Head motion remains a challenging confound in functional magnetic resonance imaging (fMRI) studies of both children and adults. Most pediatric neuroimaging labs have developed experience-based, child-friendly standards concerning e.g. the maximum length of a session or the time between mock scanner training and actual scanning. However, it is unclear which factors of child-friendly neuroimaging approaches are effective in reducing head motion. Here, we investigate three main factors including (i) time lag of mock scanner training to the actual scan, (ii) prior scan time, and (iii) task engagement in a dataset of 77 children (aged 6–13) and 64 adults (aged 18–35) using a multilevel modeling approach. In children, distributing fMRI data acquisition across multiple same-day sessions reduces head motion. In adults, motion is reduced after inside-scanner breaks. Despite these positive effects of splitting up data acquisition, motion increases over the course of a study as well as over the course of a run in both children and adults. Our results suggest that splitting up fMRI data acquisition is an effective tool to reduce head motion in general. At the same time, different ways of splitting up data acquisition benefit children and adults.

在儿童和成人的功能磁共振成像（fMRI）研究中，头部运动仍然是一个充满挑战的困惑。多数儿科神经影像实验室已经开发出基于经验的，对儿童友好的标准，例如关于课程的最大长度或模拟扫描仪培训与实际扫描之间的时间。然而，尚不清楚儿童友好型神经影像学方法的哪些因素可有效减少头部运动。在这里，我们调查了三个主要因素，其中包括（i）模拟扫描仪训练到实际扫描的时间滞后，（ii）先前的扫描时间，以及（iii）在77名儿童（6-13岁）和64位成人的数据集中进行任务（18-35岁）使用多层次建模方法。对于儿童，将fMRI数据采集分布在多个当日会话中可减少头部运动。在成年人中 内部扫描仪中断后，运动会减少。尽管拆分数据采集具有这些积极作用，但是在研究过程中以及儿童和成人的跑步过程中，运动都会增加。我们的研究结果表明，拆分fMRI数据采集通常是减少头部运动的有效工具。同时，拆分数据采集的不同方式也对儿童和成年人有利。