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Exploring Gyral Patterns of Infant Cortical Folding Based on Multi-view Curvature Information

  • Dingna Duan
  • Shunren Xia
  • Yu Meng
  • Li Wang
  • Weili Lin
  • John H. Gilmore
  • Dinggang Shen
  • Gang LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

The human cortical folding is intriguingly complex in its variability and regularity across individuals. Exploring the principal patterns of cortical folding is of great importance for neuroimaging research. The term-born neonates with minimum exposure to the complicated environments are the ideal candidates to mine the postnatal origins of principal cortical folding patterns. In this work, we propose a novel framework to study the gyral patterns of neonatal cortical folding. Specifically, first, we leverage multi-view curvature-derived features to comprehensively characterize the complex and multi-scale nature of cortical folding. Second, for each feature, we build a dissimilarity matrix for measuring the difference of cortical folding between any pair of subjects. Then, we convert these dissimilarity matrices as similarity matrices, and nonlinearly fuse them into a single matrix via a similarity network fusion method. Finally, we apply a hierarchical affinity propagation clustering approach to group subjects into several clusters based on the fused similarity matrix. The proposed framework is generic and can be applied to any cortical region, or even the whole cortical surface. Experiments are carried out on a large dataset with 600+ term-born neonates to mine the principal folding patterns of three representative gyral regions.

Keywords

Infant cortical folding Gyral pattern Curvature information 

Notes

Acknowledgements

This work was supported in part by NIH grants (MH100217, MH108914, MH107815, MH109773, and MH110274), and National Key Research and Development Program of China (No. 2016YFC1306600).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dingna Duan
    • 1
    • 2
  • Shunren Xia
    • 1
  • Yu Meng
    • 2
  • Li Wang
    • 2
  • Weili Lin
    • 2
  • John H. Gilmore
    • 3
  • Dinggang Shen
    • 2
  • Gang Li
    • 2
    Email author
  1. 1.Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang UniversityHangzhouChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of PsychiatryUniversity of North Carolina at Chapel HillChapel HillUSA

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