Anxiety onset in adolescents: a machine-learning prediction

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Alice V. Chavanne - , Université Paris-Saclay, Humboldt University of Berlin (Author)
  • Marie Laure Paillère Martinot - , Université Paris-Saclay, Sorbonne Université, Assistance publique – Hôpitaux de Paris (Author)
  • Jani Penttilä - , Tampere University Hospital (Author)
  • Yvonne Grimmer - , Heidelberg University  (Author)
  • Patricia Conrod - , University of Montreal (Author)
  • Argyris Stringaris - , University College London (Author)
  • Betteke van Noort - , Charité – Universitätsmedizin Berlin (Author)
  • Corinna Isensee - , University of Göttingen (Author)
  • Andreas Becker - , University of Göttingen (Author)
  • Tobias Banaschewski - , Heidelberg University , Central Institute of Mental Health (CIMH) (Author)
  • Arun Bokde - , Trinity College Dublin (Author)
  • Sylvane Desrivières - , King's College London (KCL) (Author)
  • Herta Flor - , Heidelberg University , University of Mannheim, Central Institute of Mental Health (CIMH) (Author)
  • Antoine Grigis - , Université Paris-Saclay (Author)
  • Hugh Garavan - , University of Vermont (Author)
  • Penny Gowland - , University of Nottingham (Author)
  • Andreas Heinz - , Charité – Universitätsmedizin Berlin (Author)
  • Ruediger Bruehl - , Physikalisch-Technische Bundesanstalt (Author)
  • Frauke Nees - , Heidelberg University , Kiel University, University of Hamburg (Author)
  • Dimitri Papadopoulos Orfanos - , Université Paris-Saclay (Author)
  • Tomáš Paus - , University of Montreal (Author)
  • Luise Poustka - , University of Göttingen (Author)
  • Sarah Hohmann - , Heidelberg University  (Author)
  • Sabina Millenet - , Heidelberg University  (Author)
  • Juliane H. Fröhner - , Department of Psychiatry and Psychotherapy (Author)
  • Michael N. Smolka - , Department of Psychiatry and Psychotherapy (Author)
  • Henrik Walter - , Charité – Universitätsmedizin Berlin (Author)
  • Robert Whelan - , Trinity College Dublin (Author)
  • Gunter Schumann - , Fudan University, Charité – Universitätsmedizin Berlin (Author)
  • Jean Luc Martinot - , Université Paris-Saclay, École normale supérieure Paris-Saclay (Author)

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. 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.

Details

Original languageEnglish
Pages (from-to)639-646
Number of pages8
JournalMolecular psychiatry
Volume28
Issue number2
Early online date8 Dec 2022
Publication statusPublished - Feb 2023
Peer-reviewedYes

External IDs

PubMed 36481929
ORCID /0000-0001-5398-5569/work/150329518
ORCID /0000-0002-8493-6396/work/150330244

Keywords

Sustainable Development Goals

Keywords

  • Humans, Adolescent, Young Adult, Adult, Prospective Studies, Anxiety Disorders/psychology, Anxiety, Algorithms, Machine Learning

Library keywords