Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden.
Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.
Brain Connect. 2020 Jun;10(5):202-211. doi: 10.1089/brain.2020.0740.
This article proposes a Bayesian hierarchical mixture model to analyze functional brain connectivity where mixture components represent "positively connected" and "non-connected" brain regions. Such an approach provides a data-informed separation of reliable and spurious connections in contrast to arbitrary thresholding of a connectivity matrix. The hierarchical structure of the model allows simultaneous inferences for the entire population as well as for each individual subject. A new connectivity measure, the posterior probability of a given pair of brain regions of a specific subject to be connected given the observed correlation of regions' activity, can be computed from the model fit. The posterior probability reflects the connectivity of a pair of regions relative to the overall connectivity pattern of an individual, which is overlooked in traditional correlation analyses. This article demonstrates that using the posterior probability might diminish the effect of spurious connections on inferences, which is present when a correlation is used as a connectivity measure. In addition, simulation analyses reveal that the sparsification of the connectivity matrix using the posterior probabilities might outperform the absolute thresholding based on correlations. Therefore, we suggest that posterior probability might be a beneficial measure of connectivity compared with the correlation. The applicability of the introduced method is exemplified by a study of functional resting-state brain connectivity in older adults.
本文提出了一种贝叶斯层次混合模型，用于分析功能脑连接，其中混合成分代表“正连接”和“无连接”的脑区。与连接矩阵的任意阈值相比，这种方法为可靠和虚假连接提供了一种数据驱动的分离。该模型的层次结构允许对整个群体以及每个个体进行同时推断。可以从模型拟合中计算出特定个体特定脑区对之间给定相关性的后验概率，作为新的连接度量。后验概率反映了一对区域相对于个体整体连接模式的连接性，这在传统的相关分析中被忽略。本文表明，当使用相关性作为连接度量时，使用后验概率可以减少虚假连接对推断的影响。此外，模拟分析表明，使用后验概率稀疏化连接矩阵可能优于基于相关性的绝对阈值。因此，我们建议后验概率可能是一种比相关性更有益的连接度量。所提出方法的适用性通过对老年人功能静息态脑连接的研究进行了说明。
