Skip to main content

Research Repository

Advanced Search

Anxiety onset in adolescents: a machine-learning prediction

Chavanne, Alice V.; Paillère Martinot, Marie Laure; Penttilä, Jani; Grimmer, Yvonne; Conrod, Patricia; Stringaris, Argyris; van Noort, Betteke; Isensee, Corinna; Becker, Andreas; Banaschewski, Tobias; Bokde, Arun L. W.; Desrivières, Sylvane; Flor, Herta; Grigis, Antoine; Garavan, Hugh; Gowland, Penny; Heinz, Andreas; Brühl, Rüdiger; Nees, Frauke; Papadopoulos Orfanos, Dimitri; Paus, Tomáš; Poustka, Luise; Hohmann, Sarah; Millenet, Sabina; Fröhner, Juliane H.; Smolka, Michael N.; Walter, Henrik; Whelan, Robert; Schumann, Gunter; Martinot, Jean-Luc; Artiges, Eric; IMAGEN consortium

Anxiety onset in adolescents: a machine-learning prediction Thumbnail


Alice V. Chavanne

Marie Laure Paillère Martinot

Jani Penttilä

Yvonne Grimmer

Patricia Conrod

Argyris Stringaris

Betteke van Noort

Corinna Isensee

Andreas Becker

Tobias Banaschewski

Arun L. W. Bokde

Sylvane Desrivières

Herta Flor

Antoine Grigis

Hugh Garavan

Andreas Heinz

Rüdiger Brühl

Frauke Nees

Dimitri Papadopoulos Orfanos

Tomáš Paus

Luise Poustka

Sarah Hohmann

Sabina Millenet

Juliane H. Fröhner

Michael N. Smolka

Henrik Walter

Robert Whelan

Gunter Schumann

Jean-Luc Martinot

Eric Artiges

IMAGEN consortium


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. Prospective prediction of pooled anxiety disorders relied mostly on psychometric features and achieved moderate performance (area under the receiver operating curve = 0.68), while generalized anxiety disorder (GAD) prediction achieved similar performance. MRI regional volumes did not improve the prediction performance of prospective pooled anxiety disorders with respect to psychometric features alone, but they improved the prediction performance of GAD, with the caudate and pallidum volumes being among the most contributing features. To conclude, in non-anxious 14 year old adolescents, future clinical anxiety onset 4–8 years later could be individually predicted. Psychometric features such as neuroticism, hopelessness and emotional symptoms were the main contributors to pooled anxiety disorders prediction. Neuroanatomical data, such as caudate and pallidum volume, proved valuable for GAD and should be included in prospective clinical anxiety prediction in adolescents.


Chavanne, A. V., Paillère Martinot, M. L., Penttilä, J., Grimmer, Y., Conrod, P., Stringaris, A., …IMAGEN consortium. (2022). Anxiety onset in adolescents: a machine-learning prediction. Molecular Psychiatry,

Journal Article Type Article
Acceptance Date Oct 10, 2022
Online Publication Date Dec 8, 2022
Publication Date Dec 8, 2022
Deposit Date Dec 20, 2022
Publicly Available Date Jan 4, 2023
Journal Molecular Psychiatry
Print ISSN 1359-4184
Electronic ISSN 1476-5578
Publisher Springer Science and Business Media LLC
Peer Reviewed Peer Reviewed
Keywords Cellular and Molecular Neuroscience; Psychiatry and Mental health; Molecular Biology
Public URL
Publisher URL
Additional Information Authors on behalf of the IMAGEN consortium


You might also like

Downloadable Citations