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| Home > Publications database > Impact of Brain Parcellation and Empirical Data on Modeling of the Resting-State Brain Dynamics > print |
| 001 | 905236 | ||
| 005 | 20220131120322.0 | ||
| 037 | _ | _ | |a FZJ-2022-00519 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Popovych, Oleksandr |0 P:(DE-Juel1)131880 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a SIAM Conference on Applications of Dynamical Systems (DS21) |c Virtual Conference |d 2021-05-23 - 2021-05-27 |w USA |
| 245 | _ | _ | |a Impact of Brain Parcellation and Empirical Data on Modeling of the Resting-State Brain Dynamics |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
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| 520 | _ | _ | |a Modern approaches to investigation of complex brain dynamics suggest to represent the brain as a functional network where nodes encapsulate brain-region-specific function while edges consolidate the structural or functional connectivity among these regions. Brain regions can be delimited using a parcellation, i.e., brain atlas. There is however no consensus on which brain atlas is more adequate for one or another analysis. We address this problem by a dynamical modeling approach, where the resting-state brain dynamics is simulated by the whole-brain personalized models derived from and validated against empirical neuroimaging structural and functional data. We investigate the impact of the fitting modalities and brain atlases based on distinct anatomical and functional parcellation techniques. We show that these simulation conditions may strongly influence the modeling results including the quality of the model fitting and structure of the model parameter space. We also assess the variation of the fitting results across subjects and parcellations and observe that variation of selected data indices extracted from the experimental data may greatly account for the variations in the fitting results. At this, a few correlative types of the data variables can be distinguished depending on their intra- and inter-parcellation explanatory power. The obtained results can contribute to the improvement of the resting-state brain modeling and data analytics. |
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| 856 | 4 | _ | |u https://meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=71474 |
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