001049623 001__ 1049623
001049623 005__ 20251218122555.0
001049623 0247_ $$2doi$$a10.1016/j.biopsych.2025.09.003
001049623 0247_ $$2ISSN$$a0006-3223
001049623 0247_ $$2ISSN$$a1873-2402
001049623 037__ $$aFZJ-2025-05411
001049623 082__ $$a610
001049623 1001_ $$0P:(DE-Juel1)187351$$aKomeyer, Vera$$b0$$ufzj
001049623 245__ $$aOverview of Challenges in Brain-Based Predictive Modeling: Toward Meaningful Predictive Insights
001049623 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2025
001049623 3367_ $$2DRIVER$$aarticle
001049623 3367_ $$2DataCite$$aOutput Types/Journal article
001049623 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1766056950_14706
001049623 3367_ $$2BibTeX$$aARTICLE
001049623 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001049623 3367_ $$00$$2EndNote$$aJournal Article
001049623 500__ $$aThis work was supported by the Helmholtz Imagining grant BrainShapes (Grant No. ZT-I-PF-4-062 [to KRP]); the Multi-Omics Data Science project was funded from the program Profilbildung 2020 (Grant No. PROFILNRW-2020-107-A [to SBE]), an initiative of the Ministry of Culture and Science of the State of North Rhine-Westphalia; the H2020 Research Infrastructures (Grant No. EBRAIN-Health 101058516 [to SBE]); the Deutsche Forschungsgemeinschaft Collaborative Research Centre CRC1451 (Project No. 431549029 [to SBE]) on motor performance project B05; and the Universitätsklinikum Düsseldorf, Forschungskommission funded project VoxNorm [to KRP].
001049623 520__ $$aPredictive analytics based on machine learning (ML) and artificial intelligence is a powerful tool enabling precision psychiatry and providing insights into brain-behavior relationships. However, given the mixed results observed in the field so far, making meaningful progress requires careful consideration of several key challenges to ensure the validity of models and findings, including overfitting, confounding biases, site effect harmonization, and interpretability, among others. First, we highlight limitations of cross-validation, a ubiquitous ML strategy used to prevent overfitting and obtain generalization estimates, emphasizing the risk of performance inflation and the need for independent validation. Next, we introduce different types of so-called third variables that can influence the examination of a brain-behavioral relationship of interest in different ways, using causal inference principles. We emphasize the biasing impact of confounding variables on ML models and summarize common mitigation strategies. We then discuss site-specific effects in multisite datasets, reviewing different harmonization strategies to reduce unwanted variability and site-specific noise. Finally, we explore post hoc model interpretation methods to enhance model transparency while cautioning against misinterpretation. By integrating rigorous result validation, confounder control, and interpretability techniques, researchers can ensure that ML models produce more reliable and generalizable findings and avoid spurious associations.KeywordsBrain-behavior associationsConfoundsCross-validationHarmonizationMachine learningModel interpretability
001049623 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001049623 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001049623 7001_ $$0P:(DE-Juel1)194707$$aNieto, Nicolás$$b1$$ufzj
001049623 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b2$$ufzj
001049623 7001_ $$0P:(DE-Juel1)185083$$aRaimondo, Federico$$b3
001049623 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b4$$ufzj
001049623 773__ $$0PERI:(DE-600)1499907-9$$a10.1016/j.biopsych.2025.09.003$$gp. S000632232501460X$$pS000632232501460X$$tBiological psychiatry$$v.$$x0006-3223$$y2025
001049623 8767_ $$d2025-12-16$$eHybrid-OA$$jDEAL
001049623 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)187351$$aForschungszentrum Jülich$$b0$$kFZJ
001049623 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194707$$aForschungszentrum Jülich$$b1$$kFZJ
001049623 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b2$$kFZJ
001049623 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b2
001049623 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)185083$$aForschungszentrum Jülich$$b3$$kFZJ
001049623 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172843$$aForschungszentrum Jülich$$b4$$kFZJ
001049623 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
001049623 9141_ $$y2025
001049623 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001049623 915pc $$0PC:(DE-HGF)0125$$2APC$$aDEAL: Elsevier 09/01/2023
001049623 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2024-12-09$$wger
001049623 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bBIOL PSYCHIAT : 2022$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-09
001049623 915__ $$0StatID:(DE-HGF)9910$$2StatID$$aIF >= 10$$bBIOL PSYCHIAT : 2022$$d2024-12-09
001049623 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001049623 980__ $$ajournal
001049623 980__ $$aEDITORS
001049623 980__ $$aVDBINPRINT
001049623 980__ $$aI:(DE-Juel1)INM-7-20090406
001049623 980__ $$aAPC
001049623 980__ $$aUNRESTRICTED
001049623 9801_ $$aAPC