Brain–heart interactions considering complex physiological data: processing schemes for time-variant, frequency-dependent, topographical and statistical examination of directed interactions by convergent cross mapping
Brain–heart interactions considering complex physiological data: processing schemes for time-variant, frequency-dependent, topographical and statistical examination of directed interactions by convergent cross mapping
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DOI：
10.1088/1361-6579/ab5050
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发表时间：
2019-10
影响因子：
3.2
通讯作者：
K. Schiecke;A. Schumann;F. Benninger;M. Feucht;K. Bär;P. Schlattmann
中科院分区：
文献类型：
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作者：
K. Schiecke;A. Schumann;F. Benninger;M. Feucht;K. Bär;P. Schlattmann
背景：在高度复杂的生理系统中，有多种复杂的方法可用于量化相互作用。在特定的生理状态或特定疾病中，脑-心相互作用在识别中枢神经系统和自主神经系统之间的耦合方面发挥着重要作用。这些交互作用分析的关键点是考虑到数据的非线性、直观的图形表示和对所获得的结果进行适当的统计评估的适当的预处理。目的：本研究的目的是在考虑前处理、图形表示和统计分析的基础上，为此类调查提供通用的处理方案。方法：使用两个已定义的数据集来开发这些处理方案。用非线性收敛交叉映射(CCM)技术研究了颞叶癫痫患儿发作前、发作后和发作后的脑-心相互作用，以及偏执型精神分裂症患者和健康对照组静息期的脑-心相互作用。采用替代数据、自助法和线性混合效应模型方法进行统计分析。主要结果：CCM能够揭示颞叶癫痫儿童脑心相互作用的特定和统计显著的时间和频率依赖模式，以及精神分裂症患者的局部和频率依赖的脑心相互作用的统计显著模式，以及显示与健康对照组受试者的差异。找到了合适的统计模型来量化组内差异。意义：给出了一般性的处理方案和前处理、适应交互分析和执行统计分析的要点。分析的一般概念也可用于表示更复杂的生理系统的相互作用分析和数据的其他方法。
Background: A multitude of complex methods is available to quantify interactions in highly complex physiological systems. Brain–heart interactions play an important role in identifying couplings between the central nervous system and the autonomic nervous system during defined physiological states or specific diseases. The crucial point of those interaction analyses is adequate pre-processing taking into account nonlinearity of data, intuitive graphical representation and suitable statistical evaluation of the achieved results. Objective: The aim of this study is to provide generalized processing schemes for such investigations taking into account pre-processing, graphical representation and statistical analysis. Approach: Two defined data sets were used to develop these processing schemes. Brain–heart interactions in children with temporal lobe epilepsy during the pre-ictal, ictal and post-ictal periods as well as in patients with paranoid schizophrenia and healthy control subjects during the resting state period were investigated by nonlinear convergent cross mapping (CCM). Surrogate data, bootstrapping and linear mixed-effects model approaches were utilized for statistical analyses. Main results: CCM was able to reveal specific and statistically significant time- and frequency-dependent patterns of brain–heart interactions for children with temporal lobe epilepsy and provide a statistically significant pattern of topographic- and frequency-dependent brain–heart interactions for schizophrenic patients, as well as to show the differences from healthy control subjects. Suitable statistical models were found to quantify group differences. Significance: Generalized processing schemes and crucial points of pre-processing, adapted interaction analysis and performed statistical analysis are provided. The general concept of analyses is transferable also to other methods of interactions analysis and data representing even more complex physiological systems.