Multiscale co-simulation of deep brain stimulation with brain networks in neurodegenerative disorders

Shaheen, Hina; Pal, Swadesh; Melnik, Roderick

doi:10.1016/j.brain.2022.100058

Cited by 5 publications

(4 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also provided details on the coupled dynamics involving cytokines, astrocytes, and microglia, and applied the developed ideas to the necrosis factor alpha (TNF-α) inhibitor and anti-Aβ drug and analyzed their influence on the brain cells, suggesting conditions under which the drug can prevent cell damage. We have also brought additional features to this coupling, highlighting further the importance of astrocytes [19]. Due to a major challenge of brain network modelling, its multi-scale spatio-temporal nature, covering scales from synapses to the whole brain, it was necessary to develop a particular mathematical framework that would account for the coupled multiphysics and biochemical activities which spread through such a complex system shape brain capacity inside a structure-function relationship.…”

Section: Coupling Neuronal and Glial Cellsmentioning

confidence: 99%

“…As it was shown in [13], such considerations also require nonlocal models. At the same time, at larger scales, such as the whole brain modelling, the current trend focuses on various aspects of multiscale co-simulation protocols, in particular in the context of various neurosurgical procedures such as Deep Brain Stimulation [17,7].…”

Section: Working Memory In Neurodegenerative Diseases Stochas-tic Mod...mentioning

confidence: 99%

See 1 more Smart Citation

Coupled Spatio-Temporal Dynamics and Nonlocality in Advanced Mathematical Models for the Analysis of Complex Neurodegenerative Disease Pathologies

Pal,

Melnik

2023

10th Edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering

View full text Add to dashboard Cite

One in six of the world's population has to deal with neurodegenerative disorders, and while medical devices exist to detect, prevent, and treat such disorders, some fundamentals of the progression of associated diseases remain ambiguous. In this contribution, we focus on Alzheimer's disease (AD), where amyloid-beta (Aβ) and tau proteins are among the main contributors to the development or propagation of AD. The Aβ proteins clump together to form plaques and disrupt cell functions. Moreover, the abnormal chemical change in the brain helps to build sticky tau tangles that block the neuron's transport system. Astrocytes generally maintain a healthy balance in the brain by clearing the Aβ toxic plaques. Even so, over-activated astrocytes release chemokines and cytokines and also react to pro-inflammatory cytokines, further increasing the production of Aβ. We have provided details of a novel coupled mathematical model that can capture astrocytes' dual behaviour, emphasizing the importance of spatio-temporal coupling and nonlocality. We have demonstrated that the disease propagation depends on memory effects, that is the disease's earlier status, which involves non-Markovian processes. We have explained how to integrate brain connectome data in the network model and to study this effect, as well as the dual role of astrocytes as a coupled phenomenon. Depending on toxic loads in the brain, we have also discussed details of the analysis of the neuronal damage in the brain. We have explained how the memory effect can slow down the propagation of toxic proteins in the brain, decreasing the rate of neuronal damage. Representative numerical examples have been given, and special attention has been paid to nonequilibrium considerations and stochastic modelling frameworks in the study of neurodegenerative diseases.

show abstract

Section: Coupling Neuronal and Glial Cellsmentioning

confidence: 99%

Section: Working Memory In Neurodegenerative Diseases Stochas-tic Mod...mentioning

confidence: 99%

Coupled Spatio-Temporal Dynamics and Nonlocality in Advanced Mathematical Models for the Analysis of Complex Neurodegenerative Disease Pathologies

Pal,

Melnik

2023

10th Edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Some studies have attempted to unravel these mechanisms, showing the importance of the cerebellum for brain dynamics [68], the relevance of cortico-subcortical interaction for shaping dynamical functional connectivity [93] and the relevance of neurotransmission [65,94]. Additionally, other studies are paving the way towards multiscale computational models in which subcortical areas are implemented with a further spatiotemporal level of detail [95][96][97][98]. These approaches could be an interesting path to extend our knowledge regarding the potential role of the thalamus (and other activating brain regions) in whole brain simulations.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Modeling the role of the thalamus in resting-state functional connectivity: Nature or structure

et al. 2023

View full text Add to dashboard Cite

The thalamus is a central brain structure that serves as a relay station for sensory inputs from the periphery to the cortex and regulates cortical arousal. Traditionally, it has been regarded as a passive relay that transmits information between brain regions. However, recent studies have suggested that the thalamus may also play a role in shaping functional connectivity (FC) in a task-based context. Based on this idea, we hypothesized that due to its centrality in the network and its involvement in cortical activation, the thalamus may also contribute to resting-state FC, a key neurological biomarker widely used to characterize brain function in health and disease. To investigate this hypothesis, we constructed ten in-silico brain network models based on neuroimaging data (MEG, MRI, and dwMRI), and simulated them including and excluding the thalamus. and raising the noise into thalamus to represent the afferences related to the reticular activating system (RAS) and the relay of peripheral sensory inputs. We simulated brain activity and compared the resulting FC to their empirical MEG counterparts to evaluate model’s performance. Results showed that a parceled version of the thalamus with higher noise, able to drive damped cortical oscillators, enhanced the match to empirical FC. However, with an already active self-oscillatory cortex, no impact on the dynamics was observed when introducing the thalamus. We also demonstrated that the enhanced performance was not related to the structural connectivity of the thalamus, but to its higher noisy inputs. Additionally, we highlighted the relevance of a balanced signal-to-noise ratio in thalamus to allow it to propagate its own dynamics. In conclusion, our study sheds light on the role of the thalamus in shaping brain dynamics and FC in resting-state and allowed us to discuss the general role of criticality in the brain at the mesoscale level.

show abstract

“…The former toolbox focuses on rapid development for scientific use cases, whereas the latter focuses on optimisation of co-simulation performance and applies the co-simulation design pattern presented in this study. An illustrative example of co-simulation of multiscale models using TVB Multiscale co-simulation is virtual deep brain stimulation (Meier et al, 2022 ; Shaheen et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Multiscale co-simulation design pattern for neuroscience applications

Kusch,

Diaz-Pier,

Klijn

et al. 2024

Front. Neuroinform.

View full text Add to dashboard Cite

Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.

show abstract

Multiscale co-simulation of deep brain stimulation with brain networks in neurodegenerative disorders

Cited by 5 publications

References 89 publications

Coupled Spatio-Temporal Dynamics and Nonlocality in Advanced Mathematical Models for the Analysis of Complex Neurodegenerative Disease Pathologies

Coupled Spatio-Temporal Dynamics and Nonlocality in Advanced Mathematical Models for the Analysis of Complex Neurodegenerative Disease Pathologies

Modeling the role of the thalamus in resting-state functional connectivity: Nature or structure

Multiscale co-simulation design pattern for neuroscience applications

Contact Info

Product

Resources

About