Functional anomaly mapping of aphasia recovery

项目摘要 沟通障碍(失语症)是左半球最常见和最令人衰弱的结果之一 卒中。尽管失语症的症状变化很大，恢复情况很难预测，但许多研究已经 显示病变的大小和位置是失语症症状和康复的主要驱动因素。不过，这个 以前的研究只考虑了病变造成的直接解剖学损害。这是一个关键的 局限性，因为中风损伤也会对远离大脑结构的大脑结构的功能造成间接影响 损伤。在整个应用程序中，我将其称为“远程功能障碍”。尽管最初认为可以解决 中风后不久，人们知道远程功能障碍在整个康复过程中都会独立存在 为结果作出贡献。失语症康复的研究几乎完全集中在康复的概念上 通过重组，通过大脑的可塑性重组来改善行为 网络。这些研究回避了旧的观点，即恢复是通过部分解决 由病变引起的远端功能障碍(RRD)。因此，目前还不清楚RRD如何导致失语症 恢复。申请人开发了一种新的机器学习方法，称为函数异常映射 (FAM)使用静息的BOLD功能磁共振信号来绘制整个大脑的远程功能障碍图 个别中风幸存者。FAM图具有比当前测量更好的重测可靠性，如任务- 相关的fMRI活动和静息状态功能连接，以及使其 作为临床有用的工具，前景看好。申请者已经证明了远程功能障碍 FAM的测量与慢性失语症患者的行为结果有关。在接受指导的过程中 在本奖项阶段，申请者将优化FAM方法并测试关于 慢性失语症患者产生远端功能障碍的生物学机制。在.期间 独立阶段，申请人建议进行一项纵向研究，以了解RRD对 失语症康复。申请人提出了一项全面的培训计划，以扩大他在 以下领域：中风康复的生物学机制和失语症以外的神经可塑性，机器 学习、生物标记物开发和高级神经成像分析。在此期间的研究和培训 该奖项将使申请者能够制定一项长期的、独立的研究计划，重点是 了解失语症的神经相关因素并开发翻译脑措施以告知临床 失语症神经康复中的决策。

Project Summary Difficulty communicating (aphasia) is one of the most common and debilitating results of left-hemisphere stroke. Although aphasia symptoms are highly variable and recovery is difficult to predict, much research has shown that lesion size and location are major drivers of aphasia symptoms and recovery. However, this previous research has only considered direct anatomical damage caused by the lesion. This is a critical limitation because stroke lesions also cause indirect effects on the function of brain structures distant from the lesion. Throughout this application, I refer to this as “remote dysfunction.” Although initially thought to resolve quickly after the stroke, remote dysfunction is now known to persist throughout recovery and independently contribute to outcomes. Studies of aphasia recovery have focused almost exclusively on the idea of recovery through reorganization, whereby behavioral improvement occurs through plastic reorganization of brain networks. These studies have eschewed the older idea that recovery occurs through partial resolution of remote dysfunction (RRD) caused by lesions. Consequently, it is not clear how RRD contributes to aphasia recovery. The applicant has developed a new machine learning approach called functional anomaly mapping (FAM) that uses resting BOLD functional MRI signal to map remote dysfunction throughout the brain in individual stroke survivors. FAM maps have much better test-retest reliability than current measures, like task- related fMRI activity and resting state functional connectivity, as well as several other features that make it promising as a clinically useful tool. The applicant has already demonstrated that remote dysfunction measured with FAM relates to behavioral outcomes in people with chronic aphasia. During the mentored phase of this award, the applicant will optimize the FAM approach and test competing hypotheses about the biological mechanisms generating the remote dysfunction measured in chronic aphasia. During the independent phase, the applicant proposes a longitudinal study to understand the contribution of RRD to aphasia recovery. The applicant proposes a comprehensive training plan to expand his knowledge in the following areas: the biological mechanisms of stroke recovery and neuroplasticity beyond aphasia, machine learning, biomarker development, and advanced neuroimaging analysis. The research and training during this award will enable the applicant to develop a long-term, independent research program focused on understanding the neural correlates of aphasia and developing translational brain measures to inform clinical decision-making in aphasia neurorehabilitation.