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Neurite, a finite difference large scale parallel program for the simulation of electrical signal propagation in neurites under mechanical loading.

García-Grajales JA, Rucabado G, García-Dopico A, Peña JM, Jérusalem A - PLoS ONE (2015)

Bottom Line: The solvers implemented in Neurite--explicit and implicit--were therefore parallelized using graphics processing units in order to reduce the burden of the simulation costs of large scale scenarios.This paper provides the details of the parallel implementation of Neurite, along with three different application examples: a long myelinated axon, a segmented dendritic tree, and a damaged axon.The capabilities of the program to deal with large scale scenarios, segmented neuronal structures, and functional deficits under mechanical loading are specifically highlighted.

View Article: PubMed Central - PubMed

Affiliation: IMDEA Materials Institute, Getafe, Madrid, Spain; DATSI Computer Science, Universidad Politécnica de Madrid, Madrid, Spain; Mathematical Institute, University of Oxford, Oxford, UK.

ABSTRACT
With the growing body of research on traumatic brain injury and spinal cord injury, computational neuroscience has recently focused its modeling efforts on neuronal functional deficits following mechanical loading. However, in most of these efforts, cell damage is generally only characterized by purely mechanistic criteria, functions of quantities such as stress, strain or their corresponding rates. The modeling of functional deficits in neurites as a consequence of macroscopic mechanical insults has been rarely explored. In particular, a quantitative mechanically based model of electrophysiological impairment in neuronal cells, Neurite, has only very recently been proposed. In this paper, we present the implementation details of this model: a finite difference parallel program for simulating electrical signal propagation along neurites under mechanical loading. Following the application of a macroscopic strain at a given strain rate produced by a mechanical insult, Neurite is able to simulate the resulting neuronal electrical signal propagation, and thus the corresponding functional deficits. The simulation of the coupled mechanical and electrophysiological behaviors requires computational expensive calculations that increase in complexity as the network of the simulated cells grows. The solvers implemented in Neurite--explicit and implicit--were therefore parallelized using graphics processing units in order to reduce the burden of the simulation costs of large scale scenarios. Cable Theory and Hodgkin-Huxley models were implemented to account for the electrophysiological passive and active regions of a neurite, respectively, whereas a coupled mechanical model accounting for the neurite mechanical behavior within its surrounding medium was adopted as a link between electrophysiology and mechanics. This paper provides the details of the parallel implementation of Neurite, along with three different application examples: a long myelinated axon, a segmented dendritic tree, and a damaged axon. The capabilities of the program to deal with large scale scenarios, segmented neuronal structures, and functional deficits under mechanical loading are specifically highlighted.

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Segmented dendritic tree [51].This adapted version is visualized with Vaa3D [63]. The tree consists of 57 branching points and 879 elements.
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pone.0116532.g005: Segmented dendritic tree [51].This adapted version is visualized with Vaa3D [63]. The tree consists of 57 branching points and 879 elements.

Mentions: Neurite is able to load segmented neuronal geometries, with only few adaptations. For this example, a segmented structure was taken from the NeuroMorpho.Org database [49]. In this repository, segmented neurons including the dendrites, apical dendrite, soma and axon can be downloaded. For this example, a pyramidal neuron of rat hippocampus was chosen (NeuroMorpho.org ID: NMO_00223, [51]), and the simulation was reduced to the dendritic tree using CT passive elements with arbitrary properties, see Fig. 5 (the soma is shown for illustration but was not included in the simulation).


Neurite, a finite difference large scale parallel program for the simulation of electrical signal propagation in neurites under mechanical loading.

García-Grajales JA, Rucabado G, García-Dopico A, Peña JM, Jérusalem A - PLoS ONE (2015)

Segmented dendritic tree [51].This adapted version is visualized with Vaa3D [63]. The tree consists of 57 branching points and 879 elements.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4334526&req=5

pone.0116532.g005: Segmented dendritic tree [51].This adapted version is visualized with Vaa3D [63]. The tree consists of 57 branching points and 879 elements.
Mentions: Neurite is able to load segmented neuronal geometries, with only few adaptations. For this example, a segmented structure was taken from the NeuroMorpho.Org database [49]. In this repository, segmented neurons including the dendrites, apical dendrite, soma and axon can be downloaded. For this example, a pyramidal neuron of rat hippocampus was chosen (NeuroMorpho.org ID: NMO_00223, [51]), and the simulation was reduced to the dendritic tree using CT passive elements with arbitrary properties, see Fig. 5 (the soma is shown for illustration but was not included in the simulation).

Bottom Line: The solvers implemented in Neurite--explicit and implicit--were therefore parallelized using graphics processing units in order to reduce the burden of the simulation costs of large scale scenarios.This paper provides the details of the parallel implementation of Neurite, along with three different application examples: a long myelinated axon, a segmented dendritic tree, and a damaged axon.The capabilities of the program to deal with large scale scenarios, segmented neuronal structures, and functional deficits under mechanical loading are specifically highlighted.

View Article: PubMed Central - PubMed

Affiliation: IMDEA Materials Institute, Getafe, Madrid, Spain; DATSI Computer Science, Universidad Politécnica de Madrid, Madrid, Spain; Mathematical Institute, University of Oxford, Oxford, UK.

ABSTRACT
With the growing body of research on traumatic brain injury and spinal cord injury, computational neuroscience has recently focused its modeling efforts on neuronal functional deficits following mechanical loading. However, in most of these efforts, cell damage is generally only characterized by purely mechanistic criteria, functions of quantities such as stress, strain or their corresponding rates. The modeling of functional deficits in neurites as a consequence of macroscopic mechanical insults has been rarely explored. In particular, a quantitative mechanically based model of electrophysiological impairment in neuronal cells, Neurite, has only very recently been proposed. In this paper, we present the implementation details of this model: a finite difference parallel program for simulating electrical signal propagation along neurites under mechanical loading. Following the application of a macroscopic strain at a given strain rate produced by a mechanical insult, Neurite is able to simulate the resulting neuronal electrical signal propagation, and thus the corresponding functional deficits. The simulation of the coupled mechanical and electrophysiological behaviors requires computational expensive calculations that increase in complexity as the network of the simulated cells grows. The solvers implemented in Neurite--explicit and implicit--were therefore parallelized using graphics processing units in order to reduce the burden of the simulation costs of large scale scenarios. Cable Theory and Hodgkin-Huxley models were implemented to account for the electrophysiological passive and active regions of a neurite, respectively, whereas a coupled mechanical model accounting for the neurite mechanical behavior within its surrounding medium was adopted as a link between electrophysiology and mechanics. This paper provides the details of the parallel implementation of Neurite, along with three different application examples: a long myelinated axon, a segmented dendritic tree, and a damaged axon. The capabilities of the program to deal with large scale scenarios, segmented neuronal structures, and functional deficits under mechanical loading are specifically highlighted.

Show MeSH
Related in: MedlinePlus