NONLINEAR AND FRACTAL MECHANISMS OF NEURAL CONTROL

复杂的神经生理系统表现出多种类型的动力学 表明存在非线性机制的行为， 包括i）分形（混沌）波动， 其特征在于l/f类谱，正的李雅普诺夫指数， 有限关联维数; ii）突变（分叉）; 和iii）持续振荡。 最近开发的方法， 非线性数学可能适用于识别和分析 这种非线性动力学行为，但这些 神经生理学数据的分析方法需要 演示。 我们已经发现， 心率提供了一种容易获得模型系统， 研究神经自主神经系统中非线性动力学的变化 网络，本提案的总体目的是评估 非线性方法对这些数据的适用性。 心搏间期的波动，由 副交感神经-交感神经相互作用，将记录24 小时的时间周期，然后将受到数字信号 用于频谱分析、相空间映射的处理，以及 分形维数和其他非线性度量的计算。 我们 目的是确定这些分析方法是否证明 非线性神经自主控制过程的存在。 具体而言，我们建议： 1. 为了验证健康人心率调节的假设， 个体受分形（混沌）神经自主控制的支配 机制等 2. 为了验证一个假设，即各种功能障碍导致 在表现出分叉、非线性振荡 以及分形可变性的丧失。 3. 建立一个非线性神经生理数学模型 神经自主性心率控制， 正常心率波动的变异性和 实际观察到的分叉和振荡行为， 某些病理条件。

Complex neurophysiological systems exhibit many types of dynamical behavior that indicate the presence of nonlinear mechanisms, including i) fractal (chaotic) fluctuations, which are characterized by l/f-like spectra, positive Lyapunov exponents and finite correlation dimension; ii) abrupt changes (bifurcations); and iii) sustained oscillations. Recently developed methods of nonlinear mathematics might be appropriate to identify and analyze such nonlinear dynamical behavior, but the suitability of those methods for the analysis of neurophysiological data needs to be demonstrated. We have found that beat-to-beat fluctuations in heart rate provide a readily accessible model system for investigating the variety of nonlinear dynamics in a neuroautonomic network, and the general aim of this proposal is to evaluate the applicability of the nonlinear methodology to that data. Fluctuations in cardiac interbeat interval, which are modulated by parasympathetic-sympathetic interactions, will be recorded for 24 hour time periods and will then be subjected to digital signal processing for spectral analysis, phase space mapping, and calculation of fractal dimension and other nonlinear metrics. Our aim is to determine whether these analytical methods demonstrate the presence of nonlinear neuroautonomic control processes. Specifically, we propose: 1. To test the hypothesis that heartrate regulation in healthy individuals is governed by fractal (chaotic) neuroautonomic control mechanisms. 2. To test the hypothesis that a variety of dysfunctions result in heartrate data that exhibit bifurcations, nonlinear oscillations and the loss of fractal variability. 3. To develop a nonlinear mathematical neurophysiological model of neuroautonomic heartrate control that accounts for the fractal variability of normal heartrate fluctuations and for the bifurcations and oscillatory behavior actually observed under certain pathologic conditions.