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神经驱动的有限元框架内 3D 肌肉激活:探索在健康和神经退行性模拟中的应用。

Neural-driven activation of 3D muscle within a finite element framework: exploring applications in healthy and neurodegenerative simulations.

机构信息

Mechanical and Biomedical Engineering, Boise State University, Boise, ID, USA.

Department of Mechanical Engineering, NY Institute of Technology, New York, NY, USA.

出版信息

Comput Methods Biomech Biomed Engin. 2024 Dec;27(16):2389-2399. doi: 10.1080/10255842.2023.2280772. Epub 2023 Nov 15.

Abstract

This paper presents a novel computational framework for neural-driven finite element muscle models, with an application to amyotrophic lateral sclerosis (ALS). The multiscale neuromusculoskeletal (NMS) model incorporates physiologically accurate motor neurons, 3D muscle geometry, and muscle fiber recruitment. It successfully predicts healthy muscle force and tendon elongation and demonstrates a progressive decline in muscle force due to ALS, dropping from 203 N (healthy) to 155 N (120 days after ALS onset). This approach represents a preliminary step towards developing integrated neural and musculoskeletal simulations to enhance our understanding of neurodegenerative and neurodevelopmental conditions through predictive NMS models.

摘要

本文提出了一种新的神经驱动的有限元肌肉模型计算框架,并将其应用于肌萎缩性侧索硬化症(ALS)。多尺度神经肌肉骨骼(NMS)模型包含生理上准确的运动神经元、3D 肌肉几何形状和肌纤维募集。它成功地预测了健康肌肉的力量和肌腱伸长,并表明由于 ALS 导致肌肉力量逐渐下降,从 203 N(健康)下降到 155 N(ALS 发病后 120 天)。这种方法代表了朝着开发集成神经和肌肉骨骼模拟的初步步骤迈进,通过预测性 NMS 模型来增强我们对神经退行性和神经发育性疾病的理解。

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