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| 001 | 1033617 | ||
| 005 | 20250203215419.0 | ||
| 024 | 7 | _ | |a 10.31219/osf.io/67zxe |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-06497 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-06497 |
| 100 | 1 | _ | |a Jung, Kyesam |0 P:(DE-Juel1)178611 |b 0 |
| 245 | _ | _ | |a Effective workflow from multi-modal MRI data to model-based prediction |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1738563586_21854 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Predicting human behavior from neuroimaging data remains a complex challenge in neuroscience. To address this, we propose a systematic and multi-faceted framework that incorporates a model-based workflow using dynamical brain models. This approach utilizes multi-modal MRI data for brain modeling and applies the optimized modeling outcome to machine learning. We demonstrate the performance of such an approach through several examples such as sex classification and prediction of cognition or personality traits. We in particular show that incorporating the simulated data into machine learning can significantly improve the prediction performance compared to using empirical features alone. These results suggest considering the output of the dynamical brain models as an additional neuroimaging data modality that complements empirical data by capturing brain features that are difficult to measure directly. The discussed model-based workflow can offer a promising avenue for investigating and understanding inter-individual variability in brain-behavior relationships and enhancing prediction performance in neuroimaging research. |
| 536 | _ | _ | |a 5254 - Neuroscientific Data Analytics and AI (POF4-525) |0 G:(DE-HGF)POF4-5254 |c POF4-525 |f POF IV |x 0 |
| 536 | _ | _ | |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) |0 G:(EU-Grant)945539 |c 945539 |f H2020-SGA-FETFLAG-HBP-2019 |x 1 |
| 536 | _ | _ | |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) |0 G:(EU-Grant)785907 |c 785907 |f H2020-SGA-FETFLAG-HBP-2017 |x 2 |
| 536 | _ | _ | |a VirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421) |0 G:(EU-Grant)826421 |c 826421 |f H2020-SC1-DTH-2018-1 |x 3 |
| 536 | _ | _ | |a DFG project G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2025 - 2027 / Forschungszentrum Jülich (OAPKFZJ) (491111487) |0 G:(GEPRIS)491111487 |c 491111487 |x 4 |
| 536 | _ | _ | |a 5232 - Computational Principles (POF4-523) |0 G:(DE-HGF)POF4-5232 |c POF4-523 |f POF IV |x 5 |
| 536 | _ | _ | |a 5231 - Neuroscientific Foundations (POF4-523) |0 G:(DE-HGF)POF4-5231 |c POF4-523 |f POF IV |x 6 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Wischnewski, Kevin J. |0 P:(DE-Juel1)178756 |b 1 |
| 700 | 1 | _ | |a Eickhoff, Simon B |0 P:(DE-Juel1)131678 |b 2 |
| 700 | 1 | _ | |a Popovych, Oleksandr V. |0 P:(DE-Juel1)131880 |b 3 |e Corresponding author |
| 773 | _ | _ | |a 10.31219/osf.io/67zxe |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1033617/files/manuscript.pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:1033617 |p openaire |p open_access |p driver |p VDB |p ec_fundedresources |p dnbdelivery |
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| 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)POF4-5254 |x 0 |
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| 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)POF4-523 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Neuromorphic Computing and Network Dynamics |9 G:(DE-HGF)POF4-5231 |x 2 |
| 914 | 1 | _ | |y 2024 |
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| 980 | _ | _ | |a preprint |
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