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Get Free AccessNeuroimaging 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.
Willem B. Bruin, Paul Zhutovsky, Guido van Wingen, Janna Marie Bas‐Hoogendam, Nynke A. Groenewold, Kevin Hilbert, Anderson M. Winkler, André 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 Bernstein, Johannes Björkstrand, Robert Blair, Karina S. Blair, Laura Blanco‐Hinojo, Joscha Böhnlein, Paolo Brambilla, Rodrigo A. Bressan, Fabian Breuer, Marta Cano, Elisa Canu, Elise M. Cardinale, Narcı́s Cardoner, Camilla Cividini, Henk Cremers, Udo Dannlowski, Gretchen J. Diefenbach, Katharina Domschke, Alex 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 L. Harper, Anita Harrewijn, Alexandre Heeren, David Hoffman, Andrea Parolin Jackowski, Neda Jahanshad, Laura Jett, Antonia N. Kaczkurkin, Parmis Khosravi, Ellen Kingsley, Tilo Kircher, Milutin Kostić, Bart Larsen, Sang‐Hyuk Lee, Elisabeth J. Leehr, Ellen Leibenluft, Christine Löchner, Su Lui, Eleonora Maggioni, Gisele Gus Manfro, Kristoffer Månsson, Claire E. Marino, Frances Meeten, Barbara Milrod, Ana Munjiza, Benson Irungu, Michael Myers, Susanne Neufang, Jared A. Nielsen, Patricia Ohrmann, Cristina Ottaviani, Martin P. Paulus, Michael T. Perino, K Luan Phan, Sara Poletti, Daniel Porta‐Casteràs, Jesús Pujol, Andrea Reinecke, Grace Ringlein, Pavel Rjabtsenkov, Karin Roelofs, Ramiro Salas, Giovanni Abrahão Salum, Theodore D. Satterthwaite, Elisabeth Schrammen, Lisa Sindermann, Jordan W. Smoller (2022). Brain-Based Classification of Youth with Anxiety Disorders: an ENIGMA-ANXIETY Transdiagnostic Examination using Machine Learning. , DOI: https://doi.org/10.31234/osf.io/exrm9.
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Type
Preprint
Year
2022
Authors
100
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.31234/osf.io/exrm9
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