多站点休眠状态fMRI连接的统计功效和预测精度
Statistical power and prediction accuracy in multisite resting-state fMRI connectivity
摘要：
使用静息态功能磁共振成像的连通性研究越来越多地汇集在多个站点获得的数据。虽然这可能使研究人员加快招募或增加样本量，但多中心研究也可能在跨中心的连接性测量中引入系统性偏倚。在这项工作中，我们测量的网站间的连接效果和它对我们的能力，以检测个人和群体的差异的影响。我们的研究是基于真实的，而不是模拟的，多位点fMRI数据集收集在N = 345年轻，健康的受试者在8个扫描站点与3T扫描仪和异构扫描协议，从1000功能连接体项目。我们首先实证表明，在所有地点的群体水平上可靠地发现了典型的功能网络，并且地点间效应的幅度很小到中等，大脑连接的平均科恩效应大小低于0.5。然后，我们实施了一系列的蒙特卡洛模拟，基于真实数据，以评估多站点效应对检测能力的影响，在统计测试中使用一般线性模型比较两组（有和没有效果），以及用支持向量机预测组标签。作为参考，我们还使用相同的样本量在单个站点收集的fMRI数据进行了相同的模拟。模拟结果显示，使用数据从异质网站只有轻微降低我们的能力，检测到的变化相比，一个单一的研究与GLM，并有更大的影响预测精度。综上所述，我们的研究结果支持的可行性，多站点研究的rs-fMRI提供的样本量是足够大的。
英文摘要：
Connectivity studies using resting-state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed up recruitment or increase sample size, multisite studies also potentially introduce systematic biases in connectivity measures across sites. In this work, we measure the inter-site effect in connectivity and its impact on our ability to detect individual and group differences. Our study was based on real, as opposed to simulated, multisite fMRI datasets collected in N=345 young, healthy subjects across 8 scanning sites with 3T scanners and heterogeneous scanning protocols, drawn from the 1000 functional connectome project. We first empirically show that typical functional networks were reliably found at the group level in all sites, and that the amplitude of the inter-site effects was small to moderate, with a Cohen's effect size below 0.5 on average across brain connections. We then implemented a series of Monte-Carlo simulations, based on real data, to evaluate the impact of the multisite effects on detection power in statistical tests comparing two groups (with and without the effect) using a general linear model, as well as on the prediction of group labels with a support-vector machine. As a reference, we also implemented the same simulations with fMRI data collected at a single site using an identical sample size. Simulations revealed that using data from heterogeneous sites only slightly decreased our ability to detect changes compared to a monosite study with the GLM, and had a greater impact on prediction accuracy. Taken together, our results support the feasibility of multisite studies in rs-fMRI provided the sample size is large enough.
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