The quantitative evaluation of functional neuroimaging experiments: The NPAIRS data analysis framework
The quantitative evaluation of functional neuroimaging experiments: The NPAIRS data analysis framework
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DOI：
10.1006/nimg.2001.1034
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发表时间：
2002-04-01
期刊：
影响因子：
5.7
通讯作者：
Rottenberg, D
中科院分区：
文献类型：
--
作者：
Strothert, SC;Anderson, J;Rottenberg, D
我们介绍了一个数据分析框架和性能指标，用于评估和优化激活任务，实验设计，方法选择和工具的数据采集，预处理，数据分析和提取统计参数图（SPM）之间的相互作用。我们的NPAIRS（非参数预测、激活、影响和再现性恢复）框架通过使用真实的PET和fMRI数据集来检查预测准确性和与可再现SPM相关的信噪比（SNR）之间的关系，提供了模拟和ROC曲线的替代方案。使用交叉验证结果，我们绘制了实验设计变量的训练测试集预测（例如，脑状态标签）与相关SPM的再现性SNR度量。我们证明了该框架在从[O-15]水PET研究中获得的广泛性能指标中的实用性，这些研究包括12个年龄和性别匹配的数据集，这些数据集执行不同的运动任务（8个受试者/集）。对于12个数据集，我们应用NPAIRS与单变量和多变量数据分析方法：（1）证明这个框架可以用于从任何数据分析方法获得可重复的SPM，在一个共同的Z分数量表（rSPM{Z}）;（2）证明（rSPM{Z}）图像的直方图可以被建模为依赖于数据分析的噪声分布和依赖于任务的噪声分布之和，高斯信号分布，与我们的再现性性能指标单调缩放;（3）探索预测和再现性性能指标之间的关系，重点是灵活的多变量模型的偏差-方差权衡;（4）测量广泛的再现性SNR和显着影响个体受试者。一篇配套论文描述了这12个数据集中的4个数据集的学习曲线，其中描述了一种替代的互信息预测度量和NPAIRS再现性，作为2到18个受试者的训练集大小的函数。我们提出的NPAIRS框架作为一个验证工具，用于测试和优化功能神经影像学的方法选择和工具。(C)2002 Elsevier Science（美国）。
We introduce a data-analysis framework and performance metrics for evaluating and optimizing the interaction between activation tasks, experimental designs, and the methodological choices and tools for data acquisition, preprocessing, data analysis, and extraction of statistical parametric maps (SPMs). Our NPAIRS (nonparametric prediction, activation, influence, and reproducibility resampling) framework provides an alternative to simulations and ROC curves by using real PET and fMRI data sets to examine the relationship between prediction accuracy and the signal-to-noise ratios (SNRs) associated with reproducible SPMs. Using cross-validation resampling we plot training-test set predictions of the experimental design variables (e.g., brain-state labels) versus reproducibility SNR metrics for the associated SPMs. We demonstrate the utility of this framework across the wide range of performance metrics obtained from [O-15]water PET studies of 12 age- and sex-matched data sets performing different motor tasks (8 subjects/set). For the 12 data sets we apply NPAIRS with both univariate and multivariate data-analysis approaches to: (1) demonstrate that this framework may be used to obtain reproducible SPMs from any data-analysis approach on a common Z-score scale (rSPM{Z}); (2) demonstrate that the histogram of a (rSPM{Z}) image may be modeled as the sum of a data-analysis-dependent noise distribution and a task-dependent, Gaussian signal distribution that scales monotonically with our reproducibility performance metric; (3) explore the relation between prediction and reproducibility performance metrics with an emphasis on bias-variance tradeoffs for flexible, multivariate models; and (4) measure the broad range of reproducibility SNRs and the significant influence of individual subjects. A companion paper describes learning curves for four of these 12 data sets, which describe an alternative mutual-information prediction metric and NPAIRS reproducibility as a function of training-set sizes from 2 to 18 subjects. We propose the NPAIRS framework as a validation tool for testing and optimizing methodological choices and tools in functional neuroimaging. (C) 2002 Elsevier Science (USA).