Biomedical Center Martin, Comenius University in Bratislava, Jessenius Faculty of Medicine, Mala Hora 4C, 03601, Martin, Slovakia; Department of Physiology, Comenius University in Bratislava, Jessenius Faculty of Medicine, Mala Hora 4C, 03601, Martin, Slovakia.
Biomedical Center Martin, Comenius University in Bratislava, Jessenius Faculty of Medicine, Mala Hora 4C, 03601, Martin, Slovakia; Department of Physiology, Comenius University in Bratislava, Jessenius Faculty of Medicine, Mala Hora 4C, 03601, Martin, Slovakia.
Comput Biol Med. 2018 Jul 1;98:48-57. doi: 10.1016/j.compbiomed.2018.05.007. Epub 2018 May 5.
Cardiovascular complexity is a feature of healthy physiological regulation, which stems from the simultaneous activity of several cardiovascular reflexes and other non-reflex physiological mechanisms. It is manifested in the rich dynamics characterizing the spontaneous heart rate and blood pressure variability (HRV and BPV). The present study faces the challenge of disclosing the origin of short-term HRV and BPV from the statistical perspective offered by information theory. To dissect the physiological mechanisms giving rise to cardiovascular complexity in different conditions, measures of predictive information, information storage, information transfer and information modification were applied to the beat-to-beat variability of heart period (HP), systolic arterial pressure (SAP) and respiratory volume signal recorded non-invasively in 61 healthy young subjects at supine rest and during head-up tilt (HUT) and mental arithmetics (MA). Information decomposition enabled to assess simultaneously several expected and newly inferred physiological phenomena, including: (i) the decreased complexity of HP during HUT and the increased complexity of SAP during MA; (ii) the suppressed cardiorespiratory information transfer, related to weakened respiratory sinus arrhythmia, under both challenges; (iii) the altered balance of the information transferred along the two arms of the cardiovascular loop during HUT, with larger baroreflex involvement and smaller feedforward mechanical effects; and (iv) an increased importance of direct respiratory effects on SAP during HUT, and on both HP and SAP during MA. We demonstrate that a decomposition of the information contained in cardiovascular oscillations can reveal subtle changes in system dynamics and improve our understanding of the complexity changes during physiological challenges.
心血管复杂性是健康生理调节的一个特征，它源于几个心血管反射和其他非反射生理机制的同时活动。它表现在自发心率和血压变异性（HRV 和 BPV）的丰富动力学特征中。本研究面临着从信息论提供的统计角度揭示短期 HRV 和 BPV 起源的挑战。为了剖析不同条件下心血管复杂性产生的生理机制，应用预测信息、信息存储、信息传递和信息修正度量来分析 61 名健康年轻受试者在仰卧位休息、头高位倾斜（HUT）和心算（MA）期间非侵入性记录的心率（HP）、收缩压（SAP）和呼吸量信号的逐拍变异性。信息分解能够同时评估几种预期和新推断的生理现象，包括：（i）HUT 期间 HP 复杂性降低，MA 期间 SAP 复杂性增加；（ii）两种挑战下，心肺信息传递受到抑制，与呼吸窦性心律失常减弱有关；（iii）HUT 期间心血管环两个臂上传递的信息平衡发生改变，压力反射参与增加，前馈机械效应减小；（iv）HUT 期间 SAP 直接呼吸效应的重要性增加，MA 期间 HP 和 SAP 也如此。我们证明，对心血管振荡中包含的信息进行分解可以揭示系统动态的微妙变化，并增进我们对生理挑战期间复杂性变化的理解。
