使用大规模并行架构优化人体平衡模型,并应用于轻度创伤性脑损伤

G. Ciccarelli, Michael Nolan, H. Rao, Tanya Talkar, A. O'Brien, G. Vergara-Diaz, R. Zafonte, T. Quatieri, R. McKindles, P. Bonato, A. Lammert
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引用次数: 2

摘要

静态和动态平衡经常因脑损伤而中断。损伤可能是复杂的,对于轻度创伤性脑损伤(mTBI),可以通过标准临床试验检测不到。因此,需要神经学相关的建模方法来检测和推断损伤机制。目前的工作提出了具有高度对应的静态和动态平衡模型。强调领域之间的结构相似性有助于两者的发展。此外,还特别关注了神经生物学中感觉反馈和感觉整合的基础机制的组成部分。模型适用于10名健康对照志愿者和11名轻度创伤性脑损伤志愿者的实验数据。通过综合分析方法(该方法的实现由最先进的高性能计算系统实现),我们导出了一个可解释的、基于模型的特征集,该特征集可以对静态平衡任务中的mTBI和控制进行分类,ROC AUC为0.72。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human balance models optimized using a large-scale, parallel architecture with applications to mild traumatic brain injury
Static and dynamic balance are frequently disrupted through brain injuries. The impairment can be complex and for mild traumatic brain injury (mTBI) can be undetectable by standard clinical tests. Therefore, neurologically relevant modeling approaches are needed for detection and inference of mechanisms of injury. The current work presents models of static and dynamic balance that have a high degree of correspondence. Emphasizing structural similarity between the domains facilitates development of both. Furthermore, particular attention is paid to components of sensory feedback and sensory integration to ground mechanisms in neurobiology. Models are adapted to fit experimentally collected data from 10 healthy control volunteers and 11 mild traumatic brain injury volunteers. Through an analysis by synthesis approach whose implementation was made possible by a state-of-the-art high performance computing system, we derived an interpretable, model based feature set that could classify mTBI and controls in a static balance task with an ROC AUC of 0.72.
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