Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach.
Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach.
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DOI：
10.1145/3389189.3389203
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发表时间：
2020-06-01
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影响因子：
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通讯作者：
Erdogmus, Deniz
中科院分区：
文献类型：
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作者：
Akbar, Navid;Yarossi, Mathew;Erdogmus, Deniz
深度神经网络 (DNN) 能够可靠地模拟相应大脑刺激的肌肉反应，有可能增加对众多基础科学和应用用例的协调运动控制的了解。此类案例包括了解中风神经损伤导致的异常运动模式，以及基于刺激的神经恢复干预措施，例如配对联想刺激。在这项工作中，探索了潜在的 DNN 模型，并推荐具有最小平方误差的模型来实现 M2M-Net 的最佳性能，M2M-Net 是一种将运动皮层的经颅磁刺激映射到相应肌肉反应的网络，使用：有限元模拟、经验神经响应曲线、卷积自动编码器、单独的深度网络映射器和多肌肉激活记录。我们讨论不同建模方法和架构背后的基本原理，并对比它们的结果。此外，为了获得复杂性和性能分析之间权衡的比较见解，我们探索了不同的技术，包括 M2M-Net 的两个经典信息标准的扩展。最后，我们发现，当在输入处使用神经反应曲线时，类似于将运动皮层刺激映射到与肌肉的直接和协同连接的组合的模型表现最好。
A deep neural network (DNN) that can reliably model muscle responses from corresponding brain stimulation has the potential to increase knowledge of coordinated motor control for numerous basic science and applied use cases. Such cases include the understanding of abnormal movement patterns due to neurological injury from stroke, and stimulation based interventions for neurological recovery such as paired associative stimulation. In this work, potential DNN models are explored and the one with the minimum squared errors is recommended for the optimal performance of the M2M-Net, a network that maps transcranial magnetic stimulation of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings of multi-muscle activation. We discuss the rationale behind the different modeling approaches and architectures, and contrast their results. Additionally, to obtain a comparative insight of the trade-o between complexity and performance analysis, we explore different techniques, including the extension of two classical information criteria for M2M-Net. Finally, we find that the model analogous to mapping the motor cortex stimulation to a combination of direct and synergistic connection to the muscles performs the best, when the neural response profile is used at the input.