基于图论的分析方法可以识别静息态功能网络连接的瞬态空间状态，并揭示精神分裂症中的连接异常。
Graph-theoretical analysis identifies transient spatial states of resting-state dynamic functional network connectivity and reveals dysconnectivity in schizophrenia.
机构信息
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA.
Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, 21250, USA.
出版信息
J Neurosci Methods. 2021 Feb 15;350:109039. doi: 10.1016/j.jneumeth.2020.109039. Epub 2020 Dec 25.
BACKGROUND
Dynamic functional network connectivity (dFNC) summarizes associations among time-varying brain networks and is widely used for studying dynamics. However, most previous studies compute dFNC using temporal variability while spatial variability started receiving increasing attention. It is hence desirable to investigate spatial variability and the interaction between temporal and spatial variability.
NEW METHOD
We propose to use an adaptive variant of constrained independent vector analysis to simultaneously capture temporal and spatial variability, and introduce a goal-driven scheme for addressing a key challenge in dFNC analysis---determining the number of transient states. We apply our methods to resting-state functional magnetic resonance imaging data of schizophrenia patients (SZs) and healthy controls (HCs).
RESULTS
The results show spatial variability provides more features discriminative between groups than temporal variability. A comprehensive study of graph-theoretical (GT) metrics determines the optimal number of spatial states and suggests centrality as a key metric. Four networks yield significantly different levels of involvement in SZs and HCs. The high involvement of a component that relates to multiple distributed brain regions highlights dysconnectivity in SZ. One frontoparietal component and one frontal component demonstrate higher involvement in HCs, suggesting a more efficient cognitive control system relative to SZs.
COMPARISON WITH EXISTING METHODS
Spatial variability is more informative than temporal variability. The proposed goal-driven scheme determines the optimal number of states in a more interpretable way by making use of discriminative features.
CONCLUSION
GT analysis is promising in dFNC analysis as it identifies distinctive transient spatial states of dFNC and reveals unique biomedical patterns in SZs.
背景
动态功能网络连接（dFNC）总结了时变脑网络之间的关联，广泛用于研究动力学。然而，大多数先前的研究都是使用时间变异性来计算 dFNC，而空间变异性开始受到越来越多的关注。因此，研究空间变异性以及时间和空间变异性之间的相互作用是很有必要的。
新方法
我们建议使用约束独立向量分析的自适应变体来同时捕捉时间和空间变异性，并引入一种目标驱动的方案来解决 dFNC 分析中的一个关键挑战——确定瞬态状态的数量。我们将我们的方法应用于精神分裂症患者（SZ）和健康对照（HC）的静息态功能磁共振成像数据。
结果
结果表明，空间变异性比时间变异性提供了更多的组间可区分特征。对图论（GT）指标的全面研究确定了空间状态的最佳数量，并提出了中心性作为一个关键指标。四个网络在 SZ 和 HC 中表现出显著不同的参与程度。与多个分布式脑区相关的一个组件的高参与度突出了 SZ 的连接中断。一个额顶组件和一个额组件表现出 HC 中的更高参与度，这表明相对于 SZ，认知控制系统更有效。
与现有方法的比较
空间变异性比时间变异性更具信息量。所提出的目标驱动方案通过利用判别特征，以更具可解释性的方式确定了最佳状态数量。
结论
GT 分析在 dFNC 分析中很有前景，因为它可以识别出 dFNC 的独特瞬态空间状态，并揭示出 SZ 中的独特生物医学模式。
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