Objectives

Insomnia disorder has been reclassified into short-term/acute and chronic subtypes based on recent etiological advances. However, understanding the similarities and differences in the neural mechanisms underlying the two subtypes and accurately predicting the sleep quality remain challenging.

Methods

Using 29 short-term/acute insomnia participants and 44 chronic insomnia participants, we used whole-brain regional functional connectivity strength to predict unseen individuals’ Pittsburgh sleep quality index (PSQI), applying the multivariate relevance vector regression method. Evaluated using both leave-one-out and 10-fold cross-validation, the pattern of whole-brain regional functional connectivity strength significantly predicted an unseen individual’s PSQI in both datasets.

Results

There were both similarities and differences in the regions that contributed the most to PSQI prediction between the two groups. Further functional connectivity analysis suggested that between-network connectivity was re-organized between short-term/acute insomnia and chronic insomnia.

Conclusions

The present study may have clinical value by informing the prediction of sleep quality and providing novel insights into the neural basis underlying the heterogeneity of insomnia.

中文翻译：

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失眠患者睡眠质量的功能性连接套指纹：使用机器学习的个性化样本外预测

目标

根据最近的病因学进展，失眠症已被分为短期/急性和慢性亚型。然而，了解这两种亚型的神经机制的异同并准确预测睡眠质量仍然具有挑战性。

方法

我们使用29个短期/急性失眠参与者和44个慢性失眠参与者，使用多元相关向量回归方法，使用全脑区域功能连接强度预测看不见的个体的匹兹堡睡眠质量指数（PSQI）。使用留一法和十倍交叉验证法进行评估，全脑区域功能连接强度的模式在两个数据集中均显着预测了一个看不见的个体的PSQI。

结果

两组之间对PSQI预测贡献最大的区域既有相似之处，也有差异。进一步的功能连通性分析表明，短期/急性失眠和慢性失眠之间已重新组织了网络之间的连通性。

结论

通过告知睡眠质量的预测并提供失眠异质性背后的神经基础的新颖见解，本研究可能具有临床价值。