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.

沟通困难（失语）是左半球中风最常见和最虚弱的结果之一。虽然失语症的症状是高度可变的，恢复是难以预测的，许多研究表明，病变的大小和位置是失语症症状和恢复的主要驱动因素。然而，以往的研究只考虑了病变引起的直接解剖损伤。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产生间接影响。在整个应用程序中，我将其称为“远程功能障碍”。虽然人们最初认为远端功能障碍会在中风后迅速消退，但现在人们知道远端功能障碍会在整个康复过程中持续存在，并独立地影响预后。失语恢复的研究几乎完全集中在通过重组来恢复的想法上，即行为改善是通过大脑网络的可塑重组来实现的。这些研究回避了旧的观点，即恢复发生通过部分解决远程功能障碍（RRD）引起的病变。因此，RRD如何促进失语症的恢复尚不清楚。申请人开发了一种新的机器学习方法，称为功能异常映射（FAM），该方法使用静息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图谱比当前的测量方法（如任务相关的fMRI活动和静息状态功能连接）具有更好的测试-重测试可靠性，以及使其成为临床有用工具的其他几个特征。申请人已经证明用FAM测量的远程功能障碍与慢性失语症患者的行为结果有关。在本奖项的指导阶段，申请人将优化FAM方法，并测试有关慢性失语症中产生远程功能障碍的生物学机制的竞争性假设。在独立阶段，申请人提出纵向研究，以了解RRD对失语恢复的贡献。申请人提出了一个全面的培训计划，以扩大他在以下领域的知识：中风恢复的生物学机制和失语症以外的神经可塑性，机器学习，生物标志物开发和高级神经影像学分析。该奖项期间的研究和培训将使申请人能够开发一个长期的，独立的研究项目，专注于了解失语症的神经相关性，并开发翻译脑措施，为失语症神经康复的临床决策提供信息。