Image Quality of MRI brain scans is strongly influenced by within scanner head movements and the resulting image artifacts alter derived measures like brain volume and cortical thickness. Automated image quality assessment is key to controlling for confounding effects of poor image quality. In this study, we systematically test for the influence of image quality on univariate statistics and machine learning classification. We analyzed group effects of sex/gender on local brain volume and made predictions of sex/gender using logistic regression, while correcting for brain size. From three large publicly available datasets, two age and sex-balanced samples were derived to test the generalizability of the effect for pooled sample sizes of n=760 and n=1094. Results of the Bonferroni corrected t-tests over 3747 gray matter features showed a strong influence of low-quality data on the ability to find significant sex/gender differences for the smaller sample. Increasing sample size and more so image quality showed a stark increase in detecting significant effects in univariate group comparisons. For the classification of sex/gender using logistic regression, both increasing sample size and image quality had a marginal effect on the Area under the Receiver Operating Characteristic Curve for most datasets and subsamples. Our results suggest a more stringent quality control for univariate approaches than for multivariate classification with a leaning towards higher quality for classical group statistics and bigger sample sizes for machine learning applications in neuroimaging.

翻译：MRI脑部扫描的图像质量受扫描仪内头部运动的显著影响，由此产生的图像伪影会改变脑体积和皮层厚度等衍生测量指标。自动化图像质量评估是控制低图像质量混杂效应的关键。本研究系统检验了图像质量对单变量统计和机器学习分类的影响。我们在校正脑尺寸的同时，分析了性别对局部脑体积的群体效应，并利用逻辑回归进行性别预测。基于三个大型公开数据集，我们构建了两个年龄与性别平衡的样本（合并样本量分别为n=760和n=1094）以检验效应的泛化性。对3747个灰质特征的Bonferroni校正t检验结果表明，低质量数据对较小样本中发现显著性别差异的能力具有强烈影响。增加样本量，尤其是提升图像质量，能显著增强单变量组间比较中检测显著效应的能力。在使用逻辑回归进行性别分类时，对于大多数数据集和子样本而言，增加样本量和提升图像质量对受试者工作特征曲线下面积仅产生边际影响。我们的研究结果表明，与多变量分类方法相比，单变量方法需要更严格的质量控制——经典群体统计更倾向于更高质量的数据，而神经影像中的机器学习应用则需要更大的样本量。