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001     908132
005     20220722190615.0
024 7 _ |a 10.25493/CBE0-EQV
|2 doi
037 _ _ |a FZJ-2022-02396
041 _ _ |a English
100 1 _ |a Domhof, Justin W. M.
|0 P:(DE-Juel1)179582
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245 _ _ |a Parcellation-based functional connectivity simulated by personalized whole-brain dynamical models (1.0)
260 _ _ |c 2022
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336 7 _ |a MISC
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336 7 _ |a Dataset
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336 7 _ |a DATA_SET
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520 _ _ |a This dataset contains functional connectomes generated by whole-brain dynamical models for a healthy cohort and 19 brain parcellations. The models were derived from and validated against the parcellation-based empirical structural (SC) and functional connectivities (FC) of individuals, respectively, which have been published as a separate dataset ([DOI: 10.25493/81EV-ZVT](https://doi.org/10.25493/81EV-ZVT)). In the current dataset, two particular models for local dynamics were considered for modeling the mean-field activities of the brain regions, in particular, the resting-state electrical and ultra-slow blood-oxygen-level-dependent dynamics of neuronal populations. Subsequently, the constructed models were simulated, which yielded the simulated activity time series for each brain region. From these time series, the corresponding simulated FC was calculated and compared with the empirical FC of the subject. Finally, the model parameters were optimized via a grid search so that the similarity between the empirical and simulated FC was maximized. The procedure was repeated for 200 subjects, the two models and 19 parcellations, and this dataset includes the corresponding optimal model parameter settings as well as the respective simulated FCs.
536 _ _ |a 5232 - Computational Principles (POF4-523)
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588 _ _ |a Dataset connected to DataCite
650 _ 7 |a Neuroscience
|2 Other
700 1 _ |a Eickhoff, Simon B.
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700 1 _ |a Popovych, Oleksandr V.
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|e Corresponding author
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773 _ _ |a 10.25493/CBE0-EQV
856 4 _ |u https://search.kg.ebrains.eu/instances/1c0c904d-eff0-4149-9c95-85993ce5e587
909 C O |o oai:juser.fz-juelich.de:908132
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914 1 _ |y 2022
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