Functional anomaly mapping of aphasia recovery

失语症恢复的功能异常图谱

• 批准号: 10837812
• 负责人: Andrew T DeMarco
• 金额: $ 24.9万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10837812
• 关键词: Affect; Algorithms; Anatomy; Aphasia; Architecture; Area; Award; Behavioral; Biological; Blood Vessels; Brain; Characteristics; Chronic; Clinical; Communication; Data; Data Set; Deafferentation procedure; Diagnosis; Distant; Exhibits; Functional Magnetic Resonance Imaging; Functional disorder; Goals; Individual; Knowledge; Language; Lead; Left; Length; Lesion; Location; Longitudinal Studies; Machine Learning; Maps; Measures; Mentors; Modeling; National Institute on Deafness and Other Communication Disorders; Neuronal Plasticity; Outcome; Pattern; Performance; Persons; Phase; Physiology; Recovery; Reproducibility; Research; Residual state; Resolution; Rest; Sample Size; Scanning; Severities; Signal Transduction; Source; Specific qualifier value; Strategic Planning; Stroke; Structure; Symptoms; Testing; Time; Training; aphasia recovery; behavior measurement; behavioral outcome; biomarker development; brain behavior; chronic stroke; clinical decision-making; cohort; experience; imaging modality; improved; longitudinal, prospective study; machine learning algorithm; multimodality; neural correlate; neurobiological mechanism; neuroimaging; neurological rehabilitation; neuromechanism; post stroke; programs; public health relevance; spatiotemporal; stroke recovery; stroke survivor; stroke-induced aphasia; tool; white matter

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.

项目摘要难度（失语）是左眼中风的最常见和令人衰弱的结果之一。尽管失语症症状是高度可变的，并且难以预测，但许多研究表明，病变的大小和位置是失语症症状和康复的主要驱动因素。但是，这项先前的研究仅考虑了病变造成的直接解剖损害。这是一个关键限制，因为中风病变还会对远离病变的大脑结构的功能进行间接影响。通过此应用程序，我将其称为“远程功能障碍”。尽管最初被认为是在中风后快速解决的，但现在已知远程功能障碍在整个恢复过程中持续存在，并独立促进结果。失语症恢复的研究几乎完全集中在通过重组恢复的概念上，从而通过脑网络的塑性重组进行行为改善。这些研究避免了一个较旧的想法，即恢复是通过病变引起的远程功能障碍（RRD）的部分分辨率而发生的。因此，尚不清楚RRD如何促进失语症康复。该应用程序开发了一种称为功能异常映射（FAM）的新机器学习方法，该方法使用静止的大胆功能性MRI信号在单个中风存活中绘制整个大脑的远程功能障碍。 FAM地图的测试可靠性比当前的测量值（例如与任务相关的fMRI活动和静止状态功能连接性）以及其他几个功能，这些功能使其成为临床上有用的工具。该适用的已经证明，与FAM有关的远程功能障碍与慢性失语症患者的行为结果有关。在该奖项的指导阶段，适用的将优化FAM方法，并测试有关在慢性失语症中测得的远程功能障碍的生物学机制的竞争假设。在独立阶段，适用的提案进行了一项纵向研究，以了解RRD对失语症恢复的贡献。该申请提出了一项全面的培训计划，以扩大他在以下领域的知识：中风恢复和神经塑性的生物学机制，超出了失语症，机器学习，生物标志物发展和先进的神经影像学分析。该奖项期间的研究和培训将使该应用程序能够制定一项长期独立的研究计划，旨在理解失语症的神经自我反应并制定转化脑措施，以告知Aphisia Neurorehabicitation的临床决策。