Demidenko Michael I, Mumford Jeanette A, Poldrack Russell A
Department of Psychology, Stanford University, Stanford, United States.
bioRxiv. 2024 Jul 9:2024.03.19.585755. doi: 10.1101/2024.03.19.585755.
Empirical studies reporting low test-retest reliability of individual blood oxygen-level dependent (BOLD) signal estimates in functional magnetic resonance imaging (fMRI) data have resurrected interest among cognitive neuroscientists in methods that may improve reliability in fMRI. Over the last decade, several individual studies have reported that modeling decisions, such as smoothing, motion correction and contrast selection, may improve estimates of test-retest reliability of BOLD signal estimates. However, it remains an empirical question whether certain analytic decisions improve individual and group level reliability estimates in an fMRI task across multiple large, independent samples. This study used three independent samples (s: 60, 81, 119) that collected the same task (Monetary Incentive Delay task) across two runs and two sessions to evaluate the effects of analytic decisions on the individual (intraclass correlation coefficient [ICC(3,1)]) and group (Jaccard/Spearman ) reliability estimates of BOLD activity of task fMRI data. The analytic decisions in this study vary across four categories: smoothing kernel (five options), motion correction (four options), task parameterizing (three options) and task contrasts (four options), totaling 240 different pipeline permutations. Across all 240 pipelines, the median ICC estimates are consistently low, with a maximum median ICC estimate of .43 - .55 across the three samples. The analytic decisions with the greatest impact on the median ICC and group similarity estimates are the contrast, Cue Model parameterization and a larger smoothing kernel. Using an in a contrast condition meaningfully increased group similarity and ICC estimates as compared to using the cue. This effect was largest for the Cue Model parameterization; however, improvements in reliability came at the cost of interpretability. This study illustrates that estimates of reliability in the MID task are consistently low and variable at small samples, and a higher test-retest reliability may not always improve interpretability of the estimated BOLD signal.
实证研究报告称,功能磁共振成像(fMRI)数据中个体血氧水平依赖(BOLD)信号估计的重测信度较低,这重新引发了认知神经科学家对可能提高fMRI信度的方法的兴趣。在过去十年中,几项个体研究报告称,诸如平滑处理、运动校正和对比度选择等建模决策可能会提高BOLD信号估计的重测信度。然而,在多个大型独立样本的fMRI任务中,某些分析决策是否能提高个体和组水平的信度估计,这仍然是一个实证问题。本研究使用了三个独立样本(样本量分别为60、81、119),在两次扫描和两个阶段收集相同的任务(金钱激励延迟任务),以评估分析决策对任务fMRI数据中BOLD活动的个体(组内相关系数[ICC(3,1)])和组(杰卡德/斯皮尔曼)信度估计的影响。本研究中的分析决策分为四类:平滑核(五种选项)、运动校正(四种选项)、任务参数化(三种选项)和任务对比度(四种选项),总共240种不同的流程排列。在所有240种流程中,ICC估计的中位数始终较低,在三个样本中,ICC估计中位数的最大值为0.43 - 0.55。对中位数ICC和组相似性估计影响最大的分析决策是对比度、提示模型参数化和更大的平滑核。与使用提示相比,在对比度条件下使用 有意义地提高了组相似性和ICC估计。这种效应在提示模型参数化中最为明显;然而,信度的提高是以可解释性为代价的。本研究表明,在MID任务中,信度估计在小样本时始终较低且变化较大,较高的重测信度并不总是能提高估计的BOLD信号的可解释性。