scholarly journals Bridging the gap between single receptor type activity and whole‐brain dynamics

FEBS Journal ◽  
2021 ◽  
Author(s):  
Dirk Jancke ◽  
Stefan Herlitze ◽  
Morten L. Kringelbach ◽  
Gustavo Deco
Keyword(s):  
Receptor Type ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Single Receptor ◽  
Type Activity

scholarly journals Capturing the non-stationarity of whole-brain dynamics underlying human brain states

NeuroImage ◽  
2021 ◽  
pp. 118551
Author(s):  
J.A. Galadí ◽  
S. Silva Pereira ◽  
Y. Sanz Perl ◽  
M.L. Kringelbach ◽  
I. Gayte ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Brain States

scholarly journals Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson’s disease

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Victor M. Saenger ◽  
Joshua Kahan ◽  
Tom Foltynie ◽  
Karl Friston ◽  
Tipu Z. Aziz ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Underlying Mechanisms ◽  
Deep Brain

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

2020 ◽  
Author(s):  
Kyesam Jung ◽  
Simon B. Eickhoff ◽  
Oleksandr V. Popovych
Keyword(s):  
Data Processing ◽  
Resting State ◽  
Optimal Parameter ◽  
Structural Connectivity ◽  
Model Performance ◽  
Brain Dynamics ◽  
Dynamical Modeling ◽  
Whole Brain ◽  
Brain Connectome ◽  
Network Properties

AbstractDynamical 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.

Towards an Efficient Validation of Dynamical Whole-brain Models

2021 ◽  
Author(s):  
Kevin J. Wischnewski ◽  
Simon B. Eickhoff ◽  
Viktor K. Jirsa ◽  
Oleksandr V. Popovych
Keyword(s):  
Structural Connectivity ◽  
Three Dimensional ◽  
Bayesian Optimization ◽  
High Dimensional ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Phase Oscillators ◽  
Dimensional Parameter ◽  
Whole Brain ◽  
Brain Models

Abstract Simulating the resting-state brain dynamics via mathematical whole-brain models requires an optimal selection of parameters, which determine the model’s capability to replicate empirical data. Since the parameter optimization via a grid search (GS) becomes unfeasible for high-dimensional models, we evaluate several alternative approaches to maximize the correspondence between simulated and empirical functional connectivity. A dense GS serves as a benchmark to assess the performance of four optimization schemes: Nelder-Mead Algorithm (NMA), Particle Swarm Optimization (PSO), Covariance Matrix Adaptation Evolution Strategy (CMAES) and Bayesian Optimization (BO). To compare them, we employ an ensemble of coupled phase oscillators built upon individual empirical structural connectivity of 105 healthy subjects. We determine optimal model parameters from two- and three-dimensional parameter spaces and show that the overall fitting quality of the tested methods can compete with the GS. There are, however, marked differences in the required computational resources and stability properties, which we also investigate before proposing CMAES and BO as efficient alternatives to a high-dimensional GS. For the three-dimensional case, these methods generated similar results as the GS, but within less than 6% of the computation time. Our results contribute to an efficient validation of models for personalized simulations of brain dynamics.

scholarly journals Whole‐brain computational modeling reveals disruption of microscale brain dynamics in HIV infected individuals

2020 ◽  
Vol 42 (1) ◽  
pp. 95-109
Author(s):  
Yuchuan Zhuang ◽  
Zhengwu Zhang ◽  
Madalina Tivarus ◽  
Xing Qiu ◽  
Jianhui Zhong ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Brain Dynamics ◽  
Whole Brain

scholarly journals Will We Hit a Wall? Forecasting Bottlenecks to Whole Brain Emulation Development

2013 ◽  
Vol 4 (3) ◽  
pp. 153-163 ◽  
Author(s):  
Jeff Alstott
Keyword(s):  
Human Brain ◽  
Complex Network ◽  
Human Behavior ◽  
Brain Dynamics ◽  
Human Society ◽  
Whole Brain ◽  
Complex Technology ◽  
The Impact

Abstract Whole brain emulation (WBE) is the possible replication of human brain dynamics that reproduces human behavior. If created, WBE would have significant impact on human society, and forecasts frequently place WBE as arriving within a century. However, WBE would be a complex technology with a complex network of prerequisite technologies. Most forecasts only consider a fraction of this technology network. The unconsidered portions of the network may contain bottlenecks, which are slowly-developing technologies that would impede the development of WBE. Here I describe how bottlenecks in the network can be non-obvious, and the merits of identifying them early. I show that bottlenecks may be predicted even with noisy forecasts. Accurate forecasts of WBE development must incorporate potential bottlenecks, which can be found using detailed descriptions of the WBE technology network. Bottlenecks identification can also increase the impact of WBE researchers by directing effort to those technologies that will immediately affect the timeline of WBE development

Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states

NeuroImage ◽  
2018 ◽  
Vol 169 ◽  
pp. 46-56 ◽  
Author(s):  
Gustavo Deco ◽  
Joana Cabral ◽  
Victor M. Saenger ◽  
Melanie Boly ◽  
Enzo Tagliazucchi ◽  
...  
Keyword(s):  
In Silico ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Brain States

scholarly journals Estimating whole-brain dynamics by using spectral clustering

2016 ◽  
Vol 66 (3) ◽  
pp. 607-627 ◽  
Author(s):  
Ivor Cribben ◽  
Yi Yu
Keyword(s):  
Spectral Clustering ◽  
Brain Dynamics ◽  
Whole Brain

scholarly journals Turbulent-like dynamics in the human brain

2019 ◽  
Author(s):  
Gustavo Deco ◽  
Morten L. Kringelbach
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Information Transfer ◽  
Large Scale ◽  
Brain Dynamics ◽  
Whole Brain ◽  
Large Scale Network ◽  
Maximal Sensitivity ◽  
Scale Network ◽  
Best Fit

SummaryTurbulence facilitates fast energy/information transfer across scales in physical systems. These qualities are important for brain function, but it is currently unknown if the dynamic intrinsic backbone of brain also exhibits turbulence. Using large-scale neuroimaging empirical data from 1003 healthy participants, we demonstrate Kuramoto’s amplitude turbulence in human brain dynamics. Furthermore, we build a whole-brain model with coupled oscillators to demonstrate that the best fit to the data corresponds to a region of maximally developed amplitude turbulence, which also corresponds to maximal sensitivity to the processing of external stimulations (information capability). The model shows the economy of anatomy by following the Exponential Distance Rule of anatomical connections as a cost-of-wiring principle. This establishes a firm link between turbulence and optimal brain function. Overall, our results reveal a way of analysing and modelling whole-brain dynamics that suggests turbulence as the dynamic intrinsic backbone facilitating large scale network communication.

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