IEEE Trans Biomed Eng. 2022 Feb;69(2):590-601. doi: 10.1109/TBME.2021.3102015. Epub 2022 Jan 20.
Resting-state functional magnetic resonance imaging (rs-fMRI) has become a popular non-invasive way of diagnosing neurological disorders or their early stages by probing functional connectivity between different brain regions of interest (ROIs) across subjects. In the past decades, researchers have proposed many methods to estimate brain functional networks (BFNs) based on blood-oxygen-level-dependent (BOLD) signals captured by rs-fMRI. However, most of the existing methods estimate BFNs under the assumption that signals are independently sampled, which ignores the temporal dependency and sequential order of different time points (or volumes). To address this problem, in this paper, we first propose a novel BFN estimation model by introducing a latent variable to control the sequence of volumes for encoding the temporal dependency and sequential information of signals into the estimated BFNs. Then, we develop an efficient learning algorithm to solve the proposed model by the alternating optimization scheme. To verify the effectiveness of the proposed method, the estimated BFNs are used to identify subjects with mild cognitive impairment (MCIs) from normal controls (NCs). Experimental results show that our method outperforms the baseline methods in the terms of classification performance.
静息态功能磁共振成像（rs-fMRI）已成为一种通过探测不同感兴趣区域（ROIs）之间的功能连接来诊断神经障碍或其早期阶段的流行非侵入性方法。在过去的几十年中，研究人员已经提出了许多基于血氧水平依赖（BOLD）信号的方法来估计大脑功能网络（BFNs），这些信号是通过 rs-fMRI 捕获的。然而，大多数现有的方法都假设信号是独立采样的，这忽略了不同时间点（或体积）之间的时间依赖性和顺序。为了解决这个问题，本文首先通过引入一个潜在变量来控制体积的顺序，从而将信号的时间依赖性和顺序信息编码到估计的 BFNs 中，提出了一种新的 BFN 估计模型。然后，我们通过交替优化方案开发了一种有效的学习算法来解决所提出的模型。为了验证所提出方法的有效性，使用估计的 BFNs 从正常对照组（NCs）中识别出轻度认知障碍（MCI）患者。实验结果表明，我们的方法在分类性能方面优于基线方法。
