An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks

Wu, Taotao; Rifkin, Jared A.; Rayfield, Adam C.; Panzer, Matthew B.; Meaney, David F.

doi:10.1016/j.neuroimage.2022.119002

Cited by 7 publications

(3 citation statements)

References 75 publications

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“…Our KM methodology was largely inspired by Cabral et al, 2011 ., who used KMs in conjunction with the Balloon-Windkessel (BW) model to simulate functional connectivity, optimizing the model parameters to match the measured functional connectivity in a human subject. One difference in our approach was using a model that did not include signal latency among brain areas, an approximation that allowed us to model the dynamics across a far larger number of architectures than previously examined by Cabral or others ( Petkoski and Jirsa, 2019 ; Wu et al, 2022a ). Such a method more closely matched the KM from Allegra Mascaro et al, 2020 that assumed instantaneous coupling between oscillators albeit at low frequencies.…”

Section: Discussionmentioning

confidence: 99%

Brain architecture-based vulnerability to traumatic injury

Rifkin

Rayfield

et al. 2022

Front. Bioeng. Biotechnol.

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The white matter tracts forming the intricate wiring of the brain are subject-specific; this heterogeneity can complicate studies of brain function and disease. Here we collapse tractography data from the Human Connectome Project (HCP) into structural connectivity (SC) matrices and identify groups of similarly wired brains from both sexes. To characterize the significance of these architectural groupings, we examined how similarly wired brains led to distinct groupings of neural activity dynamics estimated with Kuramoto oscillator models (KMs). We then lesioned our networks to simulate traumatic brain injury (TBI) and finally we tested whether these distinct architecture groups’ dynamics exhibited differing responses to simulated TBI. At each of these levels we found that brain structure, simulated dynamics, and injury susceptibility were all related to brain grouping. We found four primary brain architecture groupings (two male and two female), with similar architectures appearing across both sexes. Among these groupings of brain structure, two architecture types were significantly more vulnerable than the remaining two architecture types to lesions. These groups suggest that mesoscale brain architecture types exist, and these architectural differences may contribute to differential risks to TBI and clinical outcomes across the population.

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Section: Discussionmentioning

confidence: 99%

Brain architecture-based vulnerability to traumatic injury

Rifkin

Rayfield

et al. 2022

Front. Bioeng. Biotechnol.

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“…The archetype to explore the synchronization of a system is the Kuramoto model [19], which describes a set of coupled oscillators that interact through a sinusoidal function. Although its simplicity, the Kuramoto model provides a phenomenological description of the problem displaying rich emergent dynamics such as the phase transition from incoherence to synchrony [20][21][22], and provides insight into synchronization process in nature [23][24][25][26]. The Kuramoto model has been widely studied and its variations include the presence of noise [27,28], inertia [29][30][31], weighted coupling [32][33][34], time delay [35,36], resetting [37][38][39], among many others [4,21,22,40,41].…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers have investigated synchronization process in systems that are exposed to severe damage, which implies removing some components of the system. In network science, this effect is modeled with the complete removal of particular groups of links or nodes [26,[50][51][52][53][54][55], where the percolation connectivity threshold defines the limit of the global functionality of the system. Furthermore, different authors have explored synchronization in time-varying topologies with modifications in the network structure occurring at a temporal scale comparable to the characteristic time scale of synchronization [56][57][58][59][60].…”

Section: Introductionmentioning

confidence: 99%

Influence of cumulative damage on synchronization of Kuramoto oscillators on networks

Eraso-Hernandez,

Riascos

2023

J. Phys. A: Math. Theor.

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In this paper, we study the synchronization of identical Kuramoto phase oscillators under cumulative stochastic damage to the edges of networks. We analyze the capacity of coupled oscillators to reach a coherent state from initial random phases. The process of synchronization is a global function performed by a system that gradually changes when the damage weakens individual connections of the network. We explore diverse structures characterized by different topologies. Among these are deterministic networks as a wheel or the lattice formed by the movements of the knight on a chess board, and random networks generated with the Erd\H{o}s-Rényi and Barab\'asi-Albert algorithms. In addition, we study the synchronization times of 109 non-isomorphic graphs with six nodes. The synchronization times and other introduced quantities are sensitive to the impact of damage, allowing us to measure the reduction of the capacity of synchronization and classify the effect of damage in the systems under study. This approach is general and paves the way for the exploration of the effect of damage accumulation in diverse dynamical processes in complex systems.

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Developing a Pipeline to Efficiently Represent Montreal Neurological Institute (MNI)-Based Brain Functional Regions for FE Modeling

Bian,

Mao

2024

Lecture Notes in Bioengineering

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An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks

Cited by 7 publications

References 75 publications

Brain architecture-based vulnerability to traumatic injury

Brain architecture-based vulnerability to traumatic injury

Influence of cumulative damage on synchronization of Kuramoto oscillators on networks

Developing a Pipeline to Efficiently Represent Montreal Neurological Institute (MNI)-Based Brain Functional Regions for FE Modeling

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