Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses
Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses
复制标题
DOI：
10.1016/j.neuroimage.2003.11.029
复制
发表时间：
2004-04-01
期刊：
影响因子：
5.7
通讯作者：
D'Esposito, M
中科院分区：
文献类型：
--
作者：
Handwerker, DA;Ollinger, JM;D'Esposito, M
血流动力学反应函数（HRF）的估计往往是事件相关的功能磁共振成像分析的组成部分。虽然HRF在个体和大脑区域之间存在差异，但很少有研究调查这些差异如何影响使用一般线性模型（GLM）的统计分析结果。在这项研究中，我们经验性地估计HRF从初级运动和视觉皮层和额叶和补充眼领域（SEF）在20名受试者。我们观察到受试者之间的变异性大于区域之间的变异性，并且在几对区域之间观察到峰值时间的相关变异。模拟研究了观察到的变异性对统计结果的影响，以及不同的实验设计和统计模型可以限制这些影响的方式。宽间距和快速事件相关的实验设计与两个采样率进行了测试。统计模型比较了经验得出的HRF的一个典型的HRF，并包括在GLM的HRF的一阶导数。估计和真实HRF之间的小差异不会导致假阴性，但在观察到的变化范围内的较大差异，如2.5秒的发作时间错误估计，导致假阴性。虽然小误差对活动检测的影响最小，但小至1 s的至发作时间错误估计影响模型参数估计，因此影响受试者的随机效应分析。实验和分析设计方法，如降低采样率或包括HRF的时间导数的GLM改善的结果，但没有消除HRF的错误估计所造成的误差。这些结果突出了确定最佳可能的HRF估计的益处和假设HRF在受试者或大脑区域之间的一致性的潜在负面后果。(C)2004年爱思唯尔公司All rights reserved.
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. (C) 2004 Elsevier Inc. All rights reserved.