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Title: Dynamic properties of simulated brain network models and empirical resting-state data
Brain network models (BNMs) have become a promising theoretical framework for simulating signals that are representative of whole-brain activity such as resting-state fMRI. However, it has been difficult to compare the complex brain activity obtained from simulations to empirical data. Previous studies have used simple metrics to characterize coordination between regions such as functional connectivity. We extend this by applying various different dynamic analysis tools that are currently used to understand empirical resting-state fMRI (rs-fMRI) to the simulated data. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the brain network model. We conclude that the dynamic properties that explicitly examine patterns of signal as a function of time rather than spatial coordination between different brain regions in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole-brain activity.  more » « less
Award ID(s):
1822606
PAR ID:
10110685
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Network Neuroscience
Volume:
3
Issue:
2
ISSN:
2472-1751
Page Range / eLocation ID:
405 to 426
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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