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| 001 | 905257 | ||
| 005 | 20220131120324.0 | ||
| 037 | _ | _ | |a FZJ-2022-00540 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Jung, Kyesam |0 P:(DE-Juel1)178611 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a INM & IBI Retreat 2021, Forschungszentrum Jülich |c Virtual Conference |d 2021-10-05 - 2021-10-06 |w Germany |
| 245 | _ | _ | |a Tractography density affects whole-brain structural architecture and resting-state dynamical modeling |
| 260 | _ | _ | |c 2021 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
| 336 | 7 | _ | |a Poster |b poster |m poster |0 PUB:(DE-HGF)24 |s 1642750467_26828 |2 PUB:(DE-HGF) |x After Call |
| 520 | _ | _ | |a Dynamical modeling of the resting-state brain dynamics essentially relies on the empirical neuroimaging data utilized for the model derivation and validation. There is however still no standardized data processing for magnetic resonance imaging pipelines and the structural and functionalconnectomes involved in the models. In this study, we thus address how the parameters of diffusion-weighted data processing for structural connectivity (SC) can influence the validation results of thewhole-brain mathematical models informed by SC. For this, we introduce a set of simulation conditions including the varying number of total streamlines of the whole-brain tractography (WBT)used for extraction of SC, cortical parcellations based on functional and anatomical brain propertiesand distinct model fitting modalities. The main objective of this study is to explore how the qualityof the model validation can vary across the considered simulation conditions. We observed that thegraph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We also found that the optimal numberof the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a wayhow to improve the model performance based on the network properties and the optimal parameter configurations from multiple WBT conditions. Furthermore, the population of subjects can bestratified into subgroups with divergent behaviors induced by the varying WBT density such thatdifferent recommendations can be made with respect to the data processing for individual subjectsand brain parcellations. Consequently, we list a few tentative guidelines to possible evaluation ofpersonalized optimal number of the WBT streamlines for the whole-brain model of the resting-statebrain dynamics. |
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| 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 3 |
| 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 4 |
| 536 | _ | _ | |a VirtualBrainCloud - Personalized Recommendations for Neurodegenerative Disease (826421) |0 G:(EU-Grant)826421 |c 826421 |f H2020-SC1-DTH-2018-1 |x 5 |
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| 856 | 4 | _ | |u https://events.hifis.net/event/161/ |
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| 914 | 1 | _ | |y 2021 |
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