Gardner Margaret, Shinohara Russell T, Bethlehem Richard A I, Romero-Garcia Rafael, Warrier Varun, Dorfschmidt Lena, Shanmugan Sheila, Thompson Paul, Seidlitz Jakob, Alexander-Bloch Aaron F, Chen Andrew A
Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA.
Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
bioRxiv. 2024 Jul 30:2024.06.21.599875. doi: 10.1101/2024.06.21.599875.
Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.
近期的研究利用大规模数据集和先进的归一化方法构建神经解剖特征的规范模型，并对个体形态进行基准测试。然而，当前的归一化工具并未保留包括性别和年龄在内的生物协变量对特征方差的影响；这种不足可能会在规范分数中引入误差，尤其是当这些因素在各站点分布不均时。在此，我们引入了流行的ComBat归一化方法的新扩展ComBatLS，它能保留特征位置和尺度上的生物方差。我们使用英国生物银行的数据表明，当将受试者分配到性别不均衡的合成“站点”时，ComBatLS比其他ComBat方法能更稳健地复制个体的规范分数。此外，我们证明与传统方法相比，ComBatLS显著减少了规范分数中的性别偏差。最后，我们表明ComBatLS成功地对来自50多项研究的联盟数据进行了归一化处理。ComBatLS的R语言实现可在https://github.com/andy1764/ComBatFamily获取。
