DFC-Igloo: A dynamic functional connectome learning framework for identifying neurodevelopmental biomarkers in very
Junqi Wang1, Hailong Li2, Kim M Cecil3
1Imaging research center, Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Insights
Researchers developed a new method to analyze brain connectivity in very preterm infants. This approach identifies early biomarkers for cognitive and motor development, aiding timely intervention for neurodevelopmental impairments.
Area of Science:
- Neuroscience
- Developmental Biology
- Medical Imaging
Background:
- Very preterm infants face risks of neurodevelopmental impairments.
- Early detection of prognostic biomarkers is crucial for timely intervention.
- Previous studies were limited by small sample sizes and inefficient functional connectome (FC) construction algorithms for noisy neonatal data.
Purpose of the Study:
- To explore functional biomarkers for very preterm infants' future cognitive and motor development using resting-state fMRI.
- To propose an enhanced functional connectome construction algorithm to address limitations in prior research.
- To apply the new algorithm to a large cohort of very preterm infants to identify multi-level neurodevelopmental biomarkers.
Main Methods:
- Proposed an advanced dynamic FC (dFC) learning model, dFC-Igloo, leveraging structural connectome (SC) information.
- Applied graph signal smoothness to iteratively refine dFC estimations using both FC and SC.
- Evaluated the model on artificial graphs and simulated graph signals before application to a human cohort.
Main Results:
- The dFC-Igloo model demonstrated robust recovery of ground truth graphs across various noise levels.
- Applied to a cohort of very preterm infants, the model generated enhanced dFC for each infant.
- Identified neurodevelopmental biomarkers at connectome-wide, regional, and subnetwork levels based on improved dFC.
Conclusions:
- The identified biomarkers correlate with cognitive and motor developmental outcomes in very preterm infants.
- These findings provide insights into early brain development and potential neurodevelopmental challenges.
- The enhanced dFC construction algorithm offers a promising tool for neonatal neuroimaging research.
Background And Objective:
Very preterm infants are susceptible to neurodevelopmental impairments, necessitating early detection of prognostic biomarkers for timely intervention. The study aims to explore possible functional biomarkers for very preterm infants at born that relate to their future cognitive and motor development using resting-state fMRI. Prior studies are limited by the sample size and suffer from efficient functional connectome (FC) construction algorithms that can handle the noisy data contained in neonatal time series, leading to equivocal findings. Therefore, we first propose an enhanced functional connectome construction algorithm as a prerequisite step. We then apply the new FC construction algorithm to our large prospective very preterm cohort to explore multi-level neurodevelopmental biomarkers.
Methods:
There exists an intrinsic relationship between the structural connectome (SC) and FC, with a notable coupling between the two. This observation implies a putative property of graph signal smoothness on the SC as well. Yet, this property has not been fully exploited for constructing intrinsic dFC. In this study, we proposed an advanced dynamic FC (dFC) learning model, dFC-Igloo, which leveraged SC information to iteratively refine dFC estimations by applying graph signal smoothness to both FC and SC. The model was evaluated on artificial small-world graphs and simulated graph signals.
Results:
The proposed model achieved the best and most robust recovery of the ground truth graph across different noise levels and simulated SC pairs from the simulation. The model was further applied to a cohort of very preterm infants from five Neonatal Intensive Care Units, where an enhanced dFC was obtained for each infant. Based on the improved dFC, we identified neurodevelopmental biomarkers for neonates across connectome-wide, regional, and subnetwork scales.
Conclusion:
The identified markers correlate with cognitive and motor developmental outcomes, offering insights into early brain development and potential neurodevelopmental challenges.
