Ortega-Gutierrez Santiago, Petersen Nils, Masurkar Arjun, Reccius Andres, Huang Amy, Li Min, Choi Jae H, Marshall Randolph S
From the Department of Neurology, Stroke Division, Columbia University, New York, NY.
J Neuroimaging. 2014 Jul-Aug;24(4):379-86. doi: 10.1111/jon.12019. Epub 2013 Apr 22.
Cerebral autoregulation (CA) enables the brain to maintain stable cerebral blood flow (CBF). CA can be assessed noninvasively by determining correlations between CBF velocity (CBFV) and spontaneous changes in blood pressure. Postrecording signal analysis methods have included both frequency- and time-domain methods. However, the test-retest reliability, cross-validation, and determination of normal values have not been adequately established.
In 53 healthy volunteers, a transfer function analysis was applied to calculate phase shift (PS) and gain in the low frequency range (.06-.12 Hz) where CA is most apparent. Correlation analysis was used to derive mean velocity index (Mx). Intraclass correlation and bivariate correlation coefficients were applied to assess asymmetry, cross-validity, and test-retest results: The bihemispheric average PS, gain, and Mx means were 45.99+/-14.24°, .62+/-.38 cm/second/mmHg, and .41+/-.13, respectively. Gain exhibited a difference by age (P = .03). PS, gain, and Mx values showed excellent interhemispheric correlation (r > .8; P < .001). PS and gain showed good reliability (R ICC = .632, L ICC = .576; P < .001). PS and Mx showed fair correlation (r = -.37; P < .001).
CA parameters obtained by time- and frequency-domain methods correlate well, and show good interhemispheric and test-retest reliability. Group means from healthy controls may provide adequate norms for determining abnormal CA in cerebrovascular patients.
脑自动调节(CA)使大脑能够维持稳定的脑血流量(CBF)。通过确定脑血流速度(CBFV)与血压自发变化之间的相关性,可以非侵入性地评估CA。记录后信号分析方法包括频域和时域方法。然而,重测信度、交叉验证和正常值的确定尚未得到充分证实。
对53名健康志愿者应用传递函数分析来计算在CA最明显的低频范围(0.06 - 0.12 Hz)的相移(PS)和增益。使用相关分析得出平均速度指数(Mx)。应用组内相关系数和双变量相关系数来评估不对称性、交叉效度和重测结果:双侧半球平均PS、增益和Mx均值分别为45.99±14.24°、0.62±0.38 cm/秒/mmHg和0.41±0.13。增益随年龄存在差异(P = 0.03)。PS、增益和Mx值显示出极好的半球间相关性(r > 0.8;P < 0.001)。PS和增益显示出良好的信度(R ICC = 0.632,L ICC = 0.576;P < 0.001)。PS和Mx显示出中等相关性(r = -0.37;P < 0.001)。
通过时域和频域方法获得的CA参数相关性良好,并显示出良好的半球间和重测信度。健康对照的组均值可为确定脑血管疾病患者的异常CA提供适当的标准。