Brain-based classification of youth with anxiety disorders - Transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning - Research findings and methods

Aug 5, 2026

Brain-based classification of youth with anxiety disorders uses transdiagnostic examinations within the ENIGMA-Anxiety database, applying machine learning to identify patterns in brain measures that generalize across anxiety-related phenotypes. The study integrates data from multiple contributors and affiliations and evaluates how model features support grouping or differentiation of youth presenting with anxiety disorders. Results focus on the classification performance and the utility of transdiagnostic, data-driven approaches for understanding neurobiological variation in anxious youth.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Study background and objectives

Transdiagnostic framework and dataset

Machine learning approach

Brain-based classification results

University of Texas Rio Grande Valley ScholarWorks @ UTRGV | School of Medicine Publications and
Presentations | School of Medicine | | --- | --- | | 1-10-2024
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
See next page for additional authors
Follow this and additional works at: [https://scholarworks.utrgv.edu/som_pub](https://scholarworks.utrgv.edu/som_pub)
Part of the Medicine and Health Sciences Commons | | Recommended Citation Bruin, W. B., Zhutovsky, P., van Wingen, G. A., Bas-Hoogendam, J. M., Groenewold, N. A., Hilbert, K., ... & Aghajani, M. (2024) . Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning. Nature Mental Health, 2(1), 104-118. [https://doi.org/10.1038/s44220-023-00173-2](https://doi.org/10.1038/s44220-023-00173-2) This Article is brought to you for free and open access by the School of Medicine at ScholarWorks @ UTRGV. It has been accepted for inclusion in School of Medicine Publications and Presentations by an authorized administrator of ScholarWorks @ UTRGV. For more information, please [contact](contact justin.white@utrgv.edu)[ justin.white@utrgv.edu](contact justin.white@utrgv.edu), [william.flores01@utrgv.edu](william.flores01@utrgv.edu). Authors Willem B. Bruin, Paul Zhutovsky, Guido A. van Wingen, Janna Marie Bas-Hoogendam, Nynke A. Groenewold, Kevin Hilbert, Anderson M. Winkler, André Zugman, Federica Agosta, and Fredrik Åhs This article is available at ScholarWorks @ UTRGV: [https://scholarworks.utrgv.edu/som_pub/1585](https://scholarworks.utrgv.edu/som_pub/1585) Brain-Based Classification of Youth with Anxiety Disorders: Transdiagnostic and CaseControl Examinations within ENIGMA-ANXIETY using Machine Learning Authors Willem B. Bruin, MSc¹˒², Paul Zhutovsky, PhD¹˒², Guido A. van Wingen, PhD¹˒², Janna Marie Bas-Hoogendam, PhD³˒⁴˒⁵, Nynke A. Groenewold, PhD⁶˒⁷, Kevin Hilbert, PhD⁸, Anderson M. Winkler, MD, PhD⁹˒¹⁰, Andre Zugman, PhD¹⁰, Federica Agosta, MD, PhD¹¹˒¹²˒¹³, Fredrik Åhs, PhD¹⁴, Carmen Andreescu, MD¹⁵, Chase Antonacci, MPhil¹⁶, Takeshi Asami, MD, PhD¹⁷, Michal Assaf, MD¹⁸˒¹⁹, Jacques P. Barber, PhD ²⁰, Jochen Bauer, PhD²¹, Shreya Y. Bavdekar, ²², Katja Beesdo-Baum, PhD²³, Francesco Benedetti, MD²⁴, Rachel Bernstein, BA¹⁶, Johannes Björkstrand, PhD²⁵, Robert J Blair, PhD²⁶, Karina S. Blair, PhD²⁷, Laura Blanco-Hinojo, PhD²⁸˒²⁹˒³⁰, Joscha Böhnlein, PhD³¹, Paolo Brambilla, MD, PhD³²˒³³, Rodrigo A. Bressan, MD, PhD³⁴˒³⁵, Fabian Breuer, MSc³¹, Marta Cano, PhD³⁶˒³⁷, Elisa Canu, PhD¹¹, Elise M. Cardinale, PhD³⁸, Narcís Cardoner, MD, PhD³⁶˒³⁹˒³⁷, Camilla Cividini, PhD¹¹, Henk Cremers, PhD⁴⁰, Udo Dannlowski, MD, PhD³¹, Gretchen J. Diefenbach, PhD⁴¹˒⁴², Katharina Domschke, MD, PhD⁴³, Alexander G.G. Doruyter, PhD⁴⁴˒⁴⁵, Thomas Dresler, PhD⁴⁶˒⁴⁷, Angelika Erhardt, MD⁴⁸, Massimo Filippi, MD¹¹˒¹²˒⁴⁹˒¹³, Gregory A. Fonzo, PhD⁵⁰, Gabrielle F. Freitag, MSc⁵¹, Tomas Furmark, PhD⁵², Tian Ge, PhD ⁵³˒⁵⁴, Andrew J. Gerber, MD, PhD⁵⁵˒⁵⁶, Savannah N. Gosnell, PhD⁵⁷, Hans J. Grabe, MD⁵⁸, Dominik Grotegerd, PhD³¹, Ruben C. Gur, PhD⁵⁹˒⁶⁰˒⁶¹, Raquel E. Gur, MD, PhD⁵⁹˒⁶⁰, Alfons O. Hamm, PhD⁶², Laura K. M. Han, PhD ⁶³˒⁶⁴, Jennifer C. Harper, MSc⁶⁵, Anita Harrewijn, PhD⁶⁶˒¹⁶, Alexandre Heeren, PhD⁶⁷, David Hofmann, PhD⁶⁸, Andrea P. Jackowski, PhD³⁴˒³⁵, Neda Jahanshad, PhD⁶⁹, Laura Jett, BSc¹⁶, Antonia N. Kaczkurkin, PhD⁷⁰, Parmis Khosravi, PhD¹⁰, Ellen N. Kingsley, MSc⁷¹, Tilo Kircher, MD, PhD⁷², Milutin Kostic, MD, PhD⁷³˒⁷⁴, Bart Larsen, PhD⁵⁹, SangHyuk Lee, MD, PhD⁷⁵, Elisabeth J. Leehr, PhD³¹, Ellen Leibenluft, MD⁷⁶, Christine Lochner, PhD⁷⁷, Su Lui, PhD⁷⁸, Eleonora Maggioni, PhD⁷⁹˒⁸⁰, Gisele G. Manfro, MD, PhD⁸¹˒⁸², Kristoffer N.T. Månsson, PhD⁸³, Claire E. Marino, BA²², Frances

FAQs

What database and framework are used in the study?

The study uses the ENIGMA-Anxiety database and a transdiagnostic framework to examine neurobiological patterns relevant to youth anxiety disorders.

How does machine learning contribute to the analysis?

Machine learning models are applied to brain measures to support classification and to evaluate how well patterns can distinguish or group youth with anxiety-related profiles.

What is the main research goal?

The goal is to develop and assess brain-based classification of youth with anxiety disorders that generalizes through transdiagnostic examinations and data-driven modeling.

Aug 5, 2026

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