  Understanding human fetal neurodevelopment is of great clinical importance as
abnormal development is linked to adverse neuropsychiatric outcomes after
birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have
provided new insight into development of the human brain before birth, but
these studies have predominately focused on brain functional connectivity (i.e.
Fisher z-score), which requires manual processing steps for feature extraction
from fMRI images. Deep learning approaches (i.e., Convolutional Neural
Networks) have achieved remarkable success on learning directly from image
data, yet have not been applied on fetal fMRI for understanding fetal
neurodevelopment. Here, we bridge this gap by applying a novel application of
deep 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI
data. Specifically, we test a supervised CNN framework as a data-driven
approach to isolate variation in fMRI signals that relate to younger v.s. older
fetal age groups. Based on the learned CNN, we further perform sensitivity
analysis to identify brain regions in which changes in BOLD signal are strongly
associated with fetal brain age. The findings demonstrate that deep CNNs are a
promising approach for identifying spontaneous functional patterns in fetal
brain activity that discriminate age groups. Further, we discovered that
regions that most strongly differentiate groups are largely bilateral, share
similar distribution in older and younger age groups, and are areas of
heightened metabolic activity in early human development.
