Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates

Endo, Hidenori; Hiroe, Nobuo; Yamashita, Okito

doi:10.3389/fncom.2019.00091

Cited by 20 publications

(30 citation statements)

References 40 publications

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“…By comparing the simulated and empirical data, we can address the model performance as given by the results of the model fitting and thoroughly explore the model parameters and dynamics. Consequently, we can apply the model validation to evaluate the data processing by searching for the optimal model parameters that provide the best fitting of the model against the empirical data [19][20][21]. Such an evaluation procedure can be repeated for several modeling conditions, where the parameters of the data processing are varied.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the derivation of the whole-brain models essentially relies on the underlying network calculated from the whole-brain empirical structural connectivity (SC) that provides the brain architecture serving as a backbone of the brain dynamics [19][20][21]23]. Due to the lack of the ground truth and standardized data processing for the whole-brain SC-networks, it is difficult to evaluate whether the selected parameters of the data processing for WBT density (e.g., the number of WBT streamlines) are reliably reflecting the brain architecture, and what are the optimal values for modeling (similarity between simulated and empirical data).…”

Section: Introductionmentioning

confidence: 99%

“…The broad spectrum of the computational models used for simulation of the brain dynamics ranges from the micro-to the macro-scale [21,[26][27][28][29][30]. Besides the computational modeling concepts, the responses of brain regions can be considered as a harmonized signal [31].…”

Section: Introductionmentioning

confidence: 99%

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

Jung

Eickhoff

Popovych

2020

Preprint

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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.Author summaryThe human brain connectome at macro level provides an anatomical constitution of inter-regional connections through the white matter in the brain. Understanding the brain dynamics grounded on the structural architecture is one of the most studied and important topics actively debated in the neuroimaging research. However, the ground truth for the adequate processing and reconstruction of the human brain connectome in vivo is absent, which is crucial for evaluation of the results of the data-driven as well as model-based approaches to brain investigation. In this study we thus evaluate the effect of the whole-brain tractography density on the structural brain architecture by varying the number of total axonal fiber streamlines. The obtained results are validated throughout the dynamical modeling of the resting-state brain dynamics. We found that the tractography density may strongly affect the graph-theoretical network properties of the structural connectome. The obtained results also show that a dense whole-brain tractography is not always the best condition for the modeling, which depends on a selected brain parcellation used for the calculation of the structural connectivity and derivation of the model network. Our findings provide suggestions for the optimal data processing for neuroimaging research and brain modeling.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jung

Eickhoff

Popovych

2020

Preprint

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“…In the past, whole-brain models have been employed in a wide range of problems, including demonstrating the ability of whole-brain models to reproduce BOLD correlations from functional magnetic resonance imaging (fMRI) during resting-state [9,10] and sleep [11], explaining features of EEG [12] and MEG [5,6] recordings, studying the role of signal transmission delays between brain areas [13,14], the differential effects of neuromodulators [7,15], modeling electrical stimulation of the brain in-silico [16][17][18][19], or explaining the propagation of brain waves [20] such as in slow-wave sleep [21]. Previous work often focused on finding the parameters of optimal working points of a wholebrain model, given a functional dataset [22].…”

Section: Introductionmentioning

confidence: 99%

neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

2021

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Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

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“…In the past, whole-brain models have been employed in a wide range of problems, including demonstrating the ability of whole-brain models to reproduce BOLD correlations from functional magnetic resonance imaging (fMRI) during restingstate 9,10 and sleep 11 , explaining features of EEG 12 and MEG 5,7 recordings, studying the role of signal transmission delays between brain areas 13,14 , the differential effects of neuromodulators 8,15 , modeling electrical stimulation of the brain insilico [16][17][18] , or explaining the propagation of brain waves 19 such as in slow-wave sleep 20 . Previous work often focused on finding the parameters of optimal working points of a whole-brain model, given a functional dataset 21 .…”

Section: Introductionmentioning

confidence: 99%

neurolib: a simulation framework for whole-brain neural mass modeling

Cakan

Jajcay

Obermayer

2021

Preprint

View full text Add to dashboard Cite

neurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e. the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is possible using a parameter exploration module, which allows one to characterize the model's behavior given a set of changing parameters. An optimization module can fit a model to multimodal empirical data using an evolutionary algorithm. neurolib is designed to be extendable such that custom neural mass models can be implemented easily, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure-function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

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Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates

Cited by 20 publications

References 40 publications

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

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

neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

neurolib: a simulation framework for whole-brain neural mass modeling

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