Magnetoencephalography (MEG) scanner has been shown to be more accurate than other medical devices in detecting mild traumatic brain injury (mTBI). However, MEG scan data in certain spectrum ranges can be skewed, multimodal and heterogeneous which can mislead the conventional case-control analysis that requires the data to be homogeneous and normally distributed within the control group. To meet this challenge, we propose a flexible one-vs-K-sample testing procedure for detecting brain injury for a single-case versus heterogeneous controls. The new procedure begins with source magnitude imaging using MEG scan data in frequency domain, followed by region-wise contrast tests for abnormality between the case and controls. The critical values for these tests are automatically determined by cross-validation. We adjust the testing results for heterogeneity effects by similarity analysis. An asymptotic theory is established for the proposed test statistic. By simulated and real data analyses in the context of neurotrauma, we show that the proposed test outperforms commonly used nonparametric methods in terms of overall accuracy and ability in accommodating data non-normality and subject-heterogeneity.

翻译：脑磁图（MEG）扫描仪已被证明在检测轻度创伤性脑损伤（mTBI）方面比其他医疗设备更准确。然而，特定频谱范围内的MEG扫描数据可能存在偏态、多峰和异质性，这会误导传统的病例-对照分析，因为该方法要求对照组内数据具有同质性且服从正态分布。为应对这一挑战，我们提出了一种灵活的单样本对多样本检验程序，用于检测单一个体相对于异质性对照组的脑损伤。新方法首先利用频域MEG扫描数据进行源幅度成像，随后在区域层面进行病例与对照组间的异常对比检验。这些检验的临界值通过交叉验证自动确定。我们通过相似性分析对异质性效应引起的检验结果进行校正。为所提出的检验统计量建立了渐近理论。通过在神经创伤背景下的模拟与真实数据分析，我们证明所提出的检验在整体准确性、以及对数据非正态性和个体异质性的适应能力方面，优于常用的非参数方法。