血流的自我調控為一獨特的重要機制，使局部血流在血壓變化的情況下仍可維持恆定。當腦血流自我調控能力下降甚至喪失時，腦部便容易因內在或外在刺激造成的血壓波動受到傷害。因此腦血流自我調控能力評估、了解腦血流自我調控機轉，進而控制並治療各種不同生理狀態下的腦血流自我調控，一直為基礎醫學和臨床醫學重心之一。雖然腦血流自我調控已在許多臨床議題上有很好的發揮，但目前研究結果指出腦血流自我調控機制相當複雜且具異質性，因而運用線性模式演算法分析腦血流自我調控，無法完全掌握此一複雜調控系統。以希爾博特-黃變換為基礎的多模態壓流分析可適應具非線性及非穩態特質的生理訊號，透過系統性比較亦已證實與傳統分析方法相較，多模態壓流分析更能不被訊號中斷、儀器校正或雜訊干擾等生理訊號採集上常見的影響結果。本計劃將進一步改良該演算法，並將與臨床醫師合作，導入新型多模態壓流分析於自發性腦血流自我調控評估，以1)探究中風或前端供應腦部循環的血管發生狹窄時對腦血流自我調控的影響2)區別不同時間尺度之作用機轉對腦血流自我調控的影響3)建立多參數模型，以評估不同生理狀態或疾病影響下的腦血流自我調控。本三年期計畫主要期望將本實驗室目前發展已漸臻成熟的多模態壓流分析方法，應用於臨床同步測得之中大腦動脈與後大腦動脈的腦血流速訊號，及以完全非侵入式方法測得的連續血壓訊號，以評估腦血流自我調控功能，同時從多時間尺度面向探討不同刺激方式、不同疾病或生理狀態可能帶來的影響。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. 研究期間：10008 ~ 10107
