A Data Science Approach To Personalizing Sensorimotor Training Post-Stroke

项目摘要 卒中后损害、损害和对治疗的反应性的巨大变异性阻碍了 开发原则性和有效的上肢神经康复方法。这里, 以我们在大规模神经康复研究、脑成像和数据方面的多学科专业知识为基础 科学，以及我们已建立的合作，我们提出了一种新的自我完善的个性化康复 基于数据、算法和处理之间的连续循环的系统。在目标1中，我们将创建和 仿真了一种个性化UE康复的自改进算法，基于最新技术和 理论上合理的“贝叶斯上下文强盗”算法。预测动力学模型将产生时间- 对中风后3个月UE功能结果(ARAT)的不同预测对训练和 根据影响治疗效果的患者基线临床和神经变量，以及先前的 来自相似患者的知识。其次，顺序决策BANDIT算法将使用模型的预测 对于不同的(模拟)剂量，选择预计在3个月时使ARAT最大的周剂量 中风后，给出可行剂量的限制。在目标2中，我们将进行一项400人的队列研究 亚急性中风患者。这项研究产生的详细治疗、神经和临床数据 将使我们能够确定感觉运动训练的临床和神经调节器以及可行的剂量 亚急性中风。特别是，治疗数据将包括通过 机械化辅助治疗中的外骨骼与术中和术中手的活动量 用手腕佩戴的传感器记录常规治疗。在目标3中，我们将执行概念验证研究 自我完善的精密神经康复系统，150名参与者处于亚急性期 中风后。AIM 1的算法将根据先前的测量结果确定最佳的周剂量 实际剂量、测量的ARAT以及参与者的临床和神经环境。这一目标将于#年开始。 在目标2的200名参与者的数据被收集以更新目标1的算法之后的第三年。 该目标生成的数据将持续用于更新目标1的模型，从而产生更好的预测 因此，更有效的每周剂量计划。因此，我们将检验这一假设，即 从基线到3个月的ARAT对于每个新的50名参与者组来说将显著高于 前一组。这项研究完成后，我们将全面了解 考虑到卒中后的异质性特征，最能改善UE功能的康复治疗参数。 由此产生的550名参与者的独特数据库将公开提供，预计将成为 神经康复和中风研究的参考资料。拟议中的工作将为自我 改进用于常规护理的算法，可以通过StrokeNet基础设施进行扩展 用未来的资金。

Project Summary The large variability in lesions, impairment, and responsiveness to therapy following stroke has hindered the development of principled and effective approaches to neurorehabilitation of the upper extremity (UE). Here, building on our multidisciplinary expertise in large-scale neurorehabilitation studies, brain imaging, and data science, as well as our established collaborations, we propose a novel self-improving personalized rehabilitation system based on a continuous loop between data, algorithm, and treatment. In Aim 1, we will create and simulate a self-improving algorithm for personalized UE rehabilitation, based on the state-of-the-art and theoretically-sound “Bayesian contextual bandits” algorithm. A predictive dynamics model will generate time- varying predictions of the UE functional outcome (the ARAT) at 3 months post-stroke in response to training and based on the patient baseline clinical and neural variables that modulate the effects of treatment, and on prior knowledge from similar patients. Second, a sequential-decision bandit algorithm will use the model’s predictions for different (simulated) doses to select the weekly dose that is predicted to maximize the ARAT at 3 months post-stroke, given constraints on the feasible doses. In Aim 2, we will conduct a cohort study of 400 individuals with sub-acute stroke. The detailed treatment, neural, and clinical data generated by this study will allow us to identify the clinical and neural modulators of sensorimotor training as well as the feasible doses in sub-acute stroke. In particular, treatment data will include the schedule of arm movements recorded via an exoskeleton during mechanized adjuvant therapy and the number of hand movements both during and outside conventional therapy recorded with a wrist-worn sensor. In Aim 3, we will perform a proof-of-concept study of the self-improving precision neurorehabilitation system, with 150 participants in the sub-acute phase post-stroke. The algorithm of Aim 1 will determine the optimal weekly doses based on previous measurements of the actual doses, the measured ARAT, and the participant clinical and neural context. This Aim will start in year 3 after the data from 200 participants in Aim 2 have been collected to update the algorithm of Aim 1. The data generated by this Aim will continuously be used to update the model of Aim 1, yielding better predictions and thus more effective schedules of weekly doses. We will therefore test the hypothesis that the change in ARAT from baseline to 3-month will be significantly greater for each new group of 50 participants compared to the previous group. Upon completion of this research, we will have captured a comprehensive picture of rehabilitation treatment parameters that most improve UE function given the heterogeneous profiles after stroke. The resulting unique database of 550 participants, which will be made publicly available, is expected to become a reference in neurorehabilitation and stroke research. The proposed work will lay groundwork for a self- improving algorithm to be used in routine care, which could be scaled up through the StrokeNet infrastructure with future funding.