Schulz Steffen, Haueisen Jens, Bär Karl-Jürgen, Voss Andreas
Institute of Innovative Health Technologies, University of Applied Sciences, 07745 Jena, Germany.
Institute of Biomedical Engineering and Informatics, University of Technology, 98693 Ilmenau, Germany.
Entropy (Basel). 2019 Jul 26;21(8):733. doi: 10.3390/e21080733.
The multivariate analysis of coupling pathways within physiological (sub)systems focusing on identifying healthy and diseased conditions. In this study, we investigated a part of the central-autonomic-network (CAN) in 17 patients suffering from schizophrenia (SZO) compared to 17 age-gender matched healthy controls (CON) applying linear and nonlinear causal coupling approaches (normalized short time partial directed coherence, multivariate transfer entropy). Therefore, from all subjects continuous heart rate (successive beat-to-beat intervals, BBI), synchronized maximum successive systolic blood pressure amplitudes (SYS), synchronized calibrated respiratory inductive plethysmography signal (respiratory frequency, RESP), and the power P of frontal EEG activity were investigated for 15 min under resting conditions. The CAN revealed a bidirectional coupling structure, with central driving towards blood pressure (SYS), and respiratory driving towards P. The central-cardiac, central-vascular, and central-respiratory couplings are more dominated by linear regulatory mechanisms than nonlinear ones. The CAN showed significantly weaker nonlinear central-cardiovascular and central-cardiorespiratory coupling pathways, and significantly stronger linear central influence on the vascular system, and on the other hand significantly stronger linear respiratory and cardiac influences on central activity in SZO compared to CON, and thus, providing better understanding of the interrelationship of central and autonomic regulatory mechanisms in schizophrenia might be useful as a biomarker of this disease.
对生理（子）系统内耦合途径进行多变量分析，重点是识别健康和疾病状况。在本研究中，我们应用线性和非线性因果耦合方法（归一化短时偏定向相干、多变量转移熵），对17例精神分裂症患者（SZO）与17名年龄和性别匹配的健康对照者（CON）的中枢自主网络（CAN）的一部分进行了研究。因此，在静息条件下，对所有受试者连续15分钟监测心率（逐搏间期，BBI）、同步最大连续收缩压幅度（SYS）、同步校准的呼吸感应体积描记信号（呼吸频率，RESP）以及额叶脑电图活动的功率P。CAN显示出双向耦合结构，中枢驱动血压（SYS），呼吸驱动P。中枢-心脏、中枢-血管和中枢-呼吸耦合更多地由线性调节机制而非非线性调节机制主导。与CON相比，SZO中CAN的非线性中枢-心血管和中枢-心肺耦合途径明显较弱，中枢对血管系统的线性影响明显较强，另一方面，呼吸和心脏对中枢活动的线性影响明显较强，因此，更好地理解精神分裂症中枢和自主调节机制的相互关系可能作为该疾病的生物标志物。
