Estimation of brain functional connectivity (FC) is essential for understanding the functional organization in the brain and for identifying changes occurring due to neurological disorders, development, treatment, and other phenomena. Independent component analysis (ICA) is a matrix decomposition method that has been used extensively for estimation of brain functional networks and their FC. However, estimation of FC via ICA is often sub-optimal due to the use of ad-hoc methods or need for temporal dimension reduction prior to traditional ICA methods. Bayesian ICA methods can avoid dimension reduction, produce more accurate estimates, and facilitate inference via posterior distributions on the model parameters. In this paper, we propose a novel, computationally efficient Bayesian ICA method with population-derived priors on both the temporal covariance, representing FC, and the spatial components of the model. We propose two algorithms for parameter estimation: a Bayesian Expectation-Maximization algorithm with a Gibbs sampler at the E-step, and a more computationally efficient variational Bayes algorithm. Through extensive simulation studies using realistic fMRI data generation mechanisms, we evaluate the performance of the proposed methods and compare them with existing approaches. Finally, we perform a comprehensive evaluation of the proposed methods using fMRI data from over 400 healthy adults in the Human Connectome Project. Our analyses demonstrate that the proposed Bayesian ICA methods produce highly accurate measures of functional connectivity and spatial brain features. The proposed framework is computationally efficient and applicable to single-subject analysis, making it potentially clinically viable.

翻译：脑功能连接（FC）的估计对于理解大脑功能组织、识别由神经疾病、发育、治疗及其他现象引起的变化至关重要。独立成分分析（ICA）是一种矩阵分解方法，已被广泛用于脑功能网络及其FC的估计。然而，由于传统ICA方法常采用临时性方法或需要先进行时间维度降维，通过ICA估计FC往往效果欠佳。贝叶斯ICA方法可避免维度缩减、产生更精确的估计，并通过模型参数的后验分布促进推断。本文提出一种新颖且计算高效的贝叶斯ICA方法，该方法在代表FC的时间协方差和模型空间分量上均采用人群先验。我们提出两种参数估计算法：在E步结合吉布斯采样器的贝叶斯期望最大化算法，以及计算效率更高的变分贝叶斯算法。通过使用逼真fMRI数据生成机制进行大量仿真研究，我们评估了所提方法的性能，并与现有方法进行了比较。最后，我们利用人类连接组计划中400多名健康成年人的fMRI数据对所提方法进行了综合评价。分析结果表明，所提出的贝叶斯ICA方法能够生成高度精确的功能连接测量和空间脑特征。该框架计算高效且适用于单被试分析，具有潜在的临床可行性。