Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Centre for Doctoral Training in Healthcare Innovation, Institute of Biomedical Engineering Science, Department of Engineering, University of Oxford, Oxford, UK.
Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Neuroimage. 2018 Sep;178:370-384. doi: 10.1016/j.neuroimage.2018.04.077. Epub 2018 May 7.
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.
提出了一种用于稀疏、层次、逆协方差估计的贝叶斯模型，并将其应用于人类大脑的多体功能连接估计。它能够同时推断个体和群体水平上脑区之间连接的强度，适用于 fMRI、MEG 和 EEG 数据。该模型的两个版本都可以鼓励稀疏连接，一种是使用连续先验来抑制不相关的连接，另一种是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。使用模拟和静息状态功能成像数据集对该模型和代表逆协方差建模最新技术的十三种方法进行了全面评估。我们的新贝叶斯方法与最佳现有替代方法，即 Ng 等人的稀疏组高斯图形模型算法具有相似的性能，后者也是基于层次结构的。使用来自人类连接组计划的数据，我们表明这些层次模型能够将 MEG 贝塔波段功能网络的测量误差降低 10%，同时增加对功能连接遗传影响的估计。
