Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions – such as their multifractality or information content –, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5–4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger long-range autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.

中文翻译：

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Delta 带神经活动的多重分形和基于熵的分析揭示了精神分裂症中功能连接动力学的改变


动态功能连接（DFC）是在过去十年中建立的，作为一种有效的方法来揭示神经相互作用的不平凡的、随时间变化的特性——例如它们的多重分形或信息内容——否则这些特性对传统的静态方法来说仍然是隐藏的。多种神经精神疾病被证明与 DFC 改变有关，其中精神分裂症 (SZ) 是此类疾病中研究最深入的疾病之一。在这里，我们分析了 14 名 SZ 患者和 14 名年龄和性别匹配的健康对照 (HC) 的静息态脑电图记录。我们根据 delta 带（0.5-4 Hz）神经活动重建了动态功能网络，并在各种全局网络拓扑测量中捕获了它们的时空动态。对获取的网络测量时间序列进行动态分析，包括多重分形分析和熵估计。除了组级比较之外，我们还构建了一个分类器来探索 DFC 特征在对个体案例进行分类时的潜力。我们发现 SZ 患者的 delta 带连接性更强，并且 DFC 方差增加。替代数据测试验证了 SZ 中 DFC 的真实多重分形性质，与对照组相比，患者表现出更强的长期自相关性和多重分形程度。熵分析表明 SZ 中 DFC 的时间复杂性降低。当使用这些指标作为特征时，在对个案进行分类时，总体交叉验证准确率可以达到超过 89%。我们的结果表明，DFC 的动态特征（例如其多重分形特性和熵）是 SZ 神经动力学改变的有效标志，并且不仅在更好地理解其病理生理学方面而且在改进其诊断方面具有巨大潜力。 所提出的框架很容易适用于精神分裂症以外的神经精神疾病。