Quantifying Residual Motion Artifacts in Fetal fMRI Data. 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 brainu0027s 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 evaluationu0027s 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.

Just a very expensive breathing training? Risk of respiratory artefacts in functional connectivitybased realtime fMRI neurofeedback Abstract   Realtime functional magnetic resonance imaging neurofeedback (rtfMRI NFB) is a promising method for targeted regulation of pathological brain processes in mental disorders. But most NFB approaches so far have used relatively restricted regional activation as a target, which might not address the complexity of the underlying network changes. Aiming towards advancing novel treatment tools for disorders like schizophrenia, we developed a largescale network functional connectivitybased rtfMRI NFB approach targeting dorsolateral prefrontal cortex and anterior cingulate cortex connectivity with the striatum.  In a doubleblind randomized yokecontrolled singlesession feasibility study with Nxe2x80xafxe2x80xaf38 healthy controls, we identified strong associations between our connectivity estimates and physiological parameters reflecting the rate and regularity of breathing. These undesired artefacts are especially detrimental in rtfMRI NFB, where the same data serves as an online feedback signal and offline analysis target.  To evaluate ways to control for the identified respiratory artefacts, we compared modelbased physiological nuisance regression and global signal regression (GSR) and found that GSR was the most effective method in our data.  Our results strongly emphasize the need to control for physiological artefacts in connectivitybased rtfMRI NFB approaches and suggest that GSR might be a useful method for online data correction for respiratory artefacts.