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

项目总结/摘要 病变症状映射（LSM）是一种重要的工具，用于对患者的行为进行因果推断， 神经成像数据。最近的研究表明，结构白色物质（WM）和功能连接 大脑皮层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 连接在语言中的作用仍然不清楚，这是由于通常研究的队列的固有局限性 与LSM和患者和健康对照之间的结果往往不一致。为了阐明这个问题， 拟议的项目将使用多模式的方法来检查连接和语言在一个大的，仍然- 接受切除性神经外科手术的患者的数据集不断增长。A.这一群体经常经历 术后急性期的短暂性、部位特异性失语（B）不受相同混淆的影响 通常在LSM中研究的人群，和（c）可以在LSM之前使用皮层电图（ECoG）进行研究。 切除，允许健康和失语症的语言神经特征在同一个人。 中心假设是，神经外科队列将揭示经典语言综合征是一种 分离功能而不是模块化损伤，语言明显缺陷主要来自于 在更广泛的语言网络中支持功能连接的WM瓶颈的病变。的 其理由是，这种独特的方法将有助于一个新的和澄清的角度对语言和 大脑，使我们能够直接检查连接在多大程度上是必要的，而不仅仅是参与 健康的语言处理中心假设将通过两个特定的目的进行研究：（1）使用 多变量LSM（MLSM），以确定白色物质（WM）束的结构完整性 在切除神经外科手术后的急性期， 是预测的完整性经典，皮层语言区域单独，（2）使用网络分析， ECoG用于确定手术前是否切除了表现出强功能连接的组织 预测手术后的流畅性和理解结果较差。在第一个目标中，MLSM模型基于 将对皮质和WM ROI进行统计学比较，以确定哪种ROI提供最准确的预测 语言成果。在第二个目标中，来自随后切除的组织的ECoG的功能连接性将被确定。 分析以确定手术前的连接措施是否能更好地预测语言 结果。本文提出的研究将提供第一个多模态语言研究，包括 MLSM和ECoG，特别关注语言中连接的因果作用。这项工作是创新的 因为它将利用一个罕见的队列，复杂的多变量和基于网络的分析， 异常大的数据集来预测语言结果。这项研究意义重大，因为它将提供至关重要的 深入了解语言连接的因果作用，有可能通过以下方式改善患者护理 更好地预测语言结果，更有效地采取有针对性的干预策略。

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.