Developing Behavioral and Neuroimaging Predictors of Stroke Recovery

开发中风恢复的行为和神经影像预测因子

• 批准号: 10552568
• 负责人: MARK D'ESPOSITO
• 金额: --
• 依托单位: VA NORTHERN CALIFORNIA HEALTH CARE SYS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10552568
• 关键词: Acute; Address; Behavioral; Blood Vessels; Brain; Brain region; California; Caring; Chronic; Cognition; Cognitive; Cognitive deficits; Communities; Complement; Computing Methodologies; Data; Decision Making; Dependence; Development; Diffusion; Disparate; Emotional; Etiology; Event; Evolution; Functional Magnetic Resonance Imaging; Future; Image; Impairment; Informed Consent; Injury; Lesion; Magnetic Resonance Imaging; Measures; Methodology; Motor; Nervous System Physiology; Neurologist; Occupational; Outcome; Patients; Pharmaceutical Preparations; Phenotype; Recovery; Recovery of Function; Rehabilitation Centers; Rehabilitation therapy; Resources; Rest; Scanning; Science; Sentinel; Site; Speech; Statistical Methods; Stroke; System; Techniques; Technology; Testing; Time; Treatment Failure; Validation; Veterans; Work; acute care; analytical method; behavior measurement; biomarker identification; cognitive control; cognitive disability; cognitive function; cognitive recovery; cost; design; disability; economic cost; frontal lobe; graph theory; hospital care; improved; indexing; individual patient; innovation; network dysfunction; neuroimaging; neurological recovery; noninvasive brain stimulation; novel strategies; personalized approach; post stroke; predictive marker; programs; prospective; recruit; stem; stroke event; stroke outcome; stroke patient; stroke recovery; stroke rehabilitation; stroke therapy; tractography; traditional therapy

Given the high personal and economic costs of stroke, significant resources have been devoted to rehabilitation efforts within the VA and elsewhere. Despite this emphasis, all too often recovery after stroke remains partial, and many patients do not respond to traditional therapies. The difficulties in improving recovery after stroke stem in part from the fact that traditional approaches to predicting the effects of stroke-related brain lesions on subsequent function are often imprecise, particularly so for higher cognitive functions. Recent advances have demonstrated, for example, that similar-appearing lesions may give rise to disparate phenotypes based upon the extent to which they disrupt specific large-scale brain networks. Thus, in this proposal we will take advantage of advances in MRI methodology and analytics, within the context of validated behavioral metrics and new statistical techniques, to develop new predictors of functional recovery after stroke. Over the course of the study, patients referred from acute care hospitals to the CREC in Martinez, California for rehabilitation after stroke will be recruited to participate within two weeks of their index event. Those who provide informed consent will undergo a battery of tests to assess cognitive, emotional, motor, and other neurological function. In parallel, they will undergo structural MRI, resting state functional MRI (rs-fMRI) and diffusion tractography imaging (DTI) from which connectivity metrics derived through graph theory and Granger causality will be determined. Both behavioral and neuroimaging data will be obtained at three time points: within two weeks of the sentinel event, at three months, and at twelve months. Following the acquisition of these behavioral and imaging metrics, advanced statistical methods will be used to search for validated predictors of cognitive, emotional, and other neurological recovery at three and twelve months after stroke. As such, this proposal takes advantage of (1) validated behavioral and cognitive measures; (2) a new connectivity brain science that permits the quantification of the integrity of brain networks and has given rise to hypotheses about their evolution after injury; (3) advanced statistical techniques; and (4) longitudinal assessments in order to identify markers that will help to predict recovery after stroke. This work hopefully represents the first step in a long-term program designed to address the significant personal and economic costs of stroke in veterans and others. In addition to permitting prospective validation of any predictors of cognitive recovery, these results may also form the basis for the assessment of future approaches to stroke treatment, including individualized medication trials and targeted non-invasive brain stimulation to enhance rehabilitation efforts.

鉴于中风的高昂的个人和经济成本，大量的资源已经被 致力于退伍军人事务部和其他地方的康复工作。尽管强调了这一点，但往往 中风后的康复仍然是部分的，许多患者对传统疗法没有反应。这个 改善中风后康复的困难部分源于这样一个事实，即传统的治疗方法 预测与中风相关的脑损伤对后续功能的影响通常是不准确的， 尤其是对于更高级的认知功能。例如，最近的进展表明， 外观相似的皮损可能会导致不同的表型，这取决于它们的程度 破坏特定的大规模大脑网络。因此，在这项提案中，我们将利用先进技术 在MRI方法和分析中，在经过验证的行为指标和新的 统计技术，以开发中风后功能恢复的新预测指标。 在研究过程中，从急救医院转诊到CREC的患者 加利福尼亚州马丁内斯中风后康复将在两周内招募参加 他们的索引事件。那些提供知情同意的人将接受一系列测试，以评估 认知、情绪、运动和其他神经功能。同时，它们将经历结构性的 磁共振成像、静息功能磁共振成像(RS-fMRI)和扩散束成像(DTI) 将确定通过图论和格兰杰因果关系得出的连通性度量。两者都有 行为和神经成像数据将在三个时间点获得：在两周内 哨兵事件，三个月和十二个月。在获得这些行为 和成像指标，先进的统计方法将用于搜索经过验证的预测因素 中风后3个月和12个月认知、情绪和其他神经功能恢复。 因此，这一建议利用了(1)有效的行为和认知措施；(2) 一种新的连接脑科学，允许量化大脑网络和 (3)先进的统计技术；以及 (4)纵向评估，以确定有助于预测卒中后康复的标志物。 这项工作有望成为长期计划的第一步，旨在解决 退伍军人和其他人中风的巨大个人和经济代价。除了允许 对认知恢复的任何预测因素的前瞻性验证，这些结果也可能形成基础 用于评估未来的中风治疗方法，包括个体化药物试验 和有针对性的非侵入性脑刺激，以加强康复努力。