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A. Taymourtash, E. Schwartz, K. Nenning, R. Licandro, D. Sobotka, M. Diogo, D. Prayer, G. Kasprian, G. Langs:
"Evaluation of confound regression strategies for denoising in-utero resting-state functional MRI";
Poster: Organization for Human Brain Mapping (OHMB) 2020, Montreal; 2020-06-23 - 2020-07-03.

Quality control of fetal fMRI is of utmost importance since its susceptibility to motion artifacts can result in false observations. As it increasingly becomes more common to use hundreds or even thousands of scans for a single study, it is not practical to manually assess data quality, and in addition, manual assessments are biased and suffer from lack of reproducibility. Here,we present a dynamic benchmark for single-subject fMRI acquisitions that at the same time enables the comparison of nuisance regression approaches. We have also shown group-wise assessment of the residual motion using the so-called QC-FC benchmark can be deceptive for fetal fMRI studies when the re are excessive motion spikes,or generally high motion across the entire population,as it is not able to capture the irregular relations hip between fetal motion and FC.