Brain-Based Classification of Youth with Anxiety Disorders: an ENIGMA-ANXIETY Transdiagnostic Examination using Machine Learning
Authors: Bruin Willem B. , Zhutovsky Paul , van Wingen Guido , Bas-Hoogendam Janna Marie , Groenewold Nynke A. , Hilbert Kevin , Winkler Anderson M. , Zugman André , Agosta Federica , Åhs Fredrik , Andreescu Carmen , Antonacci Chase , Asami Takeshi , Assaf Michal , Barber Jacques , Bauer Jochen , Bavdekar Shreya , Beesdo-Baum Katja , Benedetti Francesco , Bernstein Rachel , Björkstrand Johannes , Blair Robert , Blair Karina S. , Blanco-Hinojo Laura , Böhnlein Joscha , Brambilla Paolo , Bressan Rodrigo , Breuer Fabian , Cano Marta , Canu Elisa , Cardinale Elise M , Cardoner Narcís , Cividini Camilla , Cremers Henk , Dannlowski Udo , Diefenbach Gretchen J. , Domschke Katharina , Doruyter Alexander , Dresler Thomas , Erhardt Angelika , Filippi Massimo , Fonzo Gregory , Freitag Gabrielle Felice , Furmark Tomas , Ge Tian , Gerber Andrew J. , Gosnell Savannah , Grabe Hans J. , Grotegerd Dominik , Gur Ruben C. , Gur Raquel E. , Hamm Alfons O. , Han Laura K. M. , Harper Jennifer , Harrewijn Anita , Heeren Alexandre , Hoffman David , Jackowski Andrea P. , Jahanshad Neda , Jett Laura , Kaczkurkin Antonia N. , Khosravi Parmis , Kingsley Ellen , Kircher Tilo , Kostić Milutin , Larsen Bart , Lee Sang-Hyuk , Leehr Elisabeth , Leibenluft Ellen , Lochner Christine , Lui Su , Maggioni Eleonora , Manfro Gisele Gus , Månsson Kristoffer , Marino Claire , Meeten Frances , Milrod Barbara , Munjiza Ana , Irungu Benson , Myers Michael , Neufang Susanne , Nielsen Jared Allen , Ohrmann Patricia , Ottaviani Cristina , Paulus Martin P , Perino Michael T. , Phan K Luan , Poletti Sara , Porta-Casteràs Daniel , Pujol Jesus , Reinecke Andrea , Ringlein Grace , Rjabtsenkov Pavel , Roelofs Karin , Salas Ramiro , Salum Giovanni , Satterthwaite Theodore D. , Schrammen Elisabeth , Sindermann Lisa , Smoller Jordan , Soares Jair , Stark Rudolf , Stein Frederike , Straube Thomas , Straube Benjamin , Strawn Jeffrey , Suarez-Jimenez Benjamin , Sylvester Chad M. , Talati Ardesheer , Thomopoulos Sophia I , Tükel Raşit , van Nieuwenhuizen Helena , Werwath Katy E. , Wittfeld Katharina , Wright Barry , Wu Mon-Ju , Yang Yunbo , Zilverstand Anna , Zwanzger Peter , Blackford Jennifer , Avery Suzanne , Clauss Jacqueline , Lueken Ulrike , Thompson Paul , Pine Daniel , Stein Dan J. , van der Wee Nic , Veltman Dick , Aghajani Moji 
Published: November 2022

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.