Accurate autocorrelation modeling substantially improves fMRI reliability
Accurate autocorrelation modeling substantially improves fMRI reliability
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DOI：
10.1038/s41467-019-09230-w
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发表时间：
2019-03-21
影响因子：
16.6
通讯作者：
Williams, Guy B.
中科院分区：
文献类型：
--
作者：
Olszowy, Wiktor;Aston, John;Williams, Guy B.
鉴于最近在一些神经成像统计方法中存在争议，我们比较了最常用的功能磁共振成像（fMRI）分析包：AFNI， FSL和SPM，关于时间自相关建模。这个过程，有时被称为预白化，在几乎所有的功能磁共振成像研究中都进行过。在这里，我们使用了11个数据集，包含980个扫描，对应于不同的fMRI协议和受试者群体。我们发现AFNI中的自相关建模虽然不完善，但比FSL和SPM的自相关建模要好得多。FSL和SPM中残余自相关噪声的存在导致一级结果严重混淆，特别是对于低频实验设计。SPM的替代预白化方法FAST比SPM的默认方法表现得更好。任务功能磁共振成像研究的可靠性可以通过更精确的自相关建模来提高。我们建议fMRI分析包提供诊断图，让用户意识到任何预白化问题。
Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. Here, we employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. We found that autocorrelation modeling in AFNI, although imperfect, performed much better than the autocorrelation modeling of FSL and SPM. The presence of residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. SPM's alternative pre-whitening method, FAST, performed better than SPM's default. The reliability of task fMRI studies could be improved with more accurate autocorrelation modeling. We recommend that fMRI analysis packages provide diagnostic plots to make users aware of any pre-whitening problems.