The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs.
The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs.
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DOI：
10.1098/rsif.2020.1024
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Moulitsas I
中科院分区：
文献类型：
--
作者：
Alexiadis A;Simmons MJH;Stamatopoulos K;Batchelor HK;Moulitsas I
本文展示了如何耦合多物理场和人工神经网络来设计人体器官的计算机模型，这些器官能够自主地适应环境刺激。该模型模拟肠的运动性，并根据管腔内容物的物理特性调整其收缩模式。Multiphysics再现了肠膜的固体力学和管腔内容物的流体力学;人工神经网络复制了肠神经系统的活动。以前的研究建议用强化学习来训练网络。在这里，我们证明了仅仅强化学习是不够的;网络的输入输出结构还应该模仿肠神经系统的基本电路。模拟验证对人体肠道中的高振幅传播收缩的体内测量。当网络具有与神经系统相同的输入输出结构时，即使面对训练范围之外的条件，模型也表现良好。该模型经过训练以优化运输，但它也使膜中的应力保持在较低水平，这正是真实的肠道中发生的情况。此外，该模型响应于其功能的非典型变化，其“症状”反映了疾病中出现的症状。如果健康的肠道模型通过添加数字炎症而人为地生病，那么运动模式就会以与炎症病理学（如炎症性肠病）一致的方式被破坏。
This article shows how to couple multiphysics and artificial neural networks to design computer models of human organs that autonomously adapt their behaviour to environmental stimuli. The model simulates motility in the intestine and adjusts its contraction patterns to the physical properties of the luminal content. Multiphysics reproduces the solid mechanics of the intestinal membrane and the fluid mechanics of the luminal content; the artificial neural network replicates the activity of the enteric nervous system. Previous studies recommended training the network with reinforcement learning. Here, we show that reinforcement learning alone is not enough; the input–output structure of the network should also mimic the basic circuit of the enteric nervous system. Simulations are validated against in vivo measurements of high-amplitude propagating contractions in the human intestine. When the network has the same input–output structure of the nervous system, the model performs well even when faced with conditions outside its training range. The model is trained to optimize transport, but it also keeps stress in the membrane low, which is exactly what occurs in the real intestine. Moreover, the model responds to atypical variations of its functioning with ‘symptoms’ that reflect those arising in diseases. If the healthy intestine model is made artificially ill by adding digital inflammation, motility patterns are disrupted in a way consistent with inflammatory pathologies such as inflammatory bowel disease.
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影响因子：
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DOI：
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发表时间：
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期刊：
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影响因子：
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