Brain Structural Connectivity Guided Vision Transformers for Identification of Functional Connectivity Characteristics in Preterm Neonates
Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
IEEE journal of biomedical and health informatics - 28(2024), 4 vom: 11. Apr., Seite 2223-2234 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Mao, Wei [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 11.04.2024 Date Revised 11.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/JBHI.2024.3355020 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM367759829 |
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| 520 | |a Preterm birth is the leading cause of death in children under five years old, and is associated with a wide sequence of complications in both short and long term. In view of rapid neurodevelopment during the neonatal period, preterm neonates may exhibit considerable functional alterations compared to term ones. However, the identified functional alterations in previous studies merely achieve moderate classification performance, while more accurate functional characteristics with satisfying discrimination ability for better diagnosis and therapeutic treatment is underexplored. To address this problem, we propose a novel brain structural connectivity (SC) guided Vision Transformer (SCG-ViT) to identify functional connectivity (FC) differences among three neonatal groups: preterm, preterm with early postnatal experience, and term. Particularly, inspired by the neuroscience-derived information, a novel patch token of SC/FC matrix is defined, and the SC matrix is then adopted as an effective mask into the ViT model to screen out input FC patch embeddings with weaker SC, and to focus on stronger ones for better classification and identification of FC differences among the three groups. The experimental results on multi-modal MRI data of 437 neonatal brains from publicly released Developing Human Connectome Project (dHCP) demonstrate that SCG-ViT achieves superior classification ability compared to baseline models, and successfully identifies holistically different FC patterns among the three groups. Moreover, these different FCs are significantly correlated with the differential gene expressions of the three groups. In summary, SCG-ViT provides a powerfully brain-guided pipeline of adopting large-scale and data-intensive deep learning models for medical imaging-based diagnosis | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Chen, Yuzhong |e verfasserin |4 aut | |
| 700 | 1 | |a He, Zhibin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zifan |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Zhenxiang |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Yusong |e verfasserin |4 aut | |
| 700 | 1 | |a He, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jingchao |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Weitong |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Chong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Kendrick, Keith M |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Becker, Benjamin |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tianming |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Tuo |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Xi |e verfasserin |4 aut | |
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