A random covariance model for bi-level graphical modeling with application to resting-state fMRI data.
A random covariance model for bi-level graphical modeling with application to resting-state fMRI data.
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作者：
Zhang L;DiLernia A;Quevedo K;Camchong J;Lim K;Pan W
我们考虑一个新的问题，双层次的图形建模，其中多个个人的图形模型可以被认为是一个共同的组级图形模型和推理的组和个人级别的图形模型的变体是感兴趣的。这样的问题出现在许多应用中，包括多主题神经成像和基因组学数据分析。我们提出了一种新的和有效的统计方法，随机协方差模型，学习组和个人层次的图形模型同时进行。所提出的方法可以很好地解释为一个随机协方差模型，模仿线性回归中的平均结构的随机效应模型。它考虑了个体图形模型之间的相似性，识别了个体共享的组级连接，并同时推断出多个个体级网络。与现有的只关注个体级图形建模的多个图形建模方法相比，我们的模型学习了多个个体图形模型背后的组级结构，并且具有特别有吸引力的计算效率。我们进一步定义了一个措施的自由度的模型选择有用的模型的复杂性。我们证明了我们的方法的渐近性质，并通过仿真研究显示其有限样本性能。最后，我们将该方法应用于我们的激励临床数据，从被诊断为精神分裂症的参与者中收集的多主题静息状态功能磁共振成像数据集，识别功能连接的个人和组级图形模型。
We consider a novel problem, bi-level graphical modeling, in which multiple individual graphical models can be considered as variants of a common group-level graphical model and inference of both the group- and individual-level graphical models is of interest. Such a problem arises from many applications, including multi-subject neuro-imaging and genomics data analysis. We propose a novel and efficient statistical method, the random covariance model, to learn the group- and individual-level graphical models simultaneously. The proposed method can be nicely interpreted as a random covariance model that mimics the random effects model for mean structures in linear regression. It accounts for similarity between individual graphical models, identifies group-level connections that are shared by individuals, and simultaneously infers multiple individual-level networks. Compared to existing multiple graphical modeling methods that only focus on individual-level graphical modeling, our model learns the group-level structure underlying the multiple individual graphical models and enjoys computational efficiency that is particularly attractive for practical use. We further define a measure of degrees-of-freedom for the complexity of the model useful for model selection. We demonstrate the asymptotic properties of our method and show its finite-sample performance through simulation studies. Finally, we apply the method to our motivating clinical data, a multi-subject resting-state functional magnetic resonance imaging dataset collected from participants diagnosed with schizophrenia, identifying both individual-and group-level graphical models of functional connectivity.
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DOI：
10.1111/rssb.12123
发表时间：
2016-03-01
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
Qiu H;Han F;Liu H;Caffo B
通讯作者：
Caffo B
影响因子：
2.7
作者：
Bien, Jacob;Tibshirani, Robert J.
通讯作者：
Tibshirani, Robert J.
影响因子：
2.7
作者：
Drton, M;Richardson, TS
通讯作者：
Richardson, TS
影响因子：
4.8
作者：
An, LTH;Tao, PD
通讯作者：
Tao, PD
影响因子：
1.9
作者：
Lin Z;Wang T;Yang C;Zhao H
通讯作者：
Zhao H