Gast ::
| Hauptseite > Online First > Overview of Challenges in Brain-Based Predictive Modeling: Toward Meaningful Predictive Insights > print |
| Hauptseite > Online First > Overview of Challenges in Brain-Based Predictive Modeling: Toward Meaningful Predictive Insights > print |
| 001 | 1049623 | ||
| 005 | 20251218122555.0 | ||
| 024 | 7 | _ | |a 10.1016/j.biopsych.2025.09.003 |2 doi |
| 024 | 7 | _ | |a 0006-3223 |2 ISSN |
| 024 | 7 | _ | |a 1873-2402 |2 ISSN |
| 037 | _ | _ | |a FZJ-2025-05411 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Komeyer, Vera |0 P:(DE-Juel1)187351 |b 0 |u fzj |
| 245 | _ | _ | |a Overview of Challenges in Brain-Based Predictive Modeling: Toward Meaningful Predictive Insights |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2025 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1766056950_14706 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a This 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]. |
| 520 | _ | _ | |a Predictive 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 |
| 536 | _ | _ | |a 5251 - Multilevel Brain Organization and Variability (POF4-525) |0 G:(DE-HGF)POF4-5251 |c POF4-525 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Nieto, Nicolás |0 P:(DE-Juel1)194707 |b 1 |u fzj |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 2 |u fzj |
| 700 | 1 | _ | |a Raimondo, Federico |0 P:(DE-Juel1)185083 |b 3 |
| 700 | 1 | _ | |a Patil, Kaustubh R. |0 P:(DE-Juel1)172843 |b 4 |u fzj |
| 773 | _ | _ | |a 10.1016/j.biopsych.2025.09.003 |g p. S000632232501460X |0 PERI:(DE-600)1499907-9 |p S000632232501460X |t Biological psychiatry |v . |y 2025 |x 0006-3223 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)187351 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)194707 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)131678 |
| 910 | 1 | _ | |a HHU Düsseldorf |0 I:(DE-HGF)0 |b 2 |6 P:(DE-Juel1)131678 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)185083 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)172843 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-525 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Decoding Brain Organization and Dysfunction |9 G:(DE-HGF)POF4-5251 |x 0 |
| 914 | 1 | _ | |y 2025 |
| 915 | p | c | |a APC keys set |0 PC:(DE-HGF)0000 |2 APC |
| 915 | p | c | |a DEAL: Elsevier 09/01/2023 |0 PC:(DE-HGF)0125 |2 APC |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2024-12-09 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-09 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2024-12-09 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2024-12-09 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2024-12-09 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2024-12-09 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |d 2024-12-09 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2024-12-09 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2024-12-09 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2024-12-09 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b BIOL PSYCHIAT : 2022 |d 2024-12-09 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2024-12-09 |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2024-12-09 |
| 915 | _ | _ | |a IF >= 10 |0 StatID:(DE-HGF)9910 |2 StatID |b BIOL PSYCHIAT : 2022 |d 2024-12-09 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-7-20090406 |k INM-7 |l Gehirn & Verhalten |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a EDITORS |
| 980 | _ | _ | |a VDBINPRINT |
| 980 | _ | _ | |a I:(DE-Juel1)INM-7-20090406 |
| 980 | _ | _ | |a APC |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a APC |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|