Department of Medical Imaging, Guangdong Second Provincial General Hospital, Guangzhou, PR China.
Department of Neurology, China-Japan Friendship Hospital, Beijing, PR China.
Neuroimage Clin. 2020;28:102439. doi: 10.1016/j.nicl.2020.102439. Epub 2020 Sep 18.
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.
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.
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.
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.
根据最近的病因学进展，失眠障碍已被重新分类为短期/急性和慢性亚型。然而，理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。
使用 29 名短期/急性失眠参与者和 44 名慢性失眠参与者，我们使用全脑区域功能连接强度来预测未观察到的个体的匹兹堡睡眠质量指数（PSQI），应用多元相关性向量回归方法。使用留一法和 10 折交叉验证进行评估，全脑区域功能连接强度的模式在两个数据集均显著预测了未观察到的个体的 PSQI。
两组中对 PSQI 预测贡献最大的区域既有相似之处，也有不同之处。进一步的功能连接分析表明，短期/急性失眠和慢性失眠之间的网络间连接发生了重新组织。
本研究通过提供对睡眠质量的预测信息以及为失眠异质性的神经基础提供新的见解，可能具有临床价值。
