{"data":{"id":"10.6084/m9.figshare.c.2121344","type":"dois","attributes":{"doi":"10.6084/m9.figshare.c.2121344","prefix":"10.6084","suffix":"m9.figshare.c.2121344","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Ramyead, Avinash","nameType":"Personal","givenName":"Avinash","familyName":"Ramyead","affiliation":[],"nameIdentifiers":[]},{"name":"Studerus, Erich","nameType":"Personal","givenName":"Erich","familyName":"Studerus","affiliation":[],"nameIdentifiers":[]},{"name":"Kometer, Michael","nameType":"Personal","givenName":"Michael","familyName":"Kometer","affiliation":[],"nameIdentifiers":[]},{"name":"Uttinger, Martina","nameType":"Personal","givenName":"Martina","familyName":"Uttinger","affiliation":[],"nameIdentifiers":[]},{"name":"Gschwandtner, Ute","nameType":"Personal","givenName":"Ute","familyName":"Gschwandtner","affiliation":[],"nameIdentifiers":[]},{"name":"Fuhr, Peter","nameType":"Personal","givenName":"Peter","familyName":"Fuhr","affiliation":[],"nameIdentifiers":[]},{"name":"Riecher-Rössler, Anita","nameType":"Personal","givenName":"Anita","familyName":"Riecher-Rössler","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients"}],"publisher":"Figshare","container":{},"publicationYear":2015,"subjects":[{"subject":"69999 Biological Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"80699 Information Systems not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"19999 Mathematical Sciences not elsewhere classified","subjectScheme":"FOR"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Mathematics","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Plant Biology"}],"contributors":[],"dates":[{"date":"2015-12-05","dateType":"Created"},{"date":"2016-05-21","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"Collection","resourceType":"Collection","resourceTypeGeneral":"Collection"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.3109/15622975.2015.1083614","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"CC-BY","rightsUri":"http://creativecommons.org/licenses/by/3.0/us"}],"descriptions":[{"description":"\u003ci\u003eObjectives\u003c/i\u003e: This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients. \u003ci\u003eMethods\u003c/i\u003e: We assessed the individualised prediction of psychosis by detecting specific patterns of beta and gamma oscillations using machine-learning algorithms. Prediction models were trained and tested on 53 neuroleptic-naïve patients with a clinical high-risk for psychosis. Of these, 18 later transitioned to psychosis. All patients were followed up for at least 3 years. For an honest estimation of the generalisation capacity, the predictive performance of the models was assessed in unseen test cases using repeated nested cross-validation. \u003ci\u003eResults\u003c/i\u003e: Transition to psychosis could be predicted from current-source density (CSD; area under the curve [AUC] = 0.77), but not from lagged phase synchronicity data (LPS; AUC = 0.56). Combining both modalities did not improve the predictive accuracy (AUC = 0.78). The left superior temporal gyrus, the left inferior parietal lobule and the precuneus most strongly contributed to the prediction of psychosis. \u003ci\u003eConclusions\u003c/i\u003e: Our results suggest that CSD measurements extracted from clinical resting state EEG can help to improve the prediction of psychosis on a single-subject level.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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