University of Tubingen: Eberhard Karls Universitat Tubingen, Tubingen, Germany.
Centre of Neurology, Hertie-Institute for Clinical Brain Research, University of Tubingen, Tubingen, Germany.
Brain Struct Funct. 2022 Dec;227(9):3129-3144. doi: 10.1007/s00429-022-02559-x. Epub 2022 Sep 1.
In vivo tracking of white matter fibres catalysed a modern perspective on the pivotal role of brain connectome disruption in neuropsychological deficits. However, the examination of white matter integrity in neurological patients by diffusion-weighted magnetic resonance imaging bears conceptual limitations and is not widely applicable, as it requires imaging-compatible patients and resources beyond the capabilities of many researchers. The indirect estimation of structural disconnection offers an elegant and economical alternative. For this approach, a patient's structural lesion information and normative connectome data are combined to estimate different measures of lesion-induced structural disconnection. Using one of several toolboxes, this method is relatively easy to implement and is even available to scientists without expertise in fibre tracking analyses. Nevertheless, the anatomo-behavioural statistical mapping of structural brain disconnection requires analysis steps that are not covered by these toolboxes. In this paper, we first review the current state of indirect lesion disconnection estimation, the different existing measures, and the available software. Second, we aim to fill the remaining methodological gap in statistical disconnection-symptom mapping by providing an overview and guide to disconnection data and the statistical mapping of their relationship to behavioural measurements using either univariate or multivariate statistical modelling. To assist in the practical implementation of statistical analyses, we have included software tutorials and analysis scripts.
在体追踪白质纤维促使人们从现代角度认识到脑连接组破坏在神经心理缺陷中的关键作用。然而,通过弥散加权磁共振成像检查神经科患者的白质完整性存在概念上的局限性,并且并不广泛适用,因为它需要成像兼容的患者和许多研究人员无法获得的资源。间接估计结构连接缺失提供了一种优雅且经济的替代方法。对于这种方法,将患者的结构损伤信息和规范的连接组数据结合起来,以估计不同的损伤诱导的结构连接缺失度量。使用几个工具包中的一个,这种方法相对容易实现,甚至不需要有纤维追踪分析专业知识的科学家也可以使用。然而,结构脑连接缺失的解剖 - 行为统计映射需要分析步骤,这些步骤不受这些工具包的覆盖。在本文中,我们首先回顾间接损伤连接缺失估计的当前状态、现有的不同测量方法以及可用的软件。其次,我们旨在通过提供使用单变量或多变量统计建模将连接缺失数据及其与行为测量关系的统计映射的概述和指南来填补统计连接缺失 - 症状映射中的剩余方法学差距。为了协助统计分析的实际实施,我们包括了软件教程和分析脚本。