Resting State fMRI in the moving fetus: A robust framework for motion, bias field and spin history correction

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2014-11, Vol.101, p.555-568
• Main Authors: Ferrazzi, Giulio, Kuklisova Murgasova, Maria, Arichi, Tomoki, Malamateniou, Christina, Fox, Matthew J., Makropoulos, Antonios, Allsop, Joanna, Rutherford, Mary, Malik, Shaihan, Aljabar, Paul, Hajnal, Joseph V.
• Format: Article
• Language: English
• Subjects: Acquisitions & mergers; Age; Bias field correction; Biomedical research; Brain - embryology; Brain Mapping - methods; Brain Mapping - standards; Brain research; Female; Fetal fMRI; Fetus - physiology; Fetuses; Gestational Age; Humans; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Medical research; Motion; Nerve Net - physiology; Pregnancy; Pregnancy Trimester, Third; Prenatal Diagnosis; Registration; Resting State Networks; Scanners; Scattered interpolation; Slice to volume registration; Spin history correction; Studies; Time series
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.06.074

There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies. •We propose a framework for performing Resting State fMRI in the moving fetus.•We correct for fetal motion on a slice-by-slice timescale.•We correct fetal fMRI data for inhomogeneity of the radio-frequency field.•We propose a method to correct fetal fMRI data for spin-history saturation effects.•We extract 6 networks that are consistent with previously published studies.