Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling.
Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling.
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DOI：
10.1038/s41598-021-02298-9
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发表时间：
2021-11-26
影响因子：
4.6
通讯作者：
Fitzpatrick CK
中科院分区：
文献类型：
--
作者：
Volk VL;Hamilton LD;Hume DR;Shelburne KB;Fitzpatrick CK
神经肌肉骨骼(NMS)模型可以帮助研究神经和肌肉骨骼系统之间的相互影响。这些计算模型有助于研究肌肉骨骼和神经退行性疾病的机制和治疗。在这项研究中，我们提出了一个预测性的NMS模型，该模型使用嵌入在有限元(FE)框架中的神经结构来模拟肌肉的激活。将先前开发的运动神经元的神经肌肉模型嵌入到简单的FE肌肉骨骼模型中。在有限元NMS模型中模拟文献中的输入刺激曲线，以验证软件平台的有效集成。对模型的运动单位招募和速率编码能力进行了评估。综合模型再现了先前发表的输出肌力，平均误差为0.0435 N。综合模型有效地展示了基于运动单位放电率和肌力输出的生理范围内的运动单位招募和速率编码。有限元框架内预测NMS模型的综合能力有助于提高我们对神经和肌肉骨骼系统如何协同工作的理解。虽然这项研究集中在一个简单的FE应用上，但这里提供的框架很容易适应神经肌肉模型、FE模拟或两者都增加的复杂性。
Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output. The combined capability of a predictive NMS model within a FE framework can aid in improving our understanding of how the nervous and musculoskeletal systems work together. While this study focused on a simple FE application, the framework presented here easily accommodates increased complexity in the neuromuscular model, the FE simulation, or both.
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影响因子：
1.9
作者：
Herrera, Antonio;Ibarz, Elena;Gracia, Luis
通讯作者：
Gracia, Luis
影响因子：
2.5
作者：
HENNEMAN, E;SOMJEN, G;CARPENTER, DO
通讯作者：
CARPENTER, DO
影响因子：
6.3
作者：
Del Vecchio, A.;Negro, F.;Farina, D.
通讯作者：
Farina, D.
DOI：
10.1002/cnm.3396
发表时间：
2020-11
影响因子：
2.1
作者：
Hume DR;Rullkoetter PJ;Shelburne KB
通讯作者：
Shelburne KB
影响因子：
3.7
作者：
Callahan DM;Umberger BR;Kent-Braun JA
通讯作者：
Kent-Braun JA