A multivariate lesion symptom mapping toolbox and examination of lesion-volume biases and correction methods in lesion-symptom mapping.
A multivariate lesion symptom mapping toolbox and examination of lesion-volume biases and correction methods in lesion-symptom mapping.
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DOI：
10.1002/hbm.24289
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发表时间：
2018-11
影响因子：
4.8
通讯作者：
Turkeltaub PE
中科院分区：
文献类型：
--
作者：
DeMarco AT;Turkeltaub PE
病变-症状映射已成为神经科学研究的基石，通过检查脑病变的后遗症来寻求大脑认知功能的定位。最近出现了多变量损伤症状映射方法，如支持向量回归，该方法在确定损伤区域是否导致行为缺陷时同时考虑多个体素。这种多变量方法能够识别传统的质量-单变量方法无法识别的复杂依赖关系。在这里，我们为支持向量回归病变症状映射（SVR-LSM）提供了一个新的工具箱，它提供了一个图形界面，并增强了使用该方法进行分析的灵活性和严密性。具体来说，该工具箱通过排列测试提供了簇级家庭错误校正，能够将行为数据和病变数据的任意干扰模型结合起来，并提供了一系列病变体积校正方法，包括一种将病变体积从病变图中的每个体素中回归的新方法。我们在一组慢性左脑卒中幸存者中展示了这些新工具，并检查了不同病变体积控制方法之间的差异。当病变体积没有得到充分控制时，在SVR-LSM和传统的基于质量-单变量体素的病变症状映射中，都发现了对全脑病变缺陷关联的强烈偏见。使用三种不同的回归方法纠正了这种偏差；其中，从行为评分和病变图中回归病变体积在分析中提供了最大的敏感性。
Lesion-symptom mapping has become a cornerstone of neuroscience research seeking to localize cognitive function in the brain by examining the sequelae of brain lesions. Recently, multivariate lesion-symptom mapping methods have emerged, such as support vector regression, which simultaneously consider many voxels at once when determining whether damaged regions contribute to behavioral deficits. Such multivariate approaches are capable of identifying complex dependences that traditional mass-univariate approach cannot. Here we provide a new toolbox for support vector regression lesion-symptom mapping (SVR-LSM) that provides a graphical interface and enhances the flexibility and rigor of analyses that can be conducted using this method. Specifically, the toolbox provides cluster-level family-wise error correction via permutation testing, the capacity to incorporate arbitrary nuisance models for behavioral data and lesion data, and makes available a range of lesion volume correction methods including a new approach that regresses lesion volume out of each voxel in the lesion maps. We demonstrate these new tools in a cohort of chronic left-hemisphere stroke survivors, and examine the difference between results achieved with various lesion volume control methods. A strong bias was found toward brainwide lesion-deficit associations in both SVR-LSM and traditional mass-univariate voxel-based lesion symptom mapping when lesion volume was not adequately controlled. This bias was corrected using three different regression approaches; among these, regressing lesion volume out of both the behavioral score and the lesion maps provided the greatest sensitivity in analyses.
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