Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks
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 interv...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.951508/full |
| _version_ | 1811339837229236224 |
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| author | Shu Zhang Ruoyang Wang Junxin Wang Zhibin He Jinru Wu Yanqing Kang Yin Zhang Huan Gao Xintao Hu Tuo Zhang |
| author_facet | Shu Zhang Ruoyang Wang Junxin Wang Zhibin He Jinru Wu Yanqing Kang Yin Zhang Huan Gao Xintao Hu Tuo Zhang |
| author_sort | Shu Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T18:33:30Z |
| format | Article |
| id | doaj.art-ae5074d9fa5547dbbf1d8cf499f8ca10 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-13T18:33:30Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-ae5074d9fa5547dbbf1d8cf499f8ca102022-12-22T02:35:00ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-10-011610.3389/fnins.2022.951508951508Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networksShu Zhang0Ruoyang Wang1Junxin Wang2Zhibin He3Jinru Wu4Yanqing Kang5Yin Zhang6Huan Gao7Xintao Hu8Tuo Zhang9Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaCenter for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi'an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi'an, ChinaCenter for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaCenter for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi'an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi'an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi'an, ChinaSchool of Automation, Northwestern Polytechnical University, Xi'an, ChinaPreterm 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.https://www.frontiersin.org/articles/10.3389/fnins.2022.951508/fullpreterm infant brainDICCCOLmulti-modalityGNNbiomarker |
| spellingShingle | Shu Zhang Ruoyang Wang Junxin Wang Zhibin He Jinru Wu Yanqing Kang Yin Zhang Huan Gao Xintao Hu Tuo Zhang Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks Frontiers in Neuroscience preterm infant brain DICCCOL multi-modality GNN biomarker |
| title | Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks |
| title_full | Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks |
| title_fullStr | Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks |
| title_full_unstemmed | Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks |
| title_short | Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks |
| title_sort | differentiate preterm and term infant brains and characterize the corresponding biomarkers via dicccol based multi modality graph neural networks |
| topic | preterm infant brain DICCCOL multi-modality GNN biomarker |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.951508/full |
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