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| 001 | 1043577 | ||
| 005 | 20250916202447.0 | ||
| 024 | 7 | _ | |a 10.1038/s41598-025-04511-5 |2 doi |
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| 037 | _ | _ | |a FZJ-2025-02934 |
| 082 | _ | _ | |a 600 |
| 100 | 1 | _ | |a Jung, Kyesam |0 P:(DE-Juel1)178611 |b 0 |u fzj |
| 245 | _ | _ | |a Effective workflow from multimodal MRI data to model-based prediction |
| 260 | _ | _ | |a [London] |c 2025 |b Springer Nature |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 500 | _ | _ | |a This work was supported by the Portfolio Theme Supercomputing and Modeling for the Human Brain by the Helmholtz association, the Human Brain Project and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreements 785907 (HBP SGA2), 945539 (HBP SGA3) and 826421 (VirtualBrainCloud). Open-access publication was funded by the Deutsche Forschungsgemeinschaft (German Research Foundation) − 491111487. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
| 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. |
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| 536 | _ | _ | |a HBP - The Human Brain Project (604102) |0 G:(EU-Grant)604102 |c 604102 |f FP7-ICT-2013-FET-F |x 2 |
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| 700 | 1 | _ | |a Wischnewski, Kevin J. |0 P:(DE-Juel1)178756 |b 1 |u fzj |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 2 |u fzj |
| 700 | 1 | _ | |a Popovych, Oleksandr V. |0 P:(DE-Juel1)131880 |b 3 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1038/s41598-025-04511-5 |g Vol. 15, no. 1, p. 20126 |0 PERI:(DE-600)2615211-3 |n 1 |p 20126 |t Scientific reports |v 15 |y 2025 |x 2045-2322 |
| 856 | 4 | _ | |u https://www.nature.com/articles/s41598-025-04511-5 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1043577/files/s41598-025-04511-5.pdf |y OpenAccess |
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