Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.
Multi-subject hierarchical inverse covariance modelling improves estimation of functional brain networks.
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DOI：
10.1016/j.neuroimage.2018.04.077
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发表时间：
2018-09
期刊：
影响因子：
5.7
通讯作者：
Smith SM
中科院分区：
文献类型：
--
作者：
Colclough GL;Woolrich MW;Harrison SJ;Rojas López PA;Valdes-Sosa PA;Smith SM
提出了稀疏、分层、逆协方差估计的贝叶斯模型，并将其应用于人脑中的多主体功能连接估计。它能够同时推断受试者和群体水平上大脑区域之间的连接强度，并且适用于功能磁共振成像、脑磁图和脑电图数据。该模型的两个版本都可以鼓励稀疏连接，要么使用连续先验来抑制不相关的连接，要么使用网络结构的显式描述来估计每对区域之间的连接概率。使用模拟和静息态功能成像数据集对该模型以及代表逆协方差建模技术水平的十三种方法进行了大规模评估。我们新颖的贝叶斯方法与现有的最佳替代方案（Ng 等人的稀疏组高斯图形模型算法）具有相似的性能，该算法也基于分层结构。使用人类连接组项目的数据，我们表明这些分层模型能够将 MEG beta 带功能网络的测量误差减少 10%，从而导致遗传对功能连接影响的估计随之增加。
A Bayesian model for sparse, hierarchical, inver-covariance estimation is presented, and applied to multi-subject functional connectivity estimation in the human brain. It enables simultaneous inference of the strength of connectivity between brain regions at both subject and population level, and is applicable to fMRI, MEG and EEG data. Two versions of the model can encourage sparse connectivity, either using continuous priors to suppress irrelevant connections, or using an explicit description of the network structure to estimate the connection probability between each pair of regions. A large evaluation of this model, and thirteen methods that represent the state of the art of inverse covariance modelling, is conducted using both simulated and resting-state functional imaging datasets. Our novel Bayesian approach has similar performance to the best extant alternative, Ng et al.'s Sparse Group Gaussian Graphical Model algorithm, which also is based on a hierarchical structure. Using data from the Human Connectome Project, we show that these hierarchical models are able to reduce the measurement error in MEG beta-band functional networks by 10%, producing concomitant increases in estimates of the genetic influence on functional connectivity.
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影响因子：
5.7
作者：
Colclough GL;Woolrich MW;Tewarie PK;Brookes MJ;Quinn AJ;Smith SM
通讯作者：
Smith SM
影响因子：
3.7
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
5.7
作者：
Colclough GL;Brookes MJ;Smith SM;Woolrich MW
通讯作者：
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影响因子：
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作者：
DEMPSTER, AP
通讯作者：
DEMPSTER, AP