Liu Aiping, Chen Xun, McKeown Martin J, Wang Z Jane
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada.
Department of Biomedical Engineering, School of Medical Engineering, Hefei University of Technology, Hefei, China.
IEEE Trans Biomed Eng. 2015 Feb;62(2):501-510. doi: 10.1109/TBME.2014.2359211. Epub 2014 Sep 19.
Despite recent progress on brain connectivity modeling using neuroimaging data such as fMRI, most current approaches assume that brain connectivity networks have time-invariant topology/coefficients. This is clearly problematic as the brain is inherently nonstationary. Here, we present a time-varying model to investigate the temporal dynamics of brain connectivity networks. The proposed method allows for abrupt changes in network structure via a fused least absolute shrinkage and selection operator (LASSO) scheme, as well as recovery of time-varying networks with smoothly changing coefficients via a weighted regression technique. Simulations demonstrate that the proposed method yields improved accuracy on estimating time-dependent connectivity patterns when compared to a static sparse regression model or a weighted time-varying regression model. When applied to real resting-state fMRI datasets from Parkinson's disease (PD) and control subjects, significantly different temporal and spatial patterns were found to be associated with PD. Specifically, PD subjects demonstrated reduced network variability over time, which may be related to impaired cognitive flexibility previously reported in PD. The temporal dynamic properties of brain connectivity in PD subjects may provide insights into brain dynamics associated with PD and may serve as a potential biomarker in future studies.
尽管最近在使用功能磁共振成像（fMRI）等神经成像数据进行脑连接建模方面取得了进展，但目前大多数方法都假定脑连接网络具有时不变的拓扑结构/系数。由于大脑本质上是非平稳的，这显然存在问题。在此，我们提出一种时变模型来研究脑连接网络的时间动态。所提出的方法通过融合的最小绝对收缩和选择算子（LASSO）方案允许网络结构的突然变化，以及通过加权回归技术恢复系数平滑变化的时变网络。模拟表明，与静态稀疏回归模型或加权时变回归模型相比，所提出的方法在估计时间相关的连接模式时具有更高的准确性。当应用于帕金森病（PD）患者和对照受试者的真实静息态fMRI数据集时，发现与PD相关的时间和空间模式存在显著差异。具体而言，PD患者表现出随着时间推移网络变异性降低，这可能与先前报道中PD患者认知灵活性受损有关。PD患者脑连接的时间动态特性可能为与PD相关的脑动力学提供见解，并可能在未来研究中作为潜在的生物标志物。
