Journal article
Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort
Simon Hartmann, Micah Cearns, Christos Pantelis, Dominic Dwyer, Blake Cavve, Enda Byrne, Isabelle Scott, Hok Pan Yuen, Caroline Gao, Kelly Allott, Ashleigh Lin, Stephen J Wood, Johanna TW Wigman, G Paul Amminger, Patrick D McGorry, Alison R Yung, Barnaby Nelson, Scott R Clark
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging | Elsevier | Published : 2024
Abstract
Background: Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging. Methods: In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality—cognition, cortical structure information, and the neuroanatomical measure of brain age gap—to a previously developed clinical model using functioning and duration of symptoms prior to service..
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Grants
Awarded by National Institute of Mental Health
Awarded by NHMRC Senior Research Fellowship
Awarded by NHMRC Career Development Fellowship
Awarded by NHMRC Emerging Leadership Fellowship
Awarded by Netherlands Organization for Scientific Research Veni
Funding Acknowledgements
This work was supported by the Prediction of Early Mental Disorder and Preventive Treatment (http:// www.pre-empt.org.au) -Centre of Research Excellence (National Health and Medical Research Council [NHMRC] Grant No. 1198304) . PDM received grants from the National Institute of Mental Health during the conduct of the study. In addition, PDM had the following patent issued: AU 2015203289; US 9884034; US 15/844444; and CA 2773031. PDM has received past unrestricted grant funding from Janssen-Cilag, Astra Zeneca, Eli Lilly, Novartis, and Pfizer and honoraria for consultancy and teaching from Janssen-Cilag, Eli Lilly, Pfizer, Astra Zeneca, Roche, Bristol Meyers Squibb, and Lundbeck. He has received grant funding from the Colonial Foundation, NMMRC, Australian Research Council, National Alliance for Research on Schizophrenia & Depression, Stanley Foundation, National Institutes of Health, Wellcome Trust, and Australian and Victorian govern- ments. SRC received speaker/consultation fees from Janssen-Cilag, Lundbeck, Otsuka, and Servier and research funding from Janssen-Cilag, Lundbeck, Otsuka, and Gilead. BN was supported by NHMRC Senior Research Fellowship (Grant No. 1137687) and a University of Melbourne Dame Kate Campbell Fellowship, all of which were unrelated to this work. CP was supported by NHMRC L3 Investigator (Grant No. 1196508) outside the submitted work. KA was supported by NHMRC Career Development Fellowship (Grant No. 1141207) and a University of Melbourne Dame Kate Campbell Fellowship. KA has received funding from the NHMRC, Medical Research Future Fund, and Wellcome Trust, all unrelated to this work. AL was supported by NHMRC Emerging Leadership Fellowship (Grant No. 2010063) . ARY was supported by NHMRC Principal Research Fellowship (Grant No. 1136829) . GPA was supported by NHMRC Senior Research Fellowship (Grant No. 1080963) . JTWW was funded by Netherlands Orga- nization for Scientific Research Veni (Grant No. 016.156.019) . All other authors report no biomedical financial interests or potential conflicts of interest.