Optimizing multivariate pattern classification in rapid event-related designs
Optimizing multivariate pattern classification in rapid event-related designs
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DOI：
10.1016/j.jneumeth.2023.109808
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发表时间：
2023-02
影响因子：
3
通讯作者：
Daniel A. Stehr;Javier O. Garcia;John A. Pyles;E. Grossman
中科院分区：
文献类型：
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作者：
Daniel A. Stehr;Javier O. Garcia;John A. Pyles;E. Grossman
多元模式分析（MVPA或模式解码）作为一种利用功能磁共振成像（fMRI）数据进行调查的敏感分析工具受到了广泛关注。然而，随着MVPA的引入，研究人员面临着大量的方法选择，迄今为止，很少有研究从控制数据集的优势出发，脱离具体的实验假设，提供指导。我们研究了四个数据处理步骤对支持向量机（SVM）分类性能的影响，目的是在常见噪声源存在的情况下最大化信息捕获。这四种技术包括：试验平均（根据单独的试验估计与基于条件的平均值进行分类）、运行内均值中心（是否对数据进行中心化）、成本选择方法（使用固定或调整的成本值）和运动相关的去噪方法（比较不去噪与捕获运动相关参考信号的各种讨厌的回归）。这些方法的影响在两个对照roi的真实fMRI数据上进行了评估，以及在由精心控制的体素和试验级噪声成分构建的模拟模式数据上进行了评估。结果我们发现，在真实数据集和模拟数据集上，通过运行平均试验和均值定心，分类性能都有显著提高。当在每次运行的条件下平均试验时，我们注意到SVM分类精度的受试者间可变性同时增加，我们将其归因于用于评估分类器预测误差的测试集的减小。因此，我们提出了一种混合技术，即每次运行平均随机抽样的试验子集，并证明它有助于减轻通过平均和丢失测试集中的样本来提高信噪比之间的权衡。与现有方法的比较虽然少数实证研究采用了基于运行的试验平均、均值中心或它们的组合，但这些研究都没有理论依据或使用控制roi进行严格的测试。因此，我们希望本研究能够为希望优化模式解码而不存在引入虚假结果风险的研究人员提供实用指南。
BackgroundMultivariate pattern analysis (MVPA or pattern decoding) has attracted considerable attention as a sensitive analytic tool for investigations using functional magnetic resonance imaging (fMRI) data. With the introduction of MVPA, however, has come a proliferation of methodological choices confronting the researcher, with few studies to date offering guidance from the vantage point of controlled datasets detached from specific experimental hypotheses.New methodWe investigated the impact of four data processing steps on support vector machine (SVM) classification performance aimed at maximizing information capture in the presence of common noise sources. The four techniques included: trial averaging (classifying on separate trial estimates versus condition-based averages), within-run mean centering (centering the data or not), method of cost selection (using a fixed or tuned cost value), and motion-related denoising approach (comparing no denoising versus a variety of nuisance regressions capturing motion-related reference signals). The impact of these approaches was evaluated on real fMRI data from two control ROIs, as well as on simulated pattern data constructed with carefully controlled voxel- and trial-level noise components.ResultsWe find significant improvements in classification performance across both real and simulated datasets with run-wise trial averaging and mean centering. When averaging trials within conditions of each run, we note a simultaneous increase in the between-subject variability of SVM classification accuracies which we attribute to the reduced size of the test set used to assess the classifier’s prediction error. Therefore, we propose a hybrid technique whereby randomly sampled subsets of trials are averaged per run and demonstrate that it helps mitigate the tradeoff between improving signal-to-noise ratio by averaging and losing exemplars in the test set.Comparison with existing methodsThough a handful of empirical studies have employed run-based trial averaging, mean centering, or their combination, such studies have done so without theoretical justification or rigorous testing using control ROIs.ConclusionsTherefore, we intend this study to serve as a practical guide for researchers wishing to optimize pattern decoding without risk of introducing spurious results.