Big Data Neuroimaging to Predict Motor Behavior After Stroke

大数据神经影像预测中风后的运动行为

• 批准号: 9888377
• 负责人: Sook-Lei Liew
• 金额: $ 13.32万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9888377
• 关键词: Activities of Daily Living; Acute; Address; Adult; Affect; Age; Area; Behavior; Behavioral; Big Data; Big Data Methods; Biological Markers; Brain; Brain imaging; Caring; Characteristics; Classification; Clinical; Clinical Research; Collaborations; Corticospinal Tracts; Data; Data Set; Diffuse; Disease; Effectiveness of Interventions; Ensure; Future; Gait; Gender; Genetic study; Genomics; Goals; Grant; Heterogeneity; Image; Impairment; Individual; Infrastructure; Injury; International; K-Series Research Career Programs; Knowledge; Language; Lead; Left; Lesion; Location; Magnetic Resonance Imaging; Measures; Medicine; Mentors; Mentorship; Methodology; Methods; Modeling; Motor; Motor Cortex; Nature; Outcome; Participant; Patients; Performance; Persons; Physical Medicine; Population; Population Heterogeneity; Precision Medicine Initiative; Property; Recovery; Recovery of Function; Rehabilitation therapy; Reproducibility; Reproducibility of Results; Research; Research Personnel; Resources; Sensitivity and Specificity; Sensory; Site; Statistical Models; Stroke; Structure; Task Performances; Techniques; Testing; Thick; Time; Training; United States National Institutes of Health; Wolves; arm; arm movement; computerized tools; disability; functional disability; functional improvement; heterogenous data; imaging biomarker; improved; large datasets; machine learning algorithm; motor behavior; motor impairment; motor recovery; muscle strength; neurobiological mechanism; neuroimaging; neurological rehabilitation; novel; personalized medicine; post stroke; predictive marker; predictive modeling; predictive test; prevent; rehabilitation research; relating to nervous system; research and development; response; skills; statistical and machine learning; stroke outcome; stroke patient; stroke recovery; stroke rehabilitation; stroke survivor; stroke therapy; supervised learning; tool; treatment response; unsupervised learning; working group

PROJECT SUMMARY Stroke is a leading cause of serious long-term adult disability around the world. Despite intensive therapy, an estimated 2/3 of stroke survivors do not fully recover and are left unable to care for themselves independently. Growing research suggests that rehabilitation is not “one-size-fits-all”; variability among stroke survivors in terms of lesion location, age, gender, time since stroke and more may all affect a person's likelihood of recovery and response to different types of treatments. Personalized rehabilitation medicine to maximize each individual's recovery potential is thus desperately needed. However, in order to develop accurate, robust, and specific predictive models that can determine an individual's recovery potential and response to different treatments, large, heterogeneous datasets are needed. The current best predictors of stroke outcomes are neuroimaging (MRI) and behavioral biomarkers that look at brain structure/function and motor performance at baseline. Generating a large enough dataset of MRI and behavioral data is extremely difficult and expensive for any one site to do on its own. This proposal addresses this problem by generating a large, diverse dataset using a novel meta-analytic approach that harmonizes post-stroke data collected worldwide. In partnership with an international consortium comprised of over 500 researchers who produce the largest-known neuroimaging and genetic studies of over 18 different diseases (ENIGMA Center for Worldwide Medicine, Imaging, and Genomics), I propose to apply ENIGMA's powerful approach to answer critical questions in stroke recovery. Under this K01 career development award, I will develop skills in big data neuroimaging analytics, clinical research, and consortium building through my ENIGMA Stroke Recovery working group in order to ask questions about stroke recovery using a large dataset approach (goal n>3,000). This project has four specific aims: Aim 1 will leverage ENIGMA's existing methodology to develop the infrastructure, optimal methods, and analysis techniques for harmonizing a large dataset of post-stroke MRI and behavioral data. Aim 2 will use this large dataset to identify neural and behavioral biomarkers predicting recovery of motor impairment (e.g., actual arm movement ability) and recovery of function (e.g., ability to perform tasks, such as picking up objects with the affected arm). Aim 3 will use supervised machine learning to generate and fine-tune highly accurate predictive models of the relationship between these biomarkers and recovery of impairment versus function. Lastly, Aim 4 will use unsupervised machine learning techniques to examine shared properties of outliers from the predictive model and determine additional neurobiological mechanisms that may prevent individuals from recovering. This approach has the potential to revolutionize the way that rehabilitation research is validated, to ensure robust, reliable, and reproducible results. The methods developed here could be extended to other domains of recovery (language, gait), to study other predictors of recovery (functional brain activity, genomics), and to other clinical populations to improve rehabilitation overall.

项目概要 尽管进行了强化治疗，中风仍然是全世界成人严重长期残疾的主要原因。 估计有 2/3 的中风幸存者没有完全康复并且无法照顾自己 越来越多的研究表明，康复治疗并不是“一刀切”的。 幸存者的病变部位、年龄、性别、中风后的时间等都可能影响一个人的 康复的可能性以及对不同类型治疗的反应。 然而，为了发展，迫切需要最大限度地发挥每个人的恢复潜力。 准确、稳健和具体的预测模型，可以确定个人的恢复潜力和 为了响应不同的治疗，需要大型、异构的数据集。 中风结果是神经影像学 (MRI) 和行为生物标志物，用于观察大脑结构/功能和 生成足够大的 MRI 和行为数据数据集非常困难。 对于任何一个站点来说，单独完成这项工作都是困难且昂贵的，该提案通过生成一个解决方案来解决这一问题。 使用新颖的元分析方法来协调收集的中风后数据的大型、多样化的数据集 与由 500 多名研究人员组成的国际联盟合作，生产该产品。 对超过 18 种不同疾病进行的最知名的神经影像学和遗传学研究（ENIGMA 全球中心 医学、影像学和基因组学），我建议应用 ENIGMA 强大的方法来回答关键问题 在这个 K01 职业发展奖下，我将培养大数据方面的技能。 通过我的 ENIGMA 中风恢复进行神经影像分析、临床研究和联盟建设 工作组使用大数据集方法询问有关中风恢复的问题（目标 n>3,000）。 该项目有四个具体目标： 目标 1 将利用 ENIGMA 的现有方法来开发 用于协调中风后 MRI 大型数据集的基础设施、最佳方法和分析技术 目标 2 将使用这个大型数据集来识别预测的神经和行为生物标志物。 运动障碍的恢复（例如，实际手臂运动能力）和功能的恢复（例如，能力） 目标 3 将使用监督机器学习 生成并微调这些生物标志物之间关系的高度准确的预测模型 最后，目标 4 将使用无监督机器学习技术来恢复损伤与功能。 检查预测模型中异常值的共同属性并确定额外的神经生物学 这种方法有可能彻底改变个人的康复机制。 验证康复研究的方式，以确保稳健、可靠和可重复的结果。 这里开发的可以扩展到其他恢复领域（语言、步态），以研究其他预测因素 康复（功能性大脑活动、基因组学），以及其他临床人群，以改善整体康复。

项目成果

The Effects of Sensory Manipulations on Motor Behavior: From Basic Science to Clinical Rehabilitation.

感觉操纵对运动行为的影响：从基础科学到临床康复。

• DOI: 10.1080/00222895.2016.1241740
• 发表时间: 2017
• 期刊: Journal of motor behavior
• 影响因子: 1.4
• 作者: Sugiyama,Taisei;Liew,Sook-Lei
• 通讯作者: Liew,Sook-Lei

Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke.

• DOI: 10.3390/s21051806
• 发表时间: 2021-03-05
• 期刊: Sensors (Basel, Switzerland)
• 作者: Marin-Pardo O;Phanord C;Donnelly MR;Laine CM;Liew SL
• 通讯作者: Liew SL

Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning.

• DOI: 10.3390/s21061952
• 发表时间: 2021-03-10
• 期刊: Sensors (Basel, Switzerland)
• 作者: Paing MP;Tungjitkusolmun S;Bui TH;Visitsattapongse S;Pintavirooj C
• 通讯作者: Pintavirooj C

Editorial: Collaborative Efforts for Understanding the Human Brain.

社论：理解人脑的协作努力。

• DOI: 10.3389/fninf.2019.00038
• 发表时间: 2019
• 期刊: Frontiers in neuroinformatics
• 影响因子: 3.5
• 作者: Liew,Sook-Lei;Schmaal,Lianne;Jahanshad,Neda
• 通讯作者: Jahanshad,Neda

Acceptability of a Telerehabilitation Biofeedback System Among Stroke Survivors: A Qualitative Analysis.

中风幸存者中远程康复生物反馈系统的可接受性：定性分析。

• DOI: 10.1177/15394492231153998
• 发表时间: 2023
• 期刊: OTJR : occupation, participation and health
• 作者: Donnelly,MirandaR;Phanord,CoralieS;Marin-Pardo,Octavio;Jeong,Jessica;Bladon,Barrisford;Wong,Kira;Abdullah,Aisha;Liew,Sook-Lei
• 通讯作者: Liew,Sook-Lei