Zhang Jian, Green Gary
School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent, United Kingdom.
Department of Psychology, University of York, York, United Kingdom.
Imaging Neurosci (Camb). 2025 Sep 8;3. doi: 10.1162/IMAG.a.137. eCollection 2025.
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.
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，某些频谱范围内的MEG扫描数据可能会出现偏差、多模态和异质性，这可能会误导传统的病例对照分析，因为这种分析要求数据在对照组内是同质且呈正态分布的。为应对这一挑战，我们提出了一种灵活的单病例与异质性对照的脑损伤检测单对K样本测试程序。新程序首先使用频域中的MEG扫描数据进行源强度成像，然后针对病例与对照之间的异常进行逐区域对比测试。这些测试的临界值通过交叉验证自动确定。我们通过相似性分析调整测试结果以考虑异质性影响。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和实际数据分析，我们表明所提出的测试在总体准确性以及适应数据非正态性和受试者异质性的能力方面优于常用的非参数方法。
