Keywords: neuroimaging1, cortical thickness2, tissue deformation3, statistical analysis4, sensitivity5, specificity6, population simulation7, repeated tests8.In recent years, the replicability of neuroimaging findings has become an important concern to the research community. Neuroimaging pipelines consist of myriad numerical procedures, which can have a cumulative effect on the accuracy of findings. To address this problem, we propose a method for simulating artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion detection, using different automated corticometry pipelines. We have applied this method to different versions of two widely used neuroimaging pipelines (CIVET and FreeSurfer), in terms of coefficients of variation; sensitivity and specificity of detecting lesions in 4 different regions of interest in the cortex, while introducing variations to the lesion size, the blurring kernel used prior to statistical analyses, and different thickness metrics (in CIVET). These variations are tested in a between-subject design (in two random groups, with and without lesions, using T1-weigted MRIs of 152 individuals from the International Consortium of Brain Mapping (ICBM) dataset, df=151) and in a within-subject pre-post-lesion design (using 21 T1-Weighted MRIs of a single adult individual, scanned in the Infant Brain Imaging Study (IBIS), df=19) The simulation method is sensitive to partial volume effect and the lesion size. Comparisons between pipelines, illustrates the ability of this method to uncover differences in sensitivity and specificity of lesion detection. In the absence of 'ground truth', such simulation tools help address reproducibility issues that may arise from spurious computational noise. We propose that this method be adopted in the workflow of software development and release.

中文翻译：

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用于测试皮质测量管道的灵敏度和准确性的仿真工具包

关键词：神经影像1，皮质厚度2，组织变形3，统计分析4，敏感性5，特异性6，群体模拟7，重复测试8。近年来，神经影像学发现的可重复性已成为研究界的一个重要问题。神经影像管道由无数的数值程序组成，这些程序可以对结果的准确性产生累积影响。为了解决这个问题，我们提出了一种模拟大脑中人工病变的方法，以使用不同的自动皮质测量管道来估计病变检测的敏感性和特异性。就变异系数而言，我们已将此方法应用于两个广泛使用的神经影像管道（CIVET 和 FreeSurfer）的不同版本；检测皮层中 4 个不同感兴趣区域的病变的敏感性和特异性，同时引入病变大小、统计分析之前使用的模糊内核和不同厚度指标（在 CIVET 中）的变化。这些变化在受试者之间的设计中进行测试（在两个随机组中，有和没有病变，使用来自国际脑图谱 (ICBM) 数据集的 152 个人的 T1 加权 MRI，df=151）和在一个内部-受试者病灶前后设计（使用单个成人个体的 21 个 T1 加权 MRI，在婴儿脑成像研究 (IBIS) 中扫描，df=19） 模拟方法对局部体积效应和病灶大小敏感。管道之间的比较说明了该方法揭示病变检测灵敏度和特异性差异的能力。在缺乏“基本事实”的情况下，此类模拟工具有助于解决可能由虚假计算噪声引起的再现性问题。我们建议在软件开发和发布的工作流程中采用这种方法。