Optimizing sensori-motor training post-stroke

优化中风后感觉运动训练

基本信息

批准号：
9512137
负责人：
Nicolas Schweighofer
金额：
$ 48.31万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-28 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9512137
关键词：
21 year old Accounting Action Research Acute Adolescent Age Algorithms Brain Brain imaging Caring Characteristics Child Childhood stroke Classification Clinical Clinical Data Clinical Trials Cohort Studies Collaborations Computer Simulation Computer software Corticospinal Tracts Data Data Quality Data Set Databases Development Devices Disease Early treatment Enrollment Etiology Evidence based practice Feasibility Studies Force of Gravity Functional Magnetic Resonance Imaging Functional disorder Future Goals Hand Health Heterogeneity Human Resources Impairment Incidence Insurance International Learning Lesion Light Link Machine Learning Magnetic Resonance Imaging Measures Methods Modeling Motor Movement Multi-Institutional Clinical Trial National Health Insurance Neurorehabilitation Outcome Patients Pattern Performance Recovery Rehabilitation therapy Research Infrastructure Rest Sampling Scanning Schedule Space Models Stroke Structure Target Populations Testing Time Training Treatment Protocols Treatment outcome Upper Extremity acute stroke arm base clinical care cost cost effective design disability dosage dynamic system effective therapy exoskeleton inclusion criteria individual patient individualized medicine kinematics motor learning novel patient population post stroke programs prospective relating to nervous system treatment group

项目摘要

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.

项目概要中风后病变、损伤和对训练的反应的巨大变异性阻碍了有原则且具有成本效益的神经康复方法的发展上肢。我们的长期目标是开发预测性个性化神经康复基于大数据集的治疗。该提案基于一个独特的设计和以相对较低的成本进行大型神经康复队列研究。建立在我们的建立了美法合作关系，在神经康复方面拥有跨学科专业知识，脑成像、动力系统和统计学习，我们将预测恢复和通过以下新颖的三管齐下的方法进行个体化治疗。在目标 1 中，我们将开发包含 500 名患者的临床和神经患者特征、治疗和结果的数据库接受 ARMEO Spring 上肢康复治疗的中风后患者常规临床护理中的装置（重力补偿外骨骼）。纳入标准如下根据国家评估，尽可能广泛地包括患有多种脑损伤的患者最先进的磁共振成像 (MRI) 和功能性 MRI 扫描。在目标 2 中，使用数据库中，我们将预测上肢结果的长期变化作为函数使用将运动学习与运动学习联系起来的动态模型来了解患者的特征和治疗恢复。最终模型将扩展并结合之前的电机计算模型在小时间尺度上学习，在长时间尺度上恢复模型，并且将包括混合准确预测个体患者的长期康复效果。在目标 3 中，基于这些预测，我们将进行一项旨在个性化上肢的可行性研究康复以最大限度地恢复。给定一个新患者，其特征是有一些基线预测康复的特征，我们将选择最有效的治疗方案对数据库中的类似患者有效。恢复模型和调度方法该提案中开发的将为未来建议时间的临床软件提供基础，来自早期临床数据、运动学表现和常规的治疗剂量和内容扫描。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。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision.

随机最优前馈反馈控制决定了有或没有视觉的手臂运动的时间和变化。

DOI：
发表时间：
2021
期刊：
影响因子：
4.3
作者：
Berret, Bastien;Conessa, Adrien;Schweighofer, Nicolas;Burdet, Etienne
通讯作者：
Burdet, Etienne

Minimizing Precision-Weighted Sensory Prediction Errors via Memory Formation and Switching in Motor Adaptation.

通过运动适应中的记忆形成和切换来最小化精确加权的感官预测误差。

DOI：
发表时间：
2019
期刊：
影响因子：
0
作者：
Oh, Youngmin;Schweighofer, Nicolas
通讯作者：
Schweighofer, Nicolas

Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons.

电耦合控制下橄榄神经元的维度和混沌放电。

DOI：
发表时间：
2020
期刊：
影响因子：
4.3
作者：
Hoang, Huu;Lang, Eric J;Hirata, Yoshito;Tokuda, Isao T;Aihara, Kazuyuki;Toyama, Keisuke;Kawato, Mitsuo;Schweighofer, Nicolas
通讯作者：
Schweighofer, Nicolas

Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke.

中风后外骨骼训练期间分离感觉运动恢复和补偿。

DOI：
发表时间：
2021
期刊：
影响因子：
0
作者：
Nibras, Nadir;Liu, Chang;Mottet, Denis;Wang, Chunji;Reinkensmeyer, David;Remy;Laffont, Isabelle;Schweighofer, Nicolas
通讯作者：
Schweighofer, Nicolas