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首页> 美国卫生研究院文献>Frontiers in Neuroscience >Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity
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Mixed Effects Models for Resampled Network Statistics Improves Statistical Power to Find Differences in Multi-Subject Functional Connectivity

机译：用于重采样网络统计的混合效应模型可提高统计能力以发现多主体功能连接中的差异

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Many complex brain disorders, such as autism spectrum disorders, exhibit a wide range of symptoms and disability. To understand how brain communication is impaired in such conditions, functional connectivity studies seek to understand individual differences in brain network structure in terms of covariates that measure symptom severity. In practice, however, functional connectivity is not observed but estimated from complex and noisy neural activity measurements. Imperfect subject network estimates can compromise subsequent efforts to detect covariate effects on network structure. We address this problem in the case of Gaussian graphical models of functional connectivity, by proposing novel two-level models that treat both subject level networks and population level covariate effects as unknown parameters. To account for imperfectly estimated subject level networks when fitting these models, we propose two related approaches—R2 based on resampling and random effects test statistics, and R3 that additionally employs random adaptive penalization. Simulation studies using realistic graph structures reveal that R2 and R3 have superior statistical power to detect covariate effects compared to existing approaches, particularly when the number of within subject observations is comparable to the size of subject networks. Using our novel models and methods to study parts of the ABIDE dataset, we find evidence of hypoconnectivity associated with symptom severity in autism spectrum disorders, in frontoparietal and limbic systems as well as in anterior and posterior cingulate cortices.
机译：许多复杂的脑部疾病，例如自闭症谱系障碍，表现出广泛的症状和残疾。为了了解在这种情况下大脑沟通如何受到损害，功能连接性研究试图通过测量症状严重程度的协变量来了解大脑网络结构的个体差异。然而，在实践中，并未观察到功能连通性，而是根据复杂且嘈杂的神经活动测量结果进行了估计。不完善的主题网络估计可能会损害随后的工作，以检测对网络结构的协变量影响。在功能连接的高斯图形模型的情况下，我们通过提出新颖的两级模型来解决此问题，该模型将主题级网络和人口级协变量效应都视为未知参数。为了在拟合这些模型时考虑不完全估计的主题级网络，我们提出了两种相关方法：基于重采样和随机效应测试统计的R 2 ，以及另外采用的R 3 随机自适应惩罚。使用逼真的图形结构进行的仿真研究表明，与现有方法相比，R 2 和R 3 具有更好的统计能力来检测协变量效应，尤其是当对象观察的数量可比较时到主题网络的规模。使用我们新颖的模型和方法来研究ABIDE数据集的一部分，我们发现与自闭症谱系障碍，额前和边缘系统以及扣带回前部和后部扣带回皮质的症状严重程度相关的证据不足。

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