Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neuroscience, University of Padua, Padua, Italy; Padua Neuroscience Center, Padua, Italy.
Neuroimage Clin. 2021;30:102639. doi: 10.1016/j.nicl.2021.102639. Epub 2021 Mar 22.
Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain's structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.
病变研究是认知神经科学家和神经学家的重要工具。然而，虽然大脑病变研究传统上旨在将神经症状定位到特定的解剖部位，但越来越多的证据表明，中风等神经疾病最好被概念化为大脑网络障碍。因此，神经科学和神经学领域的研究人员越来越有兴趣量化局灶性脑损伤对形成大脑结构连接组的白质连接的影响，但很少有专门的工具来促进这一努力。在这里，我们提出了病变量化工具包，这是一个公开的 MATLAB 软件包，用于量化局灶性脑损伤的结构影响。病变量化工具包使用基于图谱的方法来估计包裹水平的灰质病变负荷和多种白质断开严重程度的度量，包括束水平断开度量、体素水平断开图和包裹水平断开矩阵。该工具包还估计病变引起的包裹对之间最短结构路径长度的增加，这些路径提供了有关高阶结构网络拓扑变化的信息。我们详细描述了工具包生成的每个不同度量，讨论了它们的应用和使用相关的注意事项，并使用来自亚急性中风患者的真实行为数据进行了示例分析。我们表明，使用工具包生成的不同度量进行的分析产生的结果与之前文献中报告的结果高度一致，并且我们证明了使用工具包生成的不同断开度量进行的分析得到的结果的一致性。我们预计病变量化工具包将使研究人员能够解决仅使用传统病变分析难以或不可能解决的研究问题，并最终促进我们对白质断开如何导致局灶性脑损伤的认知、行为和生理后果的理解的进步。
