Parcellation-induced variation of empirical and simulated brain connectomes at group and subject levels

Recent developments of whole-brain models have demonstrated their potential when investigating resting-state brain activity. However, it has not been systematically investigated how alternating derivations of the empirical structural and functional connectivity, serving as the model input, from MRI data influence modeling results. Here, we study the influence from one major element: the brain parcellation scheme that reduces the dimensionality of brain networks by grouping thousands of voxels into a few hundred brain regions. We show graph-theoretical statistics derived from the empirical data and modeling results exhibiting a high heterogeneity across parcellations. Furthermore, the network properties of empirical brain connectomes explain the lion’s share of the variance in the modeling results with respect to the parcellation variation. Such a clear-cut relationship is not observed at the subject-resolved level per parcellation. Finally, the graph-theoretical statistics of the simulated connectome correlate with those of the empirical functional connectivity across parcellations. However, this relation is not one-to-one, and its precision can vary between models. Our results imply that network properties of both empirical connectomes can explain the goodness-of-fit of whole-brain models to empirical data at a global group level but not at a single-subject level, which provides further insights into the personalization of whole-brain models

Parcellation-based functional connectivity simulated by personalized whole-brain dynamical models (1.0)

This dataset contains functional connectomes generated by whole-brain dynamical models for a healthy cohort and 19 brain parcellations. The models were derived from and validated against the parcellation-based empirical structural (SC) and functional connectivities (FC) of individuals, respectively, which have been published as a separate dataset ([DOI: 10.25493/81EV-ZVT](https://doi.org/10.25493/81EV-ZVT)). In the current dataset, two particular models for local dynamics were considered for modeling the mean-field activities of the brain regions, in particular, the resting-state electrical and ultra-slow blood-oxygen-level-dependent dynamics of neuronal populations. Subsequently, the constructed models were simulated, which yielded the simulated activity time series for each brain region. From these time series, the corresponding simulated FC was calculated and compared with the empirical FC of the subject. Finally, the model parameters were optimized via a grid search so that the similarity between the empirical and simulated FC was maximized. The procedure was repeated for 200 subjects, the two models and 19 parcellations, and this dataset includes the corresponding optimal model parameter settings as well as the respective simulated FCs

Reliability and subject specificity of personalized whole-brain dynamical models

Dynamical whole-brain models were developed to link structural (SC) and functional connectivity (FC) together into one framework.Nowadays, they are used to investigate the dynamical regimes of the brain and how these relate to behavioral, clinical and demographic traits.However, there is no comprehensive investigation on how reliable and subject specific the modeling results are given the variability of the empirical FC.In this study, we show that the parameters of these models can be fitted with a "poor" to "good" reliability depending on the exact implementation of the modeling paradigm.We find, as a general rule of thumb, that enhanced model personalization leads to increasingly reliable model parameters.In addition, we observe no clear effect of the model complexity evaluated by separately sampling results for linear, phase oscillator and neural mass network models.In fact, the most complex neural mass model often yields modeling results with "poor" reliability comparable to the simple linear model, but demonstrates an enhanced subject specificity of the model similarity maps.Subsequently, we show that the FC simulated by these models can outperform the empirical FC in terms of both reliability and subject specificity.For the structure-function relationship, simulated FC of individual subjects may be identified from the correlations with the empirical SC with an accuracy up to 70\%, but not vice versa for non-linear models.We sample all our findings for 8 distinct brain parcellations and 6 modeling conditions and show that the parcellation-induced effect is much more pronounced for the modeling results than for the empirical data.In sum, this study provides an exploratory account on the reliability and subject specificity of dynamical whole-brain models and may be relevant for their further development and application.In particular, our findings suggest that the application of the dynamical whole-brain modeling should be tightly connected with an estimate of the reliability of the results

Parcellation-based resting-state blood-oxygen-level-dependent (BOLD) signals of a healthy cohort (v1.0)

Resting-state functional connectivity (FC) is frequently used to predict behavioral, clinical and demographic subject traits. This type of brain connectome can be derived from blood-oxygen-level-dependent (BOLD) signals that reflect the activation of individual brain regions parcellated according to a given brain atlas. Deriving FC from BOLD signals typically involves the estimation of the amount of synchronized coactivations between the BOLD time series of different brain regions. However, several measures of synchronization exist and which one of these metrics is suited best may deviate from study to study. In parallel, the appropriate selection of the brain parcellation is nowadays also still an open issue. This dataset hence comprises the region-based BOLD signals extracted from the resting-state functional magnetic resonance imaging (fMRI) data of 200 healthy subjects included in the Human Connectome Project. The time series were extracted for 20 different state-of-the-art parcellations. The neuroimaging community may use the data of this repository to study, for example, how different measures of synchronization affect the resting-state FC under various parcellation conditions

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