Modeling the neural bases of aphasia in neurosurgical patients: A multivariate, connectivity-based approach

神经外科患者失语症的神经基础建模：基于连接的多变量方法

• 批准号: 10536156
• 负责人: Deborah Levy
• 金额: $ 6.68万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10536156
• 关键词: Acquired Language Disorders; Acute; Affect; American; Aphasia; Area; Behavior; Brain; Brain region; Comprehension; Data; Data Set; Electrocorticogram; Excision; Exhibits; Functional disorder; Goals; Individual; Intervention; Language; Lead; Lesion; Light; Literature; Measures; Mentors; Mission; Modeling; Monitor; Network-based; Neurodegenerative Disorders; Operative Surgical Procedures; Outcome; Pathway Analysis; Patient Care; Patients; Pattern; Play; Population; Primary Lesion; Process; Public Health; Research; Resected; Role; Site; Stereotyping; Stroke; Symptoms; Syndrome; Time; Tissues; United States National Institutes of Health; Work; base; burden of illness; cohort; disability; experience; healing; improved; innovation; insight; language outcome; language processing; large datasets; multimodality; network models; neural model; neuroimaging; neurophysiology; neurosurgery; relating to nervous system; tool; white matter; white matter damage

PROJECT SUMMARY/ABSTRACT Lesion symptom mapping (LSM) is a crucial tool used to make causal inferences about behavior from neuroimaging data. Recent work has suggested that structural white matter (WM) and functional connectivity between cortical regions play an important role in supporting healthy language function. However, any causal role of connectivity in language remains unclear, due to both intrinsic limitations of the cohorts typically studied with LSM and often discordant findings across patients and healthy controls. To shed light on this problem, the proposed project will use a multimodal approach to examine connectivity and language in a large and still- growing dataset of patients undergoing resective neurosurgery. This population (a) regularly experiences transient, site-specific aphasias in the acute period following surgery, (b) is not subject to the same confounds of populations typically studied in LSM, and (c) can be studied using electrocorticography (ECoG) prior to resection, allowing both healthy and aphasic language to be neurally characterized within the same individuals. The central hypothesis is that the neurosurgical cohort will reveal classical language syndromes to be a function of disconnection rather than modular damage, with marked deficits in language arising primarily from lesions to WM bottlenecks supporting functional connectivity within the broader language network. The rationale is that this unique approach will contribute a new and clarifying perspective on language and the brain, allowing us to directly examine the extent to which connectivity is necessary for versus simply involved in healthy language processing. The central hypothesis will be investigated via two specific aims: (1) to use multivariate LSM (MLSM) to determine the extent to which the structural integrity of white matter (WM) tracts predicts fluency and comprehension in the acute period following resective neurosurgery over and above what is predicted by the integrity of classical, cortical language regions alone, and (2) to use network analysis of ECoG to determine whether the resection of tissue that exhibits strong functional connectivity prior to surgery predicts poorer fluency and comprehension outcomes post-surgery. In the first aim, MLSM models based on cortical and WM ROIs will be statistically compared to determine which provide the most accurate predictions of language outcomes. In the second aim, functional connectivity of ECoG from later-resected tissue will be analyzed to determine whether pre-surgical measures of connectivity lead to better predictions of language outcomes. The research proposed here will provide the first multimodal study of language including both MLSM and ECoG, with a distinct focus on the causal role of connectivity in language. This work is innovative because it will make use of a rare cohort, sophisticated multivariate and network-based analyses, and an unusually large dataset to predict language outcomes. The research is significant because it will provide vital insights into the causal role of connectivity in language, with the potential to improve patient care through better prediction of language outcomes and more effectively targeted strategies for intervention.

项目概要/摘要 病变症状图 (LSM) 是一种重要工具，用于对行为进行因果推断 神经影像数据。最近的工作表明，结构白质（WM）和功能连接 皮质区域之间的相互作用在支持健康的语言功能方面发挥着重要作用。然而，任何因果关系 由于典型研究群体的内在局限性，连通性在语言中的作用仍不清楚 LSM 的结果常常与患者和健康对照者不一致。为了阐明这个问题， 拟议的项目将使用多模式方法来检查大范围内的连通性和语言 接受切除神经外科手术的患者数据集不断增长。该人群 (a) 经常经历 手术后急性期的短暂性、部位特异性失语，(b) 不会出现同样的混淆 通常在 LSM 中研究的人群，并且 (c) 可以在之前使用皮质电图 (ECoG) 进行研究 切除，允许在同一个体中对健康语言和失语症语言进行神经表征。 中心假设是神经外科队列将揭示古典语言综合症是一种 断开连接的功能而不是模块损坏，语言上的明显缺陷主要源于 支持更广泛的语言网络内的功能连接的 WM 瓶颈的损伤。这 理由是，这种独特的方法将为语言和语言提供新的、清晰的视角。 大脑，使我们能够直接检查连通性在多大程度上是必要的，而不是简单地参与 健康的语言处理。中心假设将通过两个具体目标进行研究：（1）使用 多变量 LSM (MLSM) 确定白质 (WM) 束结构完整性的程度 预测切除神经外科手术后急性期的流畅性和理解力 仅通过古典皮质语言区域的完整性进行预测，并且（2）使用网络分析 ECoG 以确定手术前切除的组织是否表现出强大的功能连接 预测术后流畅性和理解力结果较差。在第一个目标中，MLSM 模型基于 将统计比较皮质和 WM ROI，以确定哪个提供最准确的预测 的语言结果。在第二个目标中，来自后来切除的组织的 ECoG 的功能连接将被 进行分析以确定手术前的连接测量是否可以更好地预测语言 结果。这里提出的研究将提供第一个语言的多模态研究，包括 MLSM 和 ECoG，特别关注语言连通性的因果作用。这项工作具有创新性 因为它将利用罕见的队列、复杂的多变量和基于网络的分析，以及 用于预测语言结果的异常大的数据集。这项研究意义重大，因为它将提供重要的 深入了解语言连通性的因果作用，并有可能通过以下方式改善患者护理 更好地预测语言结果和更有效的有针对性的干预策略。