Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto-striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data.
Original languageEnglish
Pages (from-to)104-118
JournalNature Mental Health
Volume2
DOIs
Publication statusPublished - 10 Jan 2024

Research programs

  • ESSB PSY

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