Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

Jung, Kyesam; Popovych, Oleksandr V.; Eickhoff, Simon B.

doi:10.1101/2020.12.03.410688

Preprint

FZJ-2020-04941

Tractography density affects whole-brain structural architecture and resting-state dynamical modeling

Jung, K. (First author)FZJ* ; Eickhoff, S. B.FZJ* ; Popovych, O. V. (Corresponding author)FZJ*

2020
Cold Spring Harbor Laboratory, NY Cold Spring Harbor

bioRxiv beta (2020) [10.1101/2020.12.03.410688]

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Please use a persistent id in citations: http://hdl.handle.net/2128/26373 doi:10.1101/2020.12.03.410688

Abstract: 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 functional connectomes 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 the whole-brain mathematical models and search for the optimal parameter settings. On this way, we simulate the functional connectivity by systems of coupled oscillators, where the underlying network is constructed from the empirical SC and evaluate the performance of the models for varying parameters of data processing. 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 properties and distinct model fitting modalities. We observed that the graph-theoretical network properties of structural connectome can be affected by varying tractography density and strongly relate to the model performance. We explored free parameters of the considered models and found the optimal parameter configurations, where the model dynamics closely replicates the empirical data. We also found that the optimal number of the total streamlines of WBT can vary for different brain atlases. Consequently, we suggest a way how 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 be stratified into subgroups with divergent behaviors induced by the varying number of WBT streamlines such that different recommendations can be made with respect to the data processing for individual subjects and brain parcellations.

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