Anxiety onset in adolescents: a machine-learning prediction
Authors: 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, Artiges Eric , Aydin Semiha , Bach Christine , Banaschewski Tobias , Barbot Alexis , Barker Gareth , Bokde Arun , Bordas Nadège , Bricaud Zuleima , Bromberg Uli , Bruehl Ruediger , Büchel Christian , Cattrell Anna , Conrod Patricia , Desrivieres Sylvane , Fadai Tahmine , Filippi Irina , Flor Herta , Frouin Vincent , Galinowski André , Gallinat Jürgen , Garavan Hugh , Briand Fanny Gollier , Gourlan Chantal , Gowland Penny , Guldner Stella , Heinz Andreas , Ittermann Bernd , Jia Tianye , Lemaitre Hervé , Martinot Jean-Luc , Massicotte Jessica , Miranda Ruben , Müller Kathrin , Nees Frauke , Nymberg Charlotte , Paillère Martinot Marie Laure , Paus Tomas , Pausova Zdenka , Poline Jean-Baptiste , Poustka Luise , Reuter Jan , Rogers John , Ruggeri Barbara , Sarvasmaa Anna S. , Schmäl Christine , Schumann Gunter , Struve Maren , Smolka Michael , Sommer Wolfgang , Vulser Hélène , Walter Henrik , Whelan Robert 
Published: December 2022

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.