Shaw Marnie E, Strother Stephen C, Gavrilescu Maria, Podzebenko Katherine, Waites Anthony, Watson John, Anderson Jon, Jackson Graeme, Egan Gary
Howard Florey Institute, University of Melbourne, Melbourne, Australia.
Neuroimage. 2003 Jul;19(3):988-1001. doi: 10.1016/s1053-8119(03)00116-2.
This study investigated the possible benefit of subject specific optimization of preprocessing strategies in functional magnetic resonance imaging (fMRI) experiments. The optimization was performed using the data-driven performance metrics developed recently [Neuroimage 15 (2002), 747]. We applied numerous preprocessing strategies and a multivariate statistical analysis to each of the 20 subjects in our two example fMRI data sets. We found that the optimal preprocessing strategy varied, in general, from subject to subject. For example, in one data set, optimum smoothing levels varied from 16 mm (4 subjects), 10 mm (5 subjects), to no smoothing at all (1 subject). This strongly suggests that group-specific preprocessing schemes may not give optimum results. For both studies, optimizing the preprocessing for each subject resulted in an increased number of suprathresholded voxels in within-subject analyses. Furthermore, we demonstrated that we were able to aggregate the optimized data with a random effects group analysis, resulting in improved sensitivity in one study and the detection of interesting, previously undetected results in the other.
本研究调查了在功能磁共振成像（fMRI）实验中针对个体优化预处理策略可能带来的益处。优化是使用最近开发的数据驱动性能指标进行的[《神经影像学》15（2002），747]。我们对两个示例fMRI数据集中的20名受试者分别应用了多种预处理策略和多元统计分析。我们发现，一般来说，最优预处理策略因受试者而异。例如，在一个数据集中，最优平滑水平从16毫米（4名受试者）、10毫米（5名受试者）到完全不进行平滑（1名受试者）各不相同。这有力地表明，针对群体的预处理方案可能无法给出最优结果。对于这两项研究，针对每个受试者优化预处理在个体内分析中导致超阈值体素数量增加。此外，我们证明我们能够通过随机效应组分析汇总优化后的数据，在一项研究中提高了敏感性，并在另一项研究中检测到了有趣的、之前未检测到的结果。
