The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics
The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics
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DOI：
10.1006/nimg.2002.1300
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发表时间：
2003-01-01
期刊：
影响因子：
5.7
通讯作者：
Strother, S
中科院分区：
文献类型：
--
作者：
LaConte, S;Anderson, J;Strother, S
这项工作提出了一种基于模拟的接收者操作特征（ROC）分析的替代方法，用于评估功能性磁共振成像（fMRI）数据分析方法。具体而言，我们应用快速发展的非参数预测、激活、影响和可重复性重采样（NPAIRS）框架，以获得基于交叉验证的不同复杂程度模型的预测准确性和整体可重复性的模型性能估计。我们依赖于分析链元模型的概念，其中预处理步骤的所有参数以及最终的统计模型都被视为估计的模型参数。然后，我们的ROC类似物包括将预测与可重复性结果绘制为竞争元模型的模型复杂度曲线。两个理论基础对于利用这种新的验证技术至关重要。首先，我们探讨了全局信噪比与我们先前推导的可重复性估计之间的关系。其次，我们将我们在预测与可重复性空间中的模型复杂度曲线视为反映经典的偏差 - 方差权衡。在考虑的特定分析链中，我们发现对齐对性能指标影响较小，时间去趋势有一定益处，而空间平滑的改进最大。（C）2002年爱思唯尔科学（美国）
This work proposes an alternative to simulation-based receiver operating characteristic (ROC) analysis for assessment of fMRI data analysis methodologies. Specifically, we apply the rapidly developing nonparametric prediction, activation, influence, and reproducibility resampling (NPAIRS) framework to obtain cross-validation-based model performance estimates of prediction accuracy and global reproducibility for various degrees of model complexity. We rely on the concept of an analysis chain meta-model in which all parameters of the preprocessing steps along with the final statistical model are treated as estimated model parameters. Our ROC analog, then, consists of plotting prediction vs. reproducibility results as curves of model complexity for competing meta-models. Two theoretical underpinnings are crucial to utilizing this new validation technique. First, we explore the relationship between global signal-to-noise and our reproducibility estimates as derived previously. Second, we submit our model complexity curves in the prediction versus reproducibility space as reflecting classic bias-variance tradeoffs. Among the particular analysis chains considered, we found little impact in performance metrics with alignment, some benefit with temporal detrending, and greatest improvement with spatial smoothing. (C) 2002 Elsevier Science (USA).