Optimizing sensori-motor training post-stroke

项目摘要 中风后的损伤、损伤和对训练的反应性有很大的变异性 妨碍制定原则性和成本效益高的神经康复方法 上肢。我们的长期目标是开发预测性的个性化神经康复 基于大数据集的治疗。这项提议基于一个独特的机会来设计和 以相对较低的成本进行一项大型神经康复队列研究。建立在我们的 建立了美法合作，在神经康复方面拥有跨学科的专业知识， 脑成像、动力系统和统计学习，我们将预测恢复和 通过以下三管齐下的新方法实现个体化治疗。在目标1中，我们将开发 500名临床和神经科患者的特征、治疗和结果的数据库 应用Armeo Spring进行上肢康复治疗的脑卒中患者 常规临床护理中的一种外骨骼重力补偿装置。纳入标准将为 尽可能广泛地包括有多种脑部损害的患者，根据国家- 最先进的磁共振成像(MRI)和功能MRI扫描。在Aim 2中，使用 数据库中，我们将预测上肢结果的长期变化 使用将运动学习与运动学习联系起来的动力学模型的患者的特征和治疗 恢复。最终的模型将扩展和结合以前的电机计算模型 小时间尺度的学习与长时间尺度的恢复模型，并将包括混合 准确预测个别患者长期康复的效果。在目标3中，基于这些 预测，我们将进行一项旨在个体化上肢的可行性研究 康复，以最大限度地恢复。给定一名新患者，其特征为多个基线 预测恢复的特征，我们将选择治疗方案中 对数据库中类似的患者有效。恢复模型和调度方法 在该提案中开发的将为未来的临床软件提供建议时机的基础， 根据早期临床数据、运动学表现和常规进行治疗的剂量和内容 扫描。这种方法将改变神经康复计划，因为临床医生， 患者和保险公司将能够确定有效的治疗方法，同时减少 成本。

Project Summary The large variability in lesions, impairment, and responsiveness to training following stroke has hindered the development of principled and cost-effective approaches to neuro-rehabilitation of the upper extremity. Our long-term goal is to develop predictive personalized neurorehabilitation therapy based on large data sets. This proposal is based on a unique opportunity to design and execute a large neuro-rehabilitation cohort study at a relatively low cost. Building on our established US-French collaborations, with interdisciplinary expertise in neurorehabilitation, brain imaging, dynamical systems, and statistical learning, we will predict recovery and individualize therapy with the following novel three-pronged approach. In Aim 1, we will develop a database of clinical and neural patient characteristics, treatments, and outcomes from 500 patients post-stroke receiving upper extremity rehabilitation therapy with the ARMEO Spring device (a gravity compensating exoskeleton) in routine clinical care. Inclusion criteria will be as broad as possible to include patients with a large variety of brain lesions, as assessed by state- of-the-art magnetic resonance imaging (MRI) and functional MRI scans. In aim 2, using the database, we will predict long-term changes in upper extremity outcomes as a function of patient's characteristics and treatment using dynamical models that link motor learning to recovery. The final models will expand and combine previous computational models of motor learning at small time scales with models of recovery at long time scales, and will include mixed effects to accurately predict long-term recovery for individual patients. In aim 3, based on these predictions, we will perform a feasibility study aimed at individualizing upper extremity rehabilitation to maximize recovery. Given a new patient, characterized by a number of baseline characteristics that predict recovery, we will select the schedule of treatment that was the most effective for similar patients in the database. The recovery models and scheduling methods developed in this proposal will provide the basis for future clinical software that suggests timing, dosage, and content of therapy from early clinical data, kinematic performance, and routine scans. Such an approach will transform neurorehabilitation programs because the clinician, patient, and insurance company will be able to determine effective treatments while reducing costs.