The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Pete & Nancy Domenici Hall, 1101 Yale Blvd. NE, Albuquerque, NM, 87106, USA.
Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85724, USA.
Brain Imaging Behav. 2018 Dec;12(6):1828-1834. doi: 10.1007/s11682-018-9836-x.
The neuroimaging community has seen a renewed interest in algorithms that provide a location-independent summary of subject-specific abnormalities (SSA) to assess individual lesion load. More recently, these methods have been extended to assess whether multiple individuals within the same cohort exhibit extrema in the same spatial location (e.g., voxel or region of interest). However, the statistical validity of this approach has not been rigorously established. The current study evaluated the potential for a spatial bias in the distribution of SSA using several common z-transformation algorithms (leave-one-out [LOO]; independent sample [IDS]; Enhanced Z-Score Microstructural Assessment of Pathology [EZ-MAP]; distribution-corrected z-scores [DisCo-Z]) using both simulated data and DTI data from 50 healthy controls. Results indicated that methods which z-transformed data based on statistical moments from a reference group (LOO, DisCo-Z) led to bias in the spatial location of extrema for the comparison group. In contrast, methods that z-transformed data using an independent third group (EZ-MAP, IDS) resulted in no spatial bias. Importantly, none of the methods exhibited bias when results were summed across all individual elements. The spatial bias is primarily driven by sampling error, in which differences in the mean and standard deviation of the untransformed data have a higher probability of producing extrema in the same spatial location for the comparison but not reference group. In conclusion, evaluating SSA overlap within cohorts should be either be avoided in deference to established group-wise comparisons or performed only when data is available from an independent third group.
神经影像学领域重新燃起了对算法的兴趣，这些算法提供了一种与位置无关的主题特异性异常（SSA）摘要，以评估个体病变负荷。最近，这些方法已扩展到评估同一队列中是否有多个个体在相同的空间位置出现极值（例如，体素或感兴趣区域）。然而，这种方法的统计有效性尚未得到严格确立。本研究使用几种常见的 z 变换算法（留一法[LOO]；独立样本[IDS]；增强 Z 分数病理微观结构评估[EZ-MAP]；分布校正 z 分数[DisCo-Z]），通过模拟数据和来自 50 名健康对照者的 DTI 数据，评估了 SSA 分布中空间偏差的可能性。结果表明，基于参考组统计矩进行 z 变换数据的方法（LOO、DisCo-Z）导致比较组极值的空间位置出现偏差。相比之下，使用独立的第三组对数据进行 z 变换的方法（EZ-MAP、IDS）则没有空间偏差。重要的是，当对所有个体元素的结果进行求和时，没有任何方法表现出偏差。这种空间偏差主要是由抽样误差驱动的，其中未变换数据的均值和标准差的差异更有可能使比较组而不是参考组在同一空间位置产生极值。总之，应该避免在参考组之间进行 SSA 重叠的评估，或者仅在可以从独立的第三组获得数据时才进行。
