Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI Using Deep 3D Convolutional Neural Networks

摘要

Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. With the advances in functional Magnetic Resonance Imaging (fMRI), recent studies focus on brain functional connectivity and have provided new insight into development of the human brain before birth. Deep Convolutional Neural Networks (CNN) 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 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI data. We build supervised CNN to isolate variation in fMRI signals that relate to younger v.s. older fetal age groups. Sensitivity analysis is then performed to identify brain regions in which changes in BOLD signal are strongly associated with fetal brain age. Based on the analysis, 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.