Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning
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 towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagno...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2024
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| _version_ | 1811139954874515456 |
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| author | Bruin, WB Zhutovsky, P van Wingen, GA Bas-Hoogendam, JM Groenewold, NA Hilbert, K Winkler, AM Zugman, A Agosta, F Åhs, F Andreescu, C Antonacci, C Asami, T Assaf, M Barber, JP Bauer, J Bavdekar, SY Beesdo-Baum, K Benedetti, F Bernstein, R Björkstrand, J Blair, RJ Blair, KS Blanco-Hinojo, L Böhnlein, J Brambilla, P Bressan, RA Breuer, F Cano, M Canu, E Cardinale, EM Cardoner, N Cividini, C Cremers, H Dannlowski, U Diefenbach, GJ Domschke, K Doruyter, AGG Dresler, T Erhardt, A Filippi, M Fonzo, GA Freitag, GF Furmark, T Ge, T Gerber, AJ Gosnell, SN Grabe, HJ Grotegerd, D Gur, RC Reinecke, A |
| author_facet | Bruin, WB Zhutovsky, P van Wingen, GA Bas-Hoogendam, JM Groenewold, NA Hilbert, K Winkler, AM Zugman, A Agosta, F Åhs, F Andreescu, C Antonacci, C Asami, T Assaf, M Barber, JP Bauer, J Bavdekar, SY Beesdo-Baum, K Benedetti, F Bernstein, R Björkstrand, J Blair, RJ Blair, KS Blanco-Hinojo, L Böhnlein, J Brambilla, P Bressan, RA Breuer, F Cano, M Canu, E Cardinale, EM Cardoner, N Cividini, C Cremers, H Dannlowski, U Diefenbach, GJ Domschke, K Doruyter, AGG Dresler, T Erhardt, A Filippi, M Fonzo, GA Freitag, GF Furmark, T Ge, T Gerber, AJ Gosnell, SN Grabe, HJ Grotegerd, D Gur, RC Reinecke, A |
| author_sort | Bruin, WB |
| collection | OXFORD |
| description | 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 towards highly powered
(i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically
informed. Hence, we uniquely built/validated supervised neuroanatomical machine learning (ML) models for
individual-level inferences, using the largest up to date neuroimaging database on youth anxiety disorders:
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 (AUC’s 0.59-0.63). Classifications were driven by neuroanatomical features (cortical thickness/surface
area, subcortical volumes) in fronto-striato-limbic and temporo-parietal regions. This benchmark study provides
estimates on individual-level classification performances that can be realistically achieved with ML using
neuroanatomical data, within a large, heterogenous, and multi-site sample of youth with anxiety disorders. |
| first_indexed | 2024-09-25T04:14:18Z |
| format | Journal article |
| id | oxford-uuid:b69f9452-a615-4c03-9db2-65fe70b8851a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:14:18Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:b69f9452-a615-4c03-9db2-65fe70b8851a2024-07-10T12:29:05ZBrain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:b69f9452-a615-4c03-9db2-65fe70b8851aEnglishSymplectic ElementsSpringer Nature2024Bruin, WBZhutovsky, Pvan Wingen, GABas-Hoogendam, JMGroenewold, NAHilbert, KWinkler, AMZugman, AAgosta, FÅhs, FAndreescu, CAntonacci, CAsami, TAssaf, MBarber, JPBauer, JBavdekar, SYBeesdo-Baum, KBenedetti, FBernstein, RBjörkstrand, JBlair, RJBlair, KSBlanco-Hinojo, LBöhnlein, JBrambilla, PBressan, RABreuer, FCano, MCanu, ECardinale, EMCardoner, NCividini, CCremers, HDannlowski, UDiefenbach, GJDomschke, KDoruyter, AGGDresler, TErhardt, AFilippi, MFonzo, GAFreitag, GFFurmark, TGe, TGerber, AJGosnell, SNGrabe, HJGrotegerd, DGur, RCReinecke, ANeuroanatomical 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 towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Hence, we uniquely built/validated supervised neuroanatomical machine learning (ML) models for individual-level inferences, using the largest up to date neuroimaging database on youth anxiety disorders: 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 (AUC’s 0.59-0.63). Classifications were driven by neuroanatomical features (cortical thickness/surface area, subcortical volumes) in fronto-striato-limbic and temporo-parietal regions. This benchmark study provides estimates on individual-level classification performances that can be realistically achieved with ML using neuroanatomical data, within a large, heterogenous, and multi-site sample of youth with anxiety disorders. |
| spellingShingle | Bruin, WB Zhutovsky, P van Wingen, GA Bas-Hoogendam, JM Groenewold, NA Hilbert, K Winkler, AM Zugman, A Agosta, F Åhs, F Andreescu, C Antonacci, C Asami, T Assaf, M Barber, JP Bauer, J Bavdekar, SY Beesdo-Baum, K Benedetti, F Bernstein, R Björkstrand, J Blair, RJ Blair, KS Blanco-Hinojo, L Böhnlein, J Brambilla, P Bressan, RA Breuer, F Cano, M Canu, E Cardinale, EM Cardoner, N Cividini, C Cremers, H Dannlowski, U Diefenbach, GJ Domschke, K Doruyter, AGG Dresler, T Erhardt, A Filippi, M Fonzo, GA Freitag, GF Furmark, T Ge, T Gerber, AJ Gosnell, SN Grabe, HJ Grotegerd, D Gur, RC Reinecke, A Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning |
| title | Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning |
| title_full | Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning |
| title_fullStr | Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning |
| title_full_unstemmed | Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning |
| title_short | Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning |
| title_sort | brain based classification of youth with anxiety disorders transdiagnostic examinations within the enigma anxiety database using machine learning |
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