LONG RANGE CORRELATIONS AND PHYSIOLOGICAL CONTROL

生理时间序列表面上的随机波动实际上可能 要非常有条理。对这些波动的研究表明， 神经控制系统的当前状态会影响未来的状态 在一段出乎意料的长时间内。对此进行了量化分析 深远的影响表明，这种波动是相互关联的。 以不同的数学模式跨越多个时间尺度 与指数衰变的长程关联相对应。 因此，拟议研究的总体目标是调查 一大类复杂的、无法解释的、不规则的动态 神经控制下的生理波动--包括自主和非自主 自主神经，通过最近在统计物理和非统计物理中发展起来的方法 线性数学。此外，还研究了长程变化。 病理条件下的相关性质可作为潜在的工具 神经生理监测。两个具体的模型系统将是 分析以阐明神经生理学控制：(I)波动 人体心脏搏动间期时间序列，以及(Ii)人体步态 跨步波动。前者受直接神经自主神经支配。 控制，而后者是非自主的。两者都有一个优势， 它们可以很容易地非侵入性地测量。 我正在申请第一个奖项，以进行独立调查 具体目标如下： 1)检验长程相关性是由以下因素产生的假设 正常的神经生理控制机制。 2)检验假设：长程相关性的变化 指示病理性扰动，因此可用于临床 监控。 3)进一步发展和完善统计方法，以检测和 描述非平稳生理学中的长程相关性 信号。 4)发展理论模型，解释长程星系的存在 与神经生理控制机制有关的相关性。这些 模型还应能够说明远程故障的原因 在某些病理条件下的相关性。 这些目标直接关系到发展的现实需要。 来自非侵入性数据的神经生理学障碍指数也是如此 作为长期目标，我们要了解 神经控制机制。

Apparently random fluctuations in physiological time series may in fact be remarkably structured. Study of these fluctuations reveals that the current state of a neurally controlled system influences future states over an unexpectedly long period of time. Quantitative analysis of this far reaching influence indicates that such fluctuations are correlated over multiple time scales in a distinct mathematical pattern corresponding to long-range correlations with power-law decay. The general aim of the proposed research, therefore, is to investigate the unexplained, irregular dynamics of a wide class of complex physiological fluctuations under neural control-both autonomic and non- autonomic, by methods recently developed in statistical physics and non- linear mathematics. Furthermore, to study the alterations of long-range correlation properties under pathologic conditions as potential tools for neurophysiological monitoring. Two specific model systems will be analyzed for elucidating neurophysiological control: (i) fluctuations in human cardiac interbeat interval time series, and (ii) human gait interstride fluctuations. The former is under direct neuroautonomic control while the latter is non-autonomic. Both have the advantage that they can be readily measured non-invasively. I am applying for the FIRST Award to pursue independent investigations with the following specific aims: 1) Test the hypothesis that long-range correlations are generated by normal neurophysiological control mechanisms. 2) Test the hypothesis that alterations of long-range correlations indicate pathological perturbations and thus can be used for clinical monitoring. 3) Further develop and refine statistical methods that can detect and characterize long-range correlations in non-stationary physiological signals. 4) Develop theoretical models that account for the presence of long-range correlations associated with neurophysiological control mechanisms. These models should also be able to account for the breakdown of long-range correlations under certain pathological conditions. These aims are directly related to the practical need for developing indices of neurophysiological disorders from non-invasive data as well as the long term goal of understanding important features common to neural control mechanisms.