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 沟通困难（失语症）是左半球中风最常见和最令人衰弱的结果之一，尽管失语症症状变化很大且恢复难以预测，但许多研究表明病变大小和位置是失语症症状的主要驱动因素。然而，这项先前的研究仅考虑了由病变引起的直接解剖学损伤，这是一个关键的限制，因为中风病变也会对远离病变的大脑结构的功能造成间接影响。尽管最初认为远程功能障碍会在中风后迅速解决，但现在已知远程功能障碍会在整个功能障碍中持续存在，并且对失语症恢复的研究几乎完全集中在通过重组（从而行为）恢复的想法上。这些研究回避了通过部分解决由测试的损伤引起的远程功能障碍（RRD）来实现恢复的旧观点，但尚不清楚RRD如何有助于失语症的恢复。新的机器学习方法称为功能异常映射 (FAM) 使用静息 BOLD 功能 MRI 信号来绘制个体中风幸存者整个大脑的远程功能障碍，FAM 映射比当前的测量方法（例如任务相关的功能磁共振活动和静息状态功能连接）具有更好的重测可靠性。以及使其成为临床有用工具的其他一些功能，申请人已经证明了 FAM 远程测量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，申请人将优化 FAM 的功能。 FAM方法并测试关于在慢性失语症中测量的产生远程功能障碍的生物机制的竞争假设。在独立阶段，申请人提出了一项纵向研究，以了解 RRD 对失语症恢复的贡献。申请人提出了一项全面的培训计划，以扩展其在失语症方面的知识。以下领域：中风恢复和失语症以外的神经可塑性的生物学机制、机器学习、生物标志物开发和先进的神经影像分析。该奖项期间的研究和培训将使申请人能够开发一个长期、独立的研究项目，重点是理解。的神经相关性失语症和开发转化脑措施，为失语症神经康复的临床决策提供信息。