Handwerker Daniel A, Ollinger John M, D'Esposito Mark
Henry H. Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA 94720, USA.
Neuroimage. 2004 Apr;21(4):1639-51. doi: 10.1016/j.neuroimage.2003.11.029.
Estimates of hemodynamic response functions (HRF) are often integral parts of event-related fMRI analyses. Although HRFs vary across individuals and brain regions, few studies have investigated how variations affect the results of statistical analyses using the general linear model (GLM). In this study, we empirically estimated HRFs from primary motor and visual cortices and frontal and supplementary eye fields (SEF) in 20 subjects. We observed more variability across subjects than regions and correlated variation of time-to-peak values across several pairs of regions. Simulations examined the effects of observed variability on statistical results and ways different experimental designs and statistical models can limit these effects. Widely spaced and rapid event-related experimental designs with two sampling rates were tested. Statistical models compared an empirically derived HRF to a canonical HRF and included the first derivative of the HRF in the GLM. Small differences between the estimated and true HRFs did not cause false negatives, but larger differences within an observed range of variation, such as a 2.5-s time-to-onset misestimate, led to false negatives. Although small errors minimally affected detection of activity, time-to-onset misestimates as small as 1 s influenced model parameter estimation and therefore random effects analyses across subjects. Experiment and analysis design methods such as decreasing the sampling rate or including the HRF's temporal derivative in the GLM improved results, but did not eliminate errors caused by HRF misestimates. These results highlight the benefits of determining the best possible HRF estimate and potential negative consequences of assuming HRF consistency across subjects or brain regions.
血流动力学响应函数（HRF）的估计通常是事件相关功能磁共振成像（fMRI）分析的重要组成部分。尽管HRF在个体和脑区之间存在差异，但很少有研究探讨这些差异如何影响使用一般线性模型（GLM）进行的统计分析结果。在本研究中，我们对20名受试者的初级运动皮层、视觉皮层、额叶和辅助眼区（SEF）的HRF进行了实证估计。我们观察到受试者之间的变异性大于脑区之间的变异性，并且几对脑区的峰值时间存在相关变异。模拟研究了观察到的变异性对统计结果的影响，以及不同实验设计和统计模型限制这些影响的方式。测试了具有两种采样率的广泛间隔和快速事件相关实验设计。统计模型将实证推导的HRF与标准HRF进行比较，并在GLM中纳入了HRF的一阶导数。估计的HRF与真实HRF之间的小差异不会导致假阴性，但在观察到的变异范围内的较大差异，例如2.5秒的起始时间估计错误，会导致假阴性。尽管小误差对活动检测的影响最小，但小至1秒的起始时间估计错误会影响模型参数估计，从而影响跨受试者的随机效应分析。降低采样率或在GLM中纳入HRF的时间导数等实验和分析设计方法改善了结果，但并未消除由HRF估计错误引起的误差。这些结果突出了确定最佳HRF估计的好处，以及假设跨受试者或脑区的HRF一致性可能带来的负面后果。
