Function Based Brain Modeling and Simulation of an Ischemic Region in Post-Stroke Patients using the Bidomain

Lopez-Rincon, Alejandro; Cantu, Cesar; Etcheverry, Gibran; Soto, Rogelio; Shimoda, Shingo

doi:10.1016/j.jneumeth.2019.108464

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…The Bidomain model has previously been used to simulate neuronal resting-state potentials during transcranial direct current stimulation 5 and an ischemic region in a post-stroke patient 23,24 . The Bidomain model includes the extracellular and the transmembrane potential, and couples naturally with dynamical models for the transmembrane potential.…”

Section: Introductionmentioning

confidence: 99%

Simulating Epileptic Seizures using the Bidomain Model

Schreiner

Mardal

2021

Preprint

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Epileptic seizures are due to excessive and synchronous neural activity. Extensive modelling of seizures has been done on the neuronal level, but it remains a challenge to scale these models up to whole brain models. Measurements of the brain’s activity over several spatiotemporal scales follow a power-law distribution in terms of frequency. During normal brain activity, the power-law exponent is often found to be around 2 for frequencies between a few Hz and up to 150 Hz, but is higher during seizures and for higher frequencies. The Bidomain model has been used with success in modelling the electrical activity of the heart, but has been explored far less in the context of the brain. This study extends previous models of epileptic seizures on the neuronal level to the whole brain using the Bidomain model. Our approach is evaluated in terms of power-law distributions. The electric potentials were simulated in 7 idealized 2D models and 3 MRI-derived 3D patient-specific models. Computed electric potentials were found to follow power-law distribtions with slopes ranging from 2 to 5 for frequencies greater than 10-30 Hz.

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Section: Introductionmentioning

confidence: 99%

Simulating Epileptic Seizures using the Bidomain Model

Schreiner

Mardal

2021

Preprint

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“…Swelling process in the brain induce large deformation in the brain tissue [21,68,43] and different mathematical and computational models have been developed to analyze the mechanical response of the brain under such internal loads [26,15,44]. Computational models have been also used to evaluate mechanical deformations during decompressive craniotomies [16,19,70].…”

Section: Introductionmentioning

confidence: 99%

Topological features dictate the mechanics of the mammalian brains

Saéz

Duñó

Sun

et al. 2020

International Journal of Mechanical Sciences

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Understanding brain mechanics is crucial in the study of pathologies involving brain deformations such as tumor, strokes, or in traumatic brain injury. Apart from the intrinsic mechanical properties of the tissue, the topology and geometry of the mammalian brains are particularly important for the mechanical response of the brain. We use computational methods in combination with geometric models to understand the role of these features. We find that the geometric quantifiers such as the gyrification index play a fundamental role in the overall mechanical response of the brain.We further demonstrate that topological diversity in brain models is more important than differences in mechanical properties: Topological differences modify not only the stresses and strains in the brain but also its spatial distribution. Therefore, computational brain models should always include detailed geometric information to generate accurate mechanical predictions. These results suggest that mammalian brain gyrification acts as a damping system to reduce mechanical damage in largemass brain mammals.Our results are relevant in several areas of science and engineering related to brain mechanics, including the study of tumor growth, the understanding of brain folding, and the analysis of traumatic brain injuries.

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“…The physical mechanism underlying the high complexity of human brain behavior undoubtedly has attracted much attention in recent years [ 1 , 2 ]. Mathematical and computational models have been proposed to explain some particular brain’s behaviors [ 3 – 5 ]. One of those behaviors is the ability to decide on a specific situation, e.g., when two persons play a specific game.…”

Section: Introductionmentioning

confidence: 99%

Modified Asano-Ohya-Khrennikov quantum-like model for decision-making process in a two-player game with nonlinear self- and cross-interaction terms of brain’s amygdala and prefrontal-cortex

2020

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In this report, we propose a modification on the Asano-Ohya-Khrennikov quantum-like decision-making process model of a two-player game by adding additional nonlinear terms to the related comparison step dynamical equation. The additions are in the form of a self-interaction and cross-interaction of the brain’s amygdala and prefrontal cortex. We show that the cross-interaction significantly determines the final decision of a player, whether it becomes a rational or an irrational choice. In contrast, the nonlinear self-interaction term provides a feedback mechanism that speeds up the corresponding decision-making process. We also suggest the form of expectation values of the overall reaction rate coefficients of those nonlinear terms by making an analogy with the original model formulation.

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Function Based Brain Modeling and Simulation of an Ischemic Region in Post-Stroke Patients using the Bidomain

Cited by 6 publications

References 27 publications

Simulating Epileptic Seizures using the Bidomain Model

Simulating Epileptic Seizures using the Bidomain Model

Topological features dictate the mechanics of the mammalian brains

Modified Asano-Ohya-Khrennikov quantum-like model for decision-making process in a two-player game with nonlinear self- and cross-interaction terms of brain’s amygdala and prefrontal-cortex

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