":"Schizophrenia.","volume":"1","author":"Kahn","year":"2015","journal-title":"Nat Rev Dis Prim."},{"key":"B4","doi-asserted-by":"publisher","first-page":"351","DOI":"10.2174\/138161212799316064","article-title":"Basic symptoms and the prediction of first-episode psychosis.","volume":"18","author":"Schultze-Lutter","year":"2012","journal-title":"Curr Pharm Des."},{"key":"B5","doi-asserted-by":"publisher","first-page":"519","DOI":"10.2147\/NDT.S225643","article-title":"Negative symptoms in schizophrenia: A review and clinical guide for recognition, assessment, and treatment.","volume":"16","author":"Correll","year":"2020","journal-title":"Neuropsychiatr Dis Treat."},{"key":"B6","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s11065-018-9388-2","article-title":"Cognitive deficits in psychotic disorders: A lifespan perspective.","volume":"28","author":"Sheffield","year":"2018","journal-title":"Neuropsychol Rev."},{"key":"B7","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1093\/schbul\/22.2.283","article-title":"Monitoring and care of young people at incipient risk of psychosis.","volume":"22","author":"Yung","year":"1996","journal-title":"Schizophr Bull."},{"key":"B8","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1001\/jamapsychiatry.2021.0830","article-title":"Probability of transition to psychosis in individuals at clinical high risk: An updated meta-analysis.","volume":"78","author":"Salazar De Pablo","year":"2021","journal-title":"JAMA Psychiatry."},{"key":"B9","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1093\/schbul\/sbs060","article-title":"At risk for schizophrenic or affective psychoses? 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