Evaluating the effects of volume censoring on fetal functional connectivity

Advances in neuroimaging have enabled non-invasive investigation of fetal brain development in vivo. Resting-state functional magnetic resonance imaging (rs-fMRI) has provided critical insights into emerging brain networks in fetuses. However, acquiring high-quality fetal rs-fMRI remains challenging due to the unpredictable and unconstrained motion of the fetal head. Nuisance regression, where the brain signal is regressed onto translational and rotational head motion parameters, has been widely and effectively used in adults to reduce the influence of motion. However, subsequent studies have revealed that associations between head motion and large-scale brain functional connectivity (FC) persisted even after regression. In ex utero groups (e.g., newborns, toddlers, and adults), censoring high-motion volumes has shown effectiveness in mitigating such lingering impacts of head motion. While censoring high motion volumes has been utilized in fetal rs-fMRI, a systematic assessment of the effectiveness of regression and censoring high motion volumes in fetuses has not been done. Establishing the effectiveness of censoring in fetal rs-fMRI is critical to avoid possible bias in findings resulting from head motion. To address this knowledge gap, we investigated the associations between head motion and fetal rs-fMRI at different analysis scales: blood oxygenation level dependent (BOLD) time series and whole-brain FC. We used a dataset of 120 fetal scans collected from 104 healthy fetuses. We found that nuisance regression reduced the association between head motion, defined by frame-by-frame displacement (FD) of head position, and BOLD time series data in all regions of interest (ROI) encompassing the whole brain. Nuisance regression, however, was not effective in reducing the impact of head motion on FC. Fetuses' FC profiles significantly predicted average FD (r = 0.09 ± 0.08; p < 10^-3) after regression, suggesting a lingering effect of motion on whole-brain patterns. To dissociate head motion and the FC, we used volume censoring and evaluated its efficacy in correcting motion at different thresholds. We demonstrated that censored data improved resting state data's ability to predict neurobiological features, such as gestational age and sex (accuracy = 55.2 ± 2.9% with 1.5 mm vs. 44.6 ± 3.6% with no censoring). Collectively, our results highlight the importance of data censoring in reducing the lingering impact of head motion on fetal rs-fMRI, thus attenuating motion-related bias. Like older age groups such as neonates and adults, combining regression and censoring techniques is recommended for large-scale FC analysis, e.g., network-based analysis, for fetuses.