FMRI Clustering in AFNI: False-Positive Rates Redux
FMRI Clustering in AFNI: False-Positive Rates Redux
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DOI：
10.1089/brain.2016.0475
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发表时间：
2017-04-01
影响因子：
3.4
通讯作者：
Taylor, Paul A.
中科院分区：
文献类型：
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作者：
Cox, Robert W.;Chen, Gang;Taylor, Paul A.
最近由Eklund及其同事在2016年发布的关于FMRI组分析工具虚高假阳性率（fpr）的报告已经成为神经影像学内外的一个大话题。他们的结论是，现有的用于确定统计显著簇的参数方法极大地夸大了fpr（“高达70%”，主要是由于噪声空间自相关函数是高斯形和平稳的错误假设），这可能会对“无数”先前的结果产生质疑；相比之下，非参数方法，如他们的方法，准确地反映名义5%的fpr。他们还表示，与其他软件相比，AFNI显示出“特别高”的fpr，这主要是由于3dClustSim中的一个bug。我们用他们自己的结果和数字，并通过重复他们的一些模拟来评论这些观点。简而言之，虽然参数方法在这些测试中显示出一些FPR膨胀（并且高斯形空间平滑的假设也似乎通常是不正确的），但它们强调报告数千个模拟案例中的单个最差结果，这大大夸大了问题的规模。重要的是，FPR统计依赖于“任务”范式和体向p值阈值；因此，我们展示了他们的研究结果如何为FMRI研究设计和分析提供有用的建议，而不是简单地对该领域早期结果的灾难性降级。关于AFNI（我们维护），3dClustSim的bug效应被大大夸大了——他们自己的结果表明，AFNI结果并不比其他结果“特别”差。我们描述了AFNI的进一步更新，以更恰当地表征空间平滑（大大降低了fpr，尽管有些仍然保持在5%左右）；此外，我们概述了两种新实现的基于排列/随机化的方法，这些方法可以产生更紧密的fpr聚类，体向p约为5%
Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 2016 have become a large topic within (and outside) neuroimaging. They concluded that existing parametric methods for determining statistically significant clusters had greatly inflated FPRs ("up to 70%," mainly due to the faulty assumption that the noise spatial autocorrelation function is Gaussian shaped and stationary), calling into question potentially "countless" previous results; in contrast, nonparametric methods, such as their approach, accurately reflected nominal 5% FPRs. They also stated that AFNI showed "particularly high" FPRs compared to other software, largely due to a bug in 3dClustSim. We comment on these points using their own results and figures and by repeating some of their simulations. Briefly, while parametric methods show some FPR inflation in those tests (and assumptions of Gaussian-shaped spatial smoothness also appear to be generally incorrect), their emphasis on reporting the single worst result from thousands of simulation cases greatly exaggerated the scale of the problem. Importantly, FPR statistics depends on "task" paradigm and voxelwise p value threshold; as such, we show how results of their study provide useful suggestions for FMRI study design and analysis, rather than simply a catastrophic downgrading of the field's earlier results. Regarding AFNI (which we maintain), 3dClustSim's bug effect was greatly overstated-their own results show that AFNI results were not "particularly" worse than others. We describe further updates in AFNI for characterizing spatial smoothness more appropriately (greatly reducing FPRs, although some remain >5%); in addition, we outline two newly implemented permutation/randomization-based approaches producing FPRs clustered much more tightly about 5% for voxelwise p