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 participants with excessive motion, and then estimate the effect of diagnosis on functional connectivity using linear regression. However, participant exclusions and linearity assumptions can cause biases. We propose an estimand 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, respectively. MoCo appears to dramatically reduce motion artifacts compared to a standard approach with no participant removal, while more efficiently utilizing participant data and accounting for possible selection biases compared to participant removal.

翻译：自闭症谱系障碍（ASD）是一种神经发育性疾病，与社交互动困难、沟通障碍以及受限或重复行为相关。为表征ASD，研究者常利用基于静息态功能磁共振成像的大脑功能连接。然而，扫描过程中被试的头动可能引发运动伪影。许多研究剔除运动过度的被试，随后通过线性回归估计诊断对功能连接的影响。但被试排除与线性假设可能导致偏差。我们提出一种估计量，在将运动相对于通过运动质量控制的扫描中低运动分布进行标准化后，量化自闭症与非ASD儿童平均功能连接的差异。我们引入一种非参数运动控制估计器MoCo，该估计器利用全体被试，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。我们建立了所提估计量的大样本有效性与多重稳健性。该框架被应用于估计132名自闭症与245名非ASD儿童（其中分别有34人与126人通过运动质量控制）之间的功能连接差异。与无被试剔除的标准方法相比，MoCo似乎显著减少了运动伪影，同时在更高效利用被试数据并考虑可能选择偏差方面优于被试剔除方法。