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Updated: Aug 23, 2025
Preterm EEG: A Multimodal Neurophysiological Protocol
Published on: February 18, 2012
Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based
Shu Zhang1, Ruoyang Wang1, Junxin Wang2
1Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
Insights
This study introduces a new framework using dense individualized and common connectivity-based cortical landmarks (DICCCOL) and graph neural networks to differentiate preterm and term infant brains. The method effectively identifies brain biomarkers, improving understanding of prematurity
Area of Science:
- Neuroscience
- Medical Imaging
- Artificial Intelligence
Background:
- Preterm birth significantly impacts infant brain development and lifelong health.
- Current research often focuses on either brain function or structure separately.
- A unified approach is needed to analyze both aspects and individual differences.
Purpose of the Study:
- To develop a novel framework for differentiating preterm and term infant brains.
- To identify and characterize biomarkers associated with preterm birth effects.
- To jointly analyze brain function and structure while preserving individual variations.
Main Methods:
- Proposed a dense individualized and common connectivity-based cortical landmarks (DICCCOL)-based multi-modality graph neural networks (DM-GNN) framework.
- Utilized functional magnetic resonance imaging (fMRI) for node features and brain fiber connectivity for graph edges.
- Employed self-attention graph pooling (SAGPOOL)-based GNN for joint analysis and biomarker identification.
Main Results:
- Successfully differentiated preterm and term infant brains with 87.6% accuracy on the dHCP dataset.
- The self-attention mechanism effectively identified significant biomarkers.
- Distinguishing features between the two groups were explored and extracted.
Conclusions:
- The proposed DM-GNN framework offers a novel and uniform approach for brain disorder differentiation.
- This method effectively characterizes corresponding biomarkers for preterm birth.
- The findings facilitate neuroprotection interventions and improve outcomes for premature infants.
Abstract:
Preterm birth is a worldwide problem that affects infants throughout their lives significantly. Therefore, differentiating brain disorders, and further identifying and characterizing the corresponding biomarkers are key issues to investigate the effects of preterm birth, which facilitates the interventions for neuroprotection and improves outcomes of prematurity. Until now, many efforts have been made to study the effects of preterm birth; however, most of the studies merely focus on either functional or structural perspective. In addition, an effective framework not only jointly studies the brain function and structure at a group-level, but also retains the individual differences among the subjects. In this study, a novel dense individualized and common connectivity-based cortical landmarks (DICCCOL)-based multi-modality graph neural networks (DM-GNN) framework is proposed to differentiate preterm and term infant brains and characterize the corresponding biomarkers. This framework adopts the DICCCOL system as the initialized graph node of GNN for each subject, utilizing both functional and structural profiles and effectively retaining the individual differences. To be specific, functional magnetic resonance imaging (fMRI) of the brain provides the features for the graph nodes, and brain fiber connectivity is utilized as the structural representation of the graph edges. Self-attention graph pooling (SAGPOOL)-based GNN is then applied to jointly study the function and structure of the brain and identify the biomarkers. Our results successfully demonstrate that the proposed framework can effectively differentiate the preterm and term infant brains. Furthermore, the self-attention-based mechanism can accurately calculate the attention score and recognize the most significant biomarkers. In this study, not only 87.6% classification accuracy is observed for the developing Human Connectome Project (dHCP) dataset, but also distinguishing features are explored and extracted. Our study provides a novel and uniform framework to differentiate brain disorders and characterize the corresponding biomarkers.
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