Brain Connectivity Supporting Language Recovery in Aphasia

摘要 失语症是中风后最常见的神经功能缺陷之一，通常是由于脑损伤引起的。 大脑皮层中与语言处理有关的区域。然而，许多人 失语症患者的语言障碍与灰质损伤程度不成比例， 相对较小的皮质下病变导致严重缺陷，或尽管相对较大，但缺陷不太严重 病变这种差异常常被归因于这样一种观念，即语言不仅依赖于语言的完整性， 灰质区域，但也对白色物质通路支持他们的能力，采取行动一致。 尽管如此，脑卒中后坏死或神经胶质病变以外的白色物质中断并不 总是在大脑行为关系模型中被测量或考虑。全面了解 失语症的神经生物学，脑损伤应量化为直接坏死/神经胶质增生的组合， 以及皮层分离这项研究提案的首要目的是全面绘制 中风幸存者的残留白色物质网络，以确定其在语言神经生物学中的作用 加工和失语症恢复。利用结构神经成像的最新进展，我们的团队开发了 连接体-病变症状映射（CLSM）用于测试与失语机制相关的特定问题， 失语症恢复。在该项目的第一个周期，这项研究产生了15个高影响力的同行评审 出版物。基于这一成功，本项目中提出的新研究将建立在这些基础上。 评价与失语症相关的三个独立的新概念主题的成就：我们将定义 多模态网络动态建模方法，以阐明结构和 卒中后功能性神经元网络完整性，包括直接和间接神经元通信，以及 与失语症的关系（目标1）。双流模型是一个很有前途的新的理论框架， 语言处理，然而，它仍然是一个过度简化，我们最近的数据表明，每个流是 由更细粒度的子网络组成。使用连接体方法，我们将定义子网络， 形成语言处理的背侧流和腹侧流（目标2）。我们将确定特定于流的白色 支持治疗后失语症恢复的物质微结构网络可塑性（目的3）。完成 为了实现这些目标，我们将利用来自该中心的大型基线行为和成像慢性失语症数据集 失语症恢复研究（C-STAR）（n-199）（目的1和2），以及正在进行的治疗数据 研究语言康复结果的预测因子（POLAR）（n=150）（目的3）。总的来说，该项目将建立 连接体和网络科学，以推进失语症的翻译和个性化研究。它将 推进神经成像方法的知识，提供有关语言处理的机械信息， 并确定治疗相关语言改善的标记。

Abstract Aphasia is one of the most common neurological deficits after a stroke, typically resulting from injury to cortical brain regions related to language processing in the dominant hemisphere. However, many individuals with aphasia can exhibit language impairments that are out of proportion to the degree of gray matter injury, with severe deficits from relatively smaller subcortical lesions, or less severe deficits in spite of relatively large lesions. This discrepancy is frequently attributed to the notion that language relies not only on the integrity of gray matter regions, but also on the white matter pathways supporting their ability to act in concert. Nonetheless, white matter disconnections beyond the necrotic or gliotic post-stroke brain lesions are not always measured or taken into account in models of brain-behavior relationships. To fully understand the neurobiology of aphasia, brain damage should be quantified as the combination of direct necrosis / gliosis as well as cortical disconnection. The overarching purpose of this research proposal is to comprehensively map residual white matter networks in stroke survivors to determine their role in the neurobiology of language processing and aphasia recovery. Using newer advancements in structural neuroimaging, our group developed Connectome-Lesion Symptom Mapping (CLSM) to test specific questions related to aphasia mechanisms and aphasia recovery. During the first cycle of this project, this research yielded 15 high-impact peer-reviewed publications. Based on this success, the novel research proposed in this project will build on these achievements to evaluate three independent new conceptual topics related to aphasia: we will define multimodal network dynamic modeling approaches to elucidate the relationship between structural and functional neuronal network integrity post-stroke, including direct and indirect neuronal communication, and their relationship with aphasia (Aim 1). The dual stream model is a promising new theoretical framework for language processing, however, it is still an oversimplification and our recent data suggests that each stream is composed of finer grained sub-networks. Using the connectome approach, we will define the sub-networks that form the dorsal and ventral streams of language processing (Aim 2). We will determine stream-specific white matter microstructural network plasticity supporting aphasia recovery after treatment (Aim 3). To accomplish these aims, we will leverage a large baseline behavioral and imaging chronic aphasia dataset from the Center for the Study of Aphasia Recovery (C-STAR) (n-199) (Aims 1 and 2), and data from the ongoing treatment study Predictor of Outcome of Language Rehabilitation (POLAR) (n=150) (Aim 3). Overall, this project will build on connectome and network science to advance translational and personalized research in aphasia. It will advance knowledge on neuroimaging methods, provide mechanistic information about language processing, and determine markers for therapy-related language improvement.