  Fetal functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful
tool for investigating brain development in utero, holding promise for
generating developmental disease biomarkers and supporting prenatal diagnosis.
However, to date its clinical applications have been limited by unpredictable
fetal and maternal motion during image acquisition. Even after spatial
realigment, these cause spurious signal fluctuations confounding measures of
functional connectivity and biasing statistical inference of relationships
between connectivity and individual differences. As there is no ground truth
for the brain's functional structure, especially before birth, quantifying the
quality of motion correction is challenging. In this paper, we propose
evaluating the efficacy of different regression based methods for removing
motion artifacts after realignment by assessing the residual relationship of
functional connectivity with estimated motion, and with the distance between
areas. Results demonstrate the sensitivity of our evaluation's criteria to
reveal the relative strengths and weaknesses among different artifact removal
methods, and underscore the need for greater care when dealing with fetal
motion.
