{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["28(2)"],"submitter":["Chavanne AV"],"pubmed_abstract":["Recent longitudinal studies in youth have reported MRI correlates of prospective anxiety symptoms during adolescence, a vulnerable period for the onset of anxiety disorders. However, their predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of gray matter volumes of interest and psychometric scores in the detection of prospective clinical anxiety. Participants with clinical anxiety at age 18-23 (N = 156) were investigated at age 14 along with healthy controls (N = 424). Shapley values were extracted for in-depth interpretation of feature importance. P"],"journal":["Molecular psychiatry"],"pagination":["639-646"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9908534"],"repository":["biostudies-literature"],"pubmed_title":["Anxiety onset in adolescents: a machine-learning prediction."],"pmcid":["PMC9908534"],"pubmed_authors":["Muller K","Bach C","Ittermann B","Aydin S","Garavan H","Poustka L","Barker G","Gourlan C","Smolka MN","Sarvasmaa AS","Filippi I","Stringaris A","Vulser H","Isensee C","Hohmann S","Cattrell A","Struve M","Ruggeri B","Walter H","Bordas N","Flor H","Bromberg U","Papadopoulos Orfanos D","Guldner S","Schmal C","Martinot JL","Grigis A","van Noort B","Schumann G","Buchel C","Bruhl R","Desrivieres S","Paillere Martinot ML","Sommer W","Frouin V","Heinz A","Smolka M","Bruehl R","Bokde ALW","Jia T","Bokde A","Massicotte J","Millenet S","Frohner JH","IMAGEN consortium","Grimmer Y","Rogers J","Penttila J","Lemaitre H","Chavanne AV","Banaschewski T","Conrod P","Reuter J","Miranda R","Gallinat J","Poline JB","Fadai T","Whelan R","Gowland P","Paus T","Nees F","Nymberg C","Artiges E","Galinowski A","Bricaud Z","Briand FG","Pausova Z","Barbot A","Becker A"],"additional_accession":[]},"is_claimable":false,"name":"Anxiety onset in adolescents: a machine-learning prediction.","description":"Recent longitudinal studies in youth have reported MRI correlates of prospective anxiety symptoms during adolescence, a vulnerable period for the onset of anxiety disorders. However, their predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of gray matter volumes of interest and psychometric scores in the detection of prospective clinical anxiety. Participants with clinical anxiety at age 18-23 (N = 156) were investigated at age 14 along with healthy controls (N = 424). Shapley values were extracted for in-depth interpretation of feature importance. P","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2025-04-27T02:57:21.704Z","creation":"2025-04-06T18:40:02.935Z"},"accession":"S-EPMC9908534","cross_references":{"pubmed":["36481929"],"doi":["10.1038/s41380-022-01840-z"]}}