Data Entry: Please note that the research database will be replaced by UNIverse by the end of October 2023. Please enter your data into the system https://universe-intern.unibas.ch. Thanks

Login for users with Unibas email account...

Login for registered users without Unibas email account...

 
Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 4405709
Author(s) Koutsouleris, Nikolaos; Riecher-Rössler, Anita; Meisenzahl, Eva M.; Smieskova, Renata; Studerus, Erich; Kambeitz-Ilankovic, Lana; von Saldern, Sebastian; Cabral, Carlos; Reiser, Maximilian; Falkai, Peter; Borgwardt, Stefan
Author(s) at UniBasel Borgwardt, Stefan
Studerus, Erich
Year 2015
Title Detecting the psychosis prodrome across high-risk populations using neuroanatomical biomarkers
Journal Schizophrenia Bulletin
Volume 41
Number 2
Pages / Article-Number 471-82
Mesh terms Adult; Biomarkers; Female; Gray Matter, pathology; Humans; Machine Learning; Male; Multicenter Studies as Topic, standards; Pattern Recognition, Automated, standards; Prodromal Symptoms; Prognosis; Psychotic Disorders, pathology; Risk Assessment; Sensitivity and Specificity; Young Adult
Abstract To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor's decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor's decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tools.
Publisher Oxford University Press
ISSN/ISBN 0586-7614 ; 1745-1701
edoc-URL https://edoc.unibas.ch/70688/
Full Text on edoc No
Digital Object Identifier DOI 10.1093/schbul/sbu078
PubMed ID http://www.ncbi.nlm.nih.gov/pubmed/24914177
ISI-Number WOS:000350979500022
Document type (ISI) Journal Article
 
   

MCSS v5.8 PRO. 0.349 sec, queries - 0.000 sec ©Universität Basel  |  Impressum   |    
28/03/2024