fMRI中全连接组关联研究的多尺度统计检验
Multiscale statistical testing for connectome-wide association studies in fMRI
摘要：
使用功能性磁共振成像（fMRI），大脑连接的改变与各种临床疾病有关。我们实证研究了大脑包裹（或规模）的数量如何影响连接体的质量单变量一般线性模型（GLM）的结果。在连接体分析中用作节点的脑包裹由组聚类分析功能定义。我们首先验证了一个经典的Benjamini-Hochberg程序与参数GLM检验在给定的规模适当控制错误发现率（FDR）。然后，我们观察到在现实的模拟，有没有实质性的通货膨胀的FDR跨尺度，只要FDR是独立控制在每个尺度，并存在真正的协会可以建立使用综合排列测试结合所有尺度。其次，我们在模拟和三个真实静息态fMRI数据集（精神分裂症，先天性失明，运动练习）上观察到，发现率随尺度的变化而明显变化，并且对于低尺度（低于25）相对较高。尽管发现率存在差异，但在三个真实数据集中，不同尺度下得出的统计图通常非常一致。一些种子仍然显示出在50左右观察到的更好的效果，说明了多尺度分析的潜在好处。根据实际数据，统计图与现有文献吻合良好。总的来说，我们的研究结果支持FDR的多尺度GLM连接体分析在统计学上是有效的，并且可以在各种实验条件下捕获具有生物学意义的效应。
英文摘要：
Alterations in brain connectivity have been associated with a variety of clinical disorders using functional magnetic resonance imaging (fMRI). We investigated empirically how the number of brain parcels (or scale) impacted the results of a mass univariate general linear model (GLM) on connectomes. The brain parcels used as nodes in the connectome analysis were functionnally defined by a group cluster analysis. We first validated that a classic Benjamini-Hochberg procedure with parametric GLM tests did control appropriately the false-discovery rate (FDR) at a given scale. We then observed on realistic simulations that there was no substantial inflation of the FDR across scales, as long as the FDR was controlled independently within each scale, and the presence of true associations could be established using an omnibus permutation test combining all scales. Second, we observed both on simulations and on three real resting-state fMRI datasets (schizophrenia, congenital blindness, motor practice) that the rate of discovery varied markedly as a function of scales, and was relatively higher for low scales, below 25. Despite the differences in discovery rate, the statistical maps derived at different scales were generally very consistent in the three real datasets. Some seeds still showed effects better observed around 50, illustrating the potential benefits of multiscale analysis. On real data, the statistical maps agreed well with the existing literature. Overall, our results support that the multiscale GLM connectome analysis with FDR is statistically valid and can capture biologically meaningful effects in a variety of experimental conditions.
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