Autism Spectrum Disorder (ASD) is a neurodevelopmental condition associated with difficulties with social interactions, communication, and restricted or repetitive behaviors. To characterize ASD, investigators often use functional connectivity derived from resting-state functional magnetic resonance imaging of the brain. However, participants' head motion during the scanning session can induce motion artifacts. Many studies remove scans with excessive motion, which can lead to drastic reductions in sample size and introduce selection bias. To avoid such exclusions, we propose an estimand inspired by causal inference methods that quantifies the difference in average functional connectivity in autistic and non-ASD children while standardizing motion relative to the low motion distribution in scans that pass motion quality control. We introduce a nonparametric estimator for motion control, called MoCo, that uses all participants and flexibly models the impacts of motion and other relevant features using an ensemble of machine learning methods. We establish large-sample efficiency and multiple robustness of our proposed estimator. The framework is applied to estimate the difference in functional connectivity between 132 autistic and 245 non-ASD children, of which 34 and 126 pass motion quality control. MoCo appears to dramatically reduce motion artifacts relative to no participant removal, while more efficiently utilizing participant data and accounting for possible selection biases relative to the na\"ive approach with participant removal.

翻译：自闭症谱系障碍（ASD）是一种神经发育障碍，其特征表现为社交互动困难、沟通障碍以及受限或重复性行为。为描述ASD的特征，研究者常使用基于静息态功能磁共振成像的脑功能连接分析。然而，扫描过程中参与者的头部运动可能引入运动伪影。许多研究通过剔除运动过度的扫描数据来应对此问题，但这会导致样本量急剧减少并引入选择偏倚。为避免此类数据剔除，我们提出一种受因果推断方法启发的估计目标，该目标在量化自闭症儿童与非ASD儿童平均功能连接差异的同时，将运动水平标准化至通过运动质量控制的扫描数据中的低运动分布。我们提出一种名为MoCo的非参数运动控制估计器，该估计器利用全部参与者数据，并通过集成机器学习方法灵活建模运动及其他相关特征的影响。我们证明了所提估计器的大样本有效性及多重稳健性。该框架被应用于估计132名自闭症儿童与245名非ASD儿童之间的功能连接差异，其中分别有34名和126名儿童的数据通过运动质量控制。相较于不剔除参与者的方法，MoCo显著降低了运动伪影；同时相较于简单剔除参与者的传统方法，MoCo更有效地利用了参与者数据并考虑了潜在的选择偏倚。