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

项目摘要/摘要 损害症状映射(LSM)是用于对行为进行因果推断的重要工具 神经成像数据。最近的研究表明，结构白质(WM)和功能连接 在支持健康的语言功能方面，大脑皮层之间起着重要作用。然而，任何原因 由于通常研究的队列的内在局限性，连接在语言中的作用仍然不清楚 与LSM有关，患者和健康对照组的研究结果往往不一致。为了阐明这个问题， 拟议的项目将使用多模式方法来检查大范围的连接和语言- 不断增长的接受神经外科手术的患者的数据集。这一人群(A)经常经历 手术后急性期的一过性特定部位失语症，(B)不会受到同样的混淆 通常在LSM中研究的人群，和(C)可以在治疗前使用皮层脑电图仪(ECoG)进行研究 切除，使健康和失语症的语言在同一个体内具有神经性特征。 中心假设是神经外科队列将揭示经典语言综合征是一种 功能断开而不是模块损坏，明显的语言缺陷主要是由于 损害到WM瓶颈，支持更广泛的语言网络内的功能连接。这个 理由是，这种独特的方法将有助于对语言和 大脑，使我们能够直接检查连接所需的程度，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 多变量最小二乘法(MLSM)确定白质束结构完整性的程度 预测神经外科手术后急性期的流利性和理解力 预测是由经典的、皮质语言区域的完整性单独进行的，以及(2)使用网络分析 ECOG以确定手术前是否切除表现出较强功能连通性的组织 预测术后流利性和理解力较差的结果。在第一个目标中，基于模型的最大似然模型 皮质ROI和WM ROI将进行统计比较，以确定哪种预测最准确 关于语言结果的。在第二个目标中，将从后来切除的组织中获得ECoG的功能连接性 分析以确定手术前的连通性测量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包括这两种语言的多模式语言研究 MLSM和ECoG，重点放在语言连通性的因果作用上。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 预测语言结果的异常大的数据集。这项研究意义重大，因为它将提供至关重要的 洞察语言连通性的因果作用，有可能通过以下方式改善患者护理 更好地预测语言结果，更有效地制定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.