Department of Radiology, Yilong Country Hospital of Traditional Chinese Medicine, Nanchong, 637000, Sichuan, China.
Department of Radiology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Institute of Brain Function, Nanchong, 637000, Sichuan, China.
Sleep Breath. 2024 Jun;28(3):1409-1414. doi: 10.1007/s11325-024-03018-z. Epub 2024 Mar 7.
From a clinical point of view, how to force a transition from insomnia brain state to healthy brain state by external driven stimulation is of great interest. This needs to define brain state of insomnia disorder as metastable substates. The current study was to identify recurrent substates of insomnia disorder in terms of probability of occurrence, lifetime, and alternation profiles by using leading eigenvector dynamics analysis (LEiDA) method.
We enrolled 32 patients with insomnia disorder and 30 healthy subjects. We firstly obtained the BOLD phase coherence matrix from Hilbert transform of BOLD signals and then extracted all the leading eigenvectors from the BOLD phase coherence matrix for all subjects across all time points. Lastly, we clustered the leading eigenvectors using a k-means clustering algorithm to find the probabilistic metastable substates (PMS) and calculate the probability of occurrence and associated lifetime for substates.
The resulting 3 clusters were optimal for brain state of insomnia disorder and healthy brain state, respectively. The occurred probabilities of the PMS were significantly different between the patients with insomnia disorder and healthy subjects, with 0.51 versus 0.44 for PMS-1 (p < 0.001), 0.25 versus 0.27 for PMS-2 (p = 0.051), and 0.24 versus 0.29 for PMS-3 (p < 0.001), as well as the lifetime (in TR) of 36.65 versus 33.15 for PMS-1 (p = 0.068), 14.36 versus 15.43 for PMS-2 (p = 0.117), and 14.80 versus 16.34 for PMS-3 (p = 0.042). The values of the diagonal of the transition matrix were much higher than the probabilities of switching states, indicating the metastable nature of substates.
The resulted probabilistic metastable substates hint the characteristic brain dynamics of insomnia disorder. The results may lay a foundation to help determine how to force a transition from insomnia brain state to healthy brain state by external driven stimulation.
从临床角度来看,如何通过外部驱动刺激将失眠的大脑状态强制转变为健康的大脑状态非常重要。这需要将失眠障碍的大脑状态定义为亚稳态子状态。本研究旨在使用主特征向量动力学分析(LEiDA)方法,根据出现概率、寿命和交替特征来识别失眠障碍的复发性亚稳态。
我们招募了 32 名失眠症患者和 30 名健康受试者。我们首先从 BOLD 信号的希尔伯特变换中获得 BOLD 相位相干矩阵,然后从所有受试者在所有时间点的 BOLD 相位相干矩阵中提取所有主特征向量。最后,我们使用 k-均值聚类算法对主特征向量进行聚类,以找到概率亚稳态子状态(PMS)并计算子状态的出现概率和相关寿命。
结果聚类为 3 个簇,分别对应失眠障碍和健康大脑状态的最佳脑状态。PMS 的出现概率在失眠症患者和健康受试者之间存在显著差异,PMS-1 为 0.51 对 0.44(p<0.001),PMS-2 为 0.25 对 0.27(p=0.051),PMS-3 为 0.24 对 0.29(p<0.001),以及 PMS-1 的寿命(在 TR 中)为 36.65 对 33.15(p=0.068),PMS-2 为 14.36 对 15.43(p=0.117),PMS-3 为 14.80 对 16.34(p=0.042)。转换矩阵的对角线上的值远高于状态切换的概率,表明子状态具有亚稳态性质。
得到的概率亚稳态子状态提示了失眠障碍的特征性大脑动力学。结果可能为通过外部驱动刺激将失眠大脑状态强制转变为健康大脑状态提供基础。