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.
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Data availability
All data used in this study are in principle publicly available within the ENIGMA Consortium. For this study, all site-level data were first transferred to a secure data environment (Amsterdam UMC) and subsequently subjected to the final mega-analyses, all with permission from individual sites. However, some data-sharing restrictions are in place. These include restrictions imposed by (1) site-specific consent documentation, ethical review boards and institutional processes, (2) along with national/international data-sharing legislations (for example, General Data Protection Regulation). Some of these restrictions may require a signed material transfer agreement for limited and predefined data use. However, data sharing might still be possible, requiring submission of a detailed analysis plan to the ENIGMA-Anxiety Consortium. If approved, access to relevant data is provided, depending on data availability, local principal investigator approval and compliance with all supervening regulations. Requests or questions regarding data availability or sharing should be sent to the corresponding author.
Code availability
Relevant analysis codes can be found at https://github.com/WillemB2104.
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Acknowledgements
This study was financially supported by The Netherlands Organization for Health Research and Development—ZonMw (research fellowship number 06360322210035 to M. Aghajani), Dutch Research Council NWO (SSH Open Competition number 15810 to M. Aghajani), Leiden University Fund (project Youth Mental Health Meets Big Data Analytics, grant number LUF23075-5-306 to M. Aghajani), Leiden University Fund (project grant number W213085-5 to M. Aghajani), Amsterdam Neuroscience (Amsterdam Neuroscience Alliance Grant Project to M. Aghajani and G.A.v.W.), the Dutch Research Council NWO (Rubicon grant number 019.201SG.022 to J.M.B.-H. and 452020227 to L.K.M.H.), the Carnegie Corporation of New York (to N.A.G.; the statements made and views expressed are solely the responsibility of the author), the National Institute of Mental Health (NIMH; IRP project grant number ZIA-MH002781 to A.M.W., C. Antonacci and D.S.P.; R01-MH070664 to J.P.B.; K23-MH114023 to G.A.F.; R01-MH117601 to N.J.; R00-MH117274 to A.N.K.; R01-MH70918-01A2 and R01-MH070664 to B. Milrod; R01-MH086517 and K23-MH076198 to K.L.P.; R01-MH101486 to J.W.S.; K01-MH118428 to B.S.-J.; K23-MH109983 to C.M.S.; and R01-MH116147, R01-MH121246 and R01-MH129742 to P.M.T.), the National Institutes of Health (grant number R01-MH101486 to J.A.N.), the National Institute of General Medical Sciences Center (grant number 1P20GM121312 to M.P.P.), the German Research Foundation (DFG; grant number BE 3809/8-1 to K.B.-B., KI588/14-1 and KI588/14-2 to T.K. and STR 1146/18-1 to B.S.), the Italian Ministry of Health (RF-2016-02364582 to P.B., GR-2010-2312442 and GR-2011-02348232 to C.O.), the Carlos III Health Institute (ISCIII; M.C. is funded by a ‘Sara Borrell’ postdoctoral contract (CD20/00189), D.P.-C. is funded by a ‘PFIS’ predoctoral fellowship (FI19/00251) and grant number PI18/00036 to N.C.), the Hartford HealthCare (grant number 129522 to G.J.D.), the South African Medical Research Council, Nuclear Technologies in Medicine and the Biosciences Initiative (NTeMBI) and Harry Crossley Foundation to A.G.G.D., the One Mind Baszucki-Brain Research Fund to G.A.F., the FRS-FNRS Belgian National Science Foundation (grant number 1.C.059.18F to A. Heeren), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; grant number 2013/08531-5 to A.P.J. and 2014/50917-0 to G.A.S.), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq; grant number 442026/2014-5 to A.P.J. and 465550/2014-2 to G.A.S.), the Ministry of Science and ICT, South Korea (grant number NRF-2019M3C7A1032262 to S.-H.L.), the National Key R&D Program of China (grant numbers 2022YFC2009901 and 2022YFC2009900 to S.L.), the National Natural Science Foundation of China (grant number 82120108014 to S.L.), Humboldt Foundation Friedrich Wilhelm Bessel Research Award and Chang Jiang Scholars (program number T2019069 to S.L.), the BIAL Foundation (grant number 288/20 to E.M.), the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant number 101026595 to K.N.T.M.), the Medical Research Council New Investigator Grant (grant number MR/W005077/1 to F.M.), and the Eunice Kennedy Schriver National Institute of Child Health and Human Development (grant number K99 HD105002 to M.T.P.). A.R. is supported by a fellowship from MQ Mental Health Research and by the NIHR Oxford Health Biomedical Research Centre, the European Research Council (grant number ERC_CoG_772337 to K.R.), European Union’s Seventh Framework Programme (FP7/2007-2013; grant number 337673 to G.A.S.), the McNair Foundation (MIND-DB; Veteran Health Administration grant number VHA I01CX001937 to R. Salas), the Brain and Behavior Research Foundation (NARSAD Young Investigator Grant to B.S.-J. and A.T.), and the Medical Research Council of the National Institute for Health and Care Research to B.W.
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W.B.B. and M. Aghajani conceived and designed the study. W.B.B. and M. Aghajani collated, analyzed and interpreted the data. All authors were involved in drafting, writing and revising the paper. All authors were involved in site-level data collection and curation. All authors read and approved the final version of the paper.
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F. Agosta is section editor of NeuroImage: Clinical; has received speaker honoraria from Biogen Idec, Roche and Zambon; and receives or has received research supports from the Italian Ministry of Health, AriSLA (Fondazione Italiana di Ricerca per la SLA) and the European Research Council. E.C. has received research support from the Italian Ministry of Health. T.D. has received consulting and speaker honoraria from Novartis, Teva and Hormosan unrelated to this paper. M.F. is editor-in-chief of the Journal of Neurology and associate editor of Human Brain Mapping, Neurological Sciences and Radiology; has received compensation for consulting services from Alexion, Almirall, Biogen, Merck, Novartis, Roche and Sanofi; has received compensation for speaking activities from Bayer, Biogen, Celgene, Chiesi Italia SpA, Eli Lilly, Genzyme, Janssen, Merck-Serono, Neopharmed Gentili, Novartis, Novo Nordisk, Roche, Sanofi, Takeda and Teva; has participated in advisory boards for Alexion, Biogen, Bristol-Myers Squibb, Merck, Novartis, Roche, Sanofi, Sanofi-Aventis, Sanofi-Genzyme and Takeda; has participated in scientific direction of educational events for Biogen, Merck, Roche, Celgene, Bristol-Myers Squibb, Lilly, Novartis and Sanofi-Genzyme; and receives research support from Biogen Idec, Merck-Serono, Novartis, Roche, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla and ARiSLA (Fondazione Italiana di Ricerca per la SLA). G.A.F. owns equity in Alto Neuroscience and is a consultant to Synapse Bio AI. H.J.G. has received travel grants and speaker honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen Cilag, and has received research funding from Fresenius Medical Care. N.J. received partial research grant support from Biogen for research unrelated to this paper. M.P.P. is an advisor to Spring Care, a behavioral health start-up, and has received royalties for an article about methamphetamine in UpToDate. P.M.T. received partial research grant support from Biogen for research unrelated to this paper. All other individually named authors in and outside of the ENIGMA-Anxiety Working Groups reported no biomedical financial interests or potential conflicts of interest.
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Bruin, W.B., Zhutovsky, P., van Wingen, G.A. et al. Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning. Nat. Mental Health 2, 104–118 (2024). https://doi.org/10.1038/s44220-023-00173-2
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DOI: https://doi.org/10.1038/s44220-023-00173-2
