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How distributed processing produces false negatives in voxel-based lesion-deficit analyses

机译:分布式处理如何在基于体素的病变缺陷分析中产生假阴性

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摘要

In this study, we hypothesized that if the same deficit can be caused by damage to one or another part of a distributed neural system, then voxel-based analyses might miss critical lesion sites because preservation of each site will not be consistently associated with preserved function. The first part of our investigation used voxel-based multiple regression analyses of data from 359 right-handed stroke survivors to identify brain regions where lesion load is associated with picture naming abilities after factoring out variance related to object recognition, semantics and speech articulation so as to focus on deficits arising at the word retrieval level. A highly significant lesion-deficit relationship was identified in left temporal and frontal/premotor regions. Post-hoc analyses showed that damage to either of these sites caused the deficit of interest in less than half the affected patients (76/162 = 47%). After excluding all patients with damage to one or both of the identified regions, our second analysis revealed a new region, in the anterior part of the left putamen, which had not been previously detected because many patients had the deficit of interest after temporal or frontal damage that preserved the left putamen. The results illustrate how (i) false negative results arise when the same deficit can be caused by different lesion sites; (ii) some of the missed effects can be unveiled by adopting an iterative approach that systematically excludes patients with lesions to the areas identified in previous analyses, (iii) statistically significant voxel-based lesion-deficit mappings can be driven by a subset of patients; (iv) focal lesions to the identified regions are needed to determine whether the deficit of interest is the consequence of focal damage or much more extensive damage that includes the identified region; and, finally, (v) univariate voxel-based lesion-deficit mappings cannot, in isolation, be used to predict outcome in other patients.
机译:在这项研究中,我们假设,如果相同的缺陷可能是由于分布式神经系统的一个或另一部分的损坏引起的,则基于体素的分析可能会错过关键病变部位,因为每个部位的保存都不会与保存的功能保持一致。我们的研究的第一部分使用基于体素的多元回归分析对359名右撇子幸存者的数据进行了回归分析,以在剔除与对象识别,语义和语音清晰度相关的差异后,确定病变负荷与图片命名能力相关的大脑区域。专注于单词检索级别上出现的缺陷。在左颞部和额叶/运动前区中发现了高度重要的病变缺损关系。事后分析表明,对这些部位中的任何一个的损害导致不到一半的患病患者(76/162%= 47%)的兴趣不足。在排除所有对一个或两个已确定区域有损伤的患者之后,我们的第二次分析显示了在左侧核壳前部的一个新区域,该区域先前未被发现,因为许多患者在颞叶或额叶后均缺乏兴趣保留左壳核的损伤。结果说明(i)当不同的病变部位引起相同的缺陷时,假阴性结果会如何产生; (ii)可以通过采用一种迭代方法来揭示某些遗漏的影响,该方法系统地将有病变的患者排除在先前分析中确定的区域之外;(iii)具有统计学意义的基于体素的病变缺损图可以由部分患者驱动; (iv)需要对已识别区域进行局灶性病变以确定感兴趣的缺陷是局灶性损害的结果还是包括已识别区域的更广泛损害的结果;最后,(v)基于单变量体素的病变缺损图不能单独用于预测其他患者的预后。

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