Developing Behavioral and Neuroimaging Predictors of Stroke Recovery

鉴于中风的高个人和经济成本， 致力于退伍军人事务部和其他地方的康复工作。尽管如此强调， 中风后的恢复仍然是部分的，许多患者对传统疗法没有反应。的 改善中风后恢复的困难部分源于这样一个事实，即传统的方法， 预测中风相关脑损伤对随后功能的影响通常是不精确的， 特别是对于高级认知功能。例如，最近的进展表明， 相似外观的病变可能会引起不同的表型，这取决于它们在多大程度上 破坏特定的大规模大脑网络。因此，在本提案中，我们将利用先进的 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，开发中风后功能恢复的新预测因素。 在研究过程中， 马丁内斯，加州中风后康复将招募参加两周内 指数事件。提供知情同意书的人将接受一系列测试， 认知、情感、运动和其他神经功能。与此同时，它们将经历结构性的 MRI、静息态功能性MRI（rs-fMRI）和弥散纤维束成像（DTI）， 将确定通过图论和格兰杰因果关系导出的连通性度量。两 将在三个时间点获得行为和神经成像数据： 三个月和十二个月时的哨兵事件。在获得这些行为之后， 和成像指标，先进的统计方法将用于搜索有效的预测因子， 中风后3个月和12个月的认知、情感和其他神经功能恢复。 因此，该提案利用了（1）经过验证的行为和认知措施;（2） 一种新的连接大脑科学，允许量化大脑网络的完整性， 已经引起了关于它们在受伤后演变的假设;（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恢复的标志物。 这项工作有望代表长期计划的第一步，该计划旨在解决 退伍军人和其他人中风的重大个人和经济成本。除允许 前瞻性验证任何认知恢复的预测因素，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，以加强康复工作。

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.