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Human balance models optimized using a large-scale, parallel architecture with applications to mild traumatic brain injury

机译：人类平衡模型使用大规模，并行架构优化，具有适用于轻度创伤性脑损伤

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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.
机译：静态和动态平衡经常通过脑损伤中断。损伤可以是复杂的并且对于轻度创伤性脑损伤（MTBI）可以通过标准临床试验无法察觉。因此，需要神经学相关的建模方法来检测和引人损伤机制。目前的工作提出了具有高度对应程度的静态和动态平衡的模型。强调域之间的结构相似性促进了两者的发展。此外，特别注意的是感觉反馈的组件和神经生物学中的接地机制的传感集成。模型适用于从10个健康控制志愿者和11种温和创伤性脑损伤志愿者进行实验收集的数据。通过通过综合方法进行分析，其实现是通过最先进的高性能计算系统实现的，我们派生了一个可解释的模型的功能集，可以在具有ROC AUC中的静态平衡任务中对MTBI进行分类和控制0.72。

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