研究期間：10108~10207;Cerebral autoregulatory mechanisms are engaged to compensate for metabolic demands and perfusion pressure variations under physiologic and pathologic conditions. Dynamic autoregulation reflects the ability of the cerebral microvasculature to control perfusion by adjusting the small-vessel resistances in response to beat-to-beat blood pressure (BP) fluctuations by involving complex interactions of myogenic and neurogenic regulations. Reliable and noninvasive assessment of cerebral autoregulation (CA) is a major challenge in medical diagnosis. Conventional approaches typically model cerebral regulation using mathematical models of a linear and time-invariant system to simulate the dynamics of BP as an input to the system, and cerebral blood flow as output. A transfer function is typically used to explore the relationship between BP and cerebral blood flow velocity (BFV) by calculating gain and phase shift between the BP and BFV power spectra. This Fourier transform based approach, however, assumed that signals are composed of superimposed sinusoidal oscillations of constant amplitude and period at a pre-determined frequency range. This assumption puts an unavoidable limitation on the reliability and application of the method, because the recorded BP and BFV signals from clinical settings are often nonstationary and are modulated by nonlinearly interacting processes at multiple time-scales corresponding to the beat-to-beat systolic pressure, respiration, spontaneous BP fluctuations, and those induced by interventions. Here, an advanced nonlinear decomposition algorithm-Hilbert Huang transform (HHT) will be incorporate into our newly proposed multimodel pressure flow (MMPF) analysis. This improved algorithm can illustrates the relationship between BP and BFV in more details over several time scales, and demonstrate better performances for certain specific types of nonstationarities. In this project, we will further improve MMPF and apply it to 1) qualitatively and quantitatively evaluate the changes of CA in patients with carotid or vertebrovascular artery stenosis in multiple time scales and their response to stent implantation.2) find the possible parameters to describe the dysfunction of CA in patients with stroke and assess the relationships between stroke prognosis and the newly derived parameters. 3) probe the underlying mechanisms over different time scales by sequential tasks in normal control subjects and build up a realistic model of human cerebral autoregulation based on the results. MMPF analysis is a promising method in analyzing the nonlinear and nonstationary processes of biological signals. In combination of three synchronized BFV signals from different region of cerebrum and noninvasive blood pressure monitor, the spatial and temporal changes of cerebral autoregulation in different diseases and physiological conditions can be noninvasively explored in a more comprehensive way.