In this repository, you can find the codes needed to reproduce the results of the paper:
The Maternal-Fetal Neurodevelopmental Groundings of Preterm Birth Risk.
Data are available upon request to dellarosa.pasquale@hsr.it
Background: Altered neurodevelopment is a major clinical sequela of Preterm Birth (PTB) being currently unexplored in-utero.
Aims: To study the association between fetal brain functional (FbF) connectivity, using resting-state functional magnetic resonance (rs-fMRI), PTB risk profiles and gestational age (GA).
Study Design: Prospective single-centre cohort study.
Subjects: A sample of 31 singleton pregnancies at 28-34 weeks assigned to a low PTB risk (LR) (n=19) or high PTB risk (HR) (n=12) group based on a) the Maternal Frailty Inventory (MaFra) for PTB risk; b) a case-specific PTB risk gradient.
Methods: Fetal brain rs-fMRI was performed on 1.5T MRI scanner. First, directed causal relations representing FbF connectivity measurements were estimated using the Greedy Equivalence Search (GES) algorithm and HR vs. LR differences tested with a novel ad-hoc developed Monte Carlo permutation test. Second, a MaFra-only random forest (RF) was compared against a MaFra-Neuro RF, trained also with the inclusion of the most important FbF connections. Third, correlation and regression analyses were performed between MaFra-Neuro class probabilities and i) the GA at birth; ii) PTB risk gradient and iii) PTB below 37 weeks.
Results: First, fewer FbF connections were evident in the HR group. Second, the MaFra-Neuro RF improved PTB risk prediction. Third, MaFra-Neuro class probabilities showed a significant association with: i) GA at birth; ii) PTB risk gradient and iii) PTB below 37 weeks.
Conclusion: FbF connectivity is a novel promising predictor of PTB and related risk profiles as well as of gestational age at birth, potentially linking different PTB phenotypes.
Keywords: preterm birth, preterm birth risk, fetal neurodevelopment, fMRI, fetal brain functional connectivity, machine learning.