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.

中文翻译：

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图论分析识别静息状态动态功能网络连接的瞬态空间状态，并揭示精神分裂症的连接障碍

背景

动态功能网络连接 (dFNC) 总结了随时间变化的大脑网络之间的关联，并广泛用于研究动力学。然而，大多数先前的研究使用时间变异性计算 dFNC，而空间变异性开始受到越来越多的关注。因此，需要研究空间可变性以及时间和空间可变性之间的相互作用。

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获时间和空间可变性，并引入目标驱动方案来解决 dFNC 分析中的关键挑战——确定瞬态的数量。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) 的静息状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区分特征。对图论 (GT) 度量的综合研究确定了空间状态的最佳数量，并建议将中心性作为一个关键度量。四个网络在 SZ 和 HC 中的参与程度显着不同。与多个分布式大脑区域相关的组件的高度参与突出了 SZ 的连接障碍。一个额顶成分和一个额叶成分显示出更高的 HCs 参与度，表明相对于 SZs 的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能提供信息。所提出的目标驱动方案通过利用判别特征以更可解释的方式确定最佳状态数。

结论

GT 分析在 dFNC 分析中很有前景，因为它可以识别 dFNC 的独特瞬态空间状态，并揭示 SZ 中独特的生物医学模式。