TY  - CONF
AU  - Domhof, Justin
AU  - Jung, Kyesam
AU  - Eickhoff, Simon
AU  - Popovych, Oleksandr
TI  - Parcellation-induced Variation of Empirical and Simulated Functional Brain Connectivity
PB  - Heinrich-Heine University Düsseldorf
M1  - FZJ-2021-02716
PY  - 2021
AB  - Recent developments of large-scale whole-brain network models have demonstrated their great potential when investigating resting-state brain activity which is believed to be highly personalised (Bansal 2018, Popovych 2019). So far, however, it has not been systematically investigated how alternating derivations of the empirical brain connectivity from MRI data, which serves as the input to these models, influence the modelling results. Here, we study the influence from an element indispensable in such calculations: the brain parcellation scheme that reduces the dimensionality of investigated brain networks by grouping thousands of voxels together into a few hundred brain regions.MethodWe extracted empirical structural and functional connectivities from the dwMRI and fMRI data of 200 subjects (96 males, age 28.5 ± 3.5 years) included in the Human Connectome Project dataset (Van Essen, 2012, 2013) using 19 different freely available brain parcellations. Subsequently, for each combination of parcellation and subject, we constructed two dynamical whole-brain models on the basis of the empirical structural connectivity. The first is a model of coupled phase oscillators, and the second is a network of Wilson-Cowan neural mass models (Wilson, 1972). Both models were used to simulate the resting-state functional connectivity, which was correlated to its empirical counterpart to obtain an index characterising their similarity. By varying the free parameters included in both models, we obtained the maximised similarity index or goodness-of-fit for every pair of parcellation and subject. To find an explanation for any differences observed in the goodness-of-fit for varying parcellations, we derived 9 graph-theoretical network statistics characterising the degree and closeness centrality distribution, the modularity and the global efficiency from both the structural and functional connectomes (Rubinov, 2010). Finally, through the use of principal component analysis combined with linear regressions, we related the goodness-of-fits to the extracted networks statistics on both the level of the group and individual subjects.ResultsOur study revealed large deviations in the goodness-of-fit across parcellations (Fig. 1A). By regressing the group-averages corresponding to both models, we showed that this heterogeneity does not depend on the considered models. Next, we conducted a principal component analysis of the parcellation-based group-averaged network statistics and structure-function relationship of the empirical connectomes. The scores of the first principal component were regressed with the group-averaged goodness-of-fits, which explained approximately 75% and 86% of the parcellation-induced variation in the goodness-of-fits of the phase oscillator and neural mass model, respectively (Fig. 1B). Including more PCs in the regression model only marginally increased this proportion (Fig. 1C). However, applying the same approach to the interindividual variation in the goodness-of-fit for every parcellation in isolation resulted in low explanatory power and its high variability across parcellations and models (Fig. 2A). Finally, a multivariate linear model was used to regress the network statistics directly with the goodness-of-fits of the individuals. This change in strategy led to an increase in explained variance, which was nevertheless still smaller than 30% and highly variable across parcellations (Fig. 2B).ConclusionOur results demonstrated that the goodness-of-fit of the model to empirical data was influenced greatly by the parcellation both at the level of the entire cohort and the individual subjects. We furthermore provided evidence that graph-theoretical network properties derived from the empirical data can explain group-averaged but not interindividual variations in this respect. These findings may contribute to mechanisms explaining how the validation of the whole-brain models works, and how the fitting results depend on the choice of parcellation.
T2  - The 27th Annual Meeting of the Organization for Human Brain Mapping
CY  - 21 Jun 2021 - 25 Jun 2021, Virtual (Virtual)
Y2  - 21 Jun 2021 - 25 Jun 2021
M2  - Virtual, Virtual
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/893375
ER  -