Department of Statistics, University of Georgia, Athens, Georgia.
Department of Applied Statistics, Kyonggi University, Suwon, South Korea.
Hum Brain Mapp. 2019 Jan;40(1):65-79. doi: 10.1002/hbm.24355. Epub 2018 Sep 5.
Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap-based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.
将个体的统计参数图（SPM）结合起来是功能磁共振成像数据的某些类型的组级分析的目标。脑图通常通过对受试者进行简单的平均来进行组合，这使得它们容易受到异常值的影响。此外，当观察到异常值时，t 检验容易出现假阳性和假阴性。我们提出了一种用于 SPM 的正则化无监督聚合方法，以找到聚合的最佳权重，从而有助于检测和减轻异常受试者的影响。我们还提出了一种基于 bootstrap 的加权 t 检验，使用最佳权重构建对异常受试者具有鲁棒性的激活图。我们验证了所提出的聚合方法的性能，并使用模拟和真实数据示例进行了测试。结果表明，正则化聚合方法可以有效地检测异常受试者，降低其权重，并产生稳健的 SPM。
