LONG RANGE CORRELATIONS AND PHYSIOLOGICAL CONTROL

显然生理时间序列的随机波动 要有很好的结构。对这些波动的研究表明， 神经控制系统的当前状态影响未来状态 在一个出乎意料的长时间。对此进行定量分析 深远的影响表明，这种波动是相关的 在不同的时间尺度上以不同的数学模式 对应于具有幂律衰减的长程相关性。 因此，拟议研究的总体目标是调查 无法解释的，不规则的动力学， 神经控制下的生理波动-自主和非自主 自主的，通过统计物理学和非统计物理学中最近开发的方法， 线性数学此外，为了研究远程 病理条件下的相关性作为潜在的工具， 神经生理监测两个具体的模型系统将 分析以阐明神经生理学控制：（i） 人心脏搏动间隔时间序列，和（ii）人步态 跨距波动前者处于直接神经自主神经支配下 控制，而后者是非自主的。两者都有优势， 它们可以容易地非侵入性地测量。 我正在申请FIRST奖，以进行独立调查 具体目标如下： 1)检验长期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)检验长期相关性的改变 从而可用于临床 监测. 3)进一步发展和完善统计方法， 描述非平稳生理过程中的长程相关性 信号. 4)开发理论模型，解释远程 与神经生理学控制机制相关。这些 模型还应该能够解释远程故障 在一定的病理条件下。 这些目标直接关系到发展的实际需要， 也可以从非侵入性数据中获得神经生理学疾病的指数 作为理解共同的重要特征的长期目标， 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.