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

Abstract

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 1.5 T. 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.

The emergence of functional architecture during early brain development

“…The emergence of long-range projection, commissural and association fibers initiates the onset of thalamocortical, as well as inter-and intrahemispheric functional connectivity. These early Present-day neuroimaging studies have detected functional brain networks from as early as 26 PCW (Ferrazzi et al, 2014;Thomason et al, 2015). These networks follow a primary-to-higher order maturational sequence, consistent with their behavioral correlates.…”

“…Furthermore, the use of sedative medication -if administered -is generally confined to infants scanned in a clinical setting and may thus introduce a bias in terms of the effect of motion, as well as differences in physiological parameters and sleep state/level of consciousness between health and disease states. Preventing motion is even more challenging in fetal imaging, where large displacements of the fetal head are not uncommon and maternal respiration and potential movement present additional challenges (Ferrazzi et al, 2014).…”

“…However, concerns have also been raised about temporal concatenation of non-consecutive time-points and how such approaches may affect the data as well as about how excessive data removal may impact correlations between timeseries Power et al, 2015). Analysis strategies designed specifically to accommodate fetal movement by addressing motion on a slice-by-slice timescale and correcting for signal variations due to changes in the position of the fetal head with respect to the receiver coil are now available and enable most (potentially all) of the acquired rs-fMRI data to be retained (Ferrazzi et al, 2014).…”

“…The first paper on the presence of functional brain networks in the neonatal brain was published in 2007 (Fransson et al, 2007) and a number of research groups have now committed to the challenging task of functional imaging in utero (Ferrazzi et al, 2014;Schöpf et al, 2012;Seshamani et al, 2014;Thomason et al, 2013). Collectively, these efforts have led to exciting insights into functional brain network architecture in the earliest phases following its emergence.…”

“…Fetal imaging is challenging due to motion of the fetus and surrounding tissues because of maternal respiration, requiring specific acquisition and analysis frameworks for this population (Ferrazzi et al, 2014;Seshamani et al, 2014). Furthermore, image resolution and signal-to-noise ratio are frequently reduced compared to that acquired in postnatal fMRI.…”