Abnormal hole detection in brain connectivity by kernel density of persistence diagram and Hodge Laplacian

Hyekyoung Lee, Moo K. Chung, Hyejin Kang, Hong Yoon Choi, Yu Kyeong Kim, Dong Soo Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

2 Citations (Scopus)

Abstract

Community and rich-club detection are a well-known method to extract functionally specialized subnetwork in brain connectivity analysis. They find densely connected subregions with large modularity or high degree in brain connectivity studies. However, densely connected nodes are not the only representation of network shape. In this study, we propose a new method to extract abnormal holes, which are another representation of network shape. While densely connected component characterizes network's efficiency, abnormal holes characterize inefficiency. The proposed method differs from the existing hole detection in two respects. One is to use Hodge Laplacian to obtain a harmonic hole in the linear combination of edges, rather than a subset of edges. The other is to use the kernel density estimation of persistence diagram of random networks to determine the significance of a hole, rather than using the persistence of a hole. We applied the proposed method to find the abnormality of metabolic connectivity in the FDG PET data of ADNI. We found that, as AD severely progressed, the brain network had more abnormal holes. The localized holes showed how inefficient the structure of brain network became as the disease progressed.

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages20-23
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - 23 May 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: 4 Apr 20187 Apr 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

Other15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/04/187/04/18

Fingerprint

Brain
Network components
Spatial Analysis

Keywords

  • Alzheimer's disease
  • Brain connectivity
  • Hodge Laplacian
  • Hole
  • Kernel density estimation

Cite this

Lee, H., Chung, M. K., Kang, H., Choi, H. Y., Kim, Y. K., & Lee, D. S. (2018). Abnormal hole detection in brain connectivity by kernel density of persistence diagram and Hodge Laplacian. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018 (pp. 20-23). (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2018.8363514
Lee, Hyekyoung ; Chung, Moo K. ; Kang, Hyejin ; Choi, Hong Yoon ; Kim, Yu Kyeong ; Lee, Dong Soo. / Abnormal hole detection in brain connectivity by kernel density of persistence diagram and Hodge Laplacian. 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. pp. 20-23 (Proceedings - International Symposium on Biomedical Imaging).
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abstract = "Community and rich-club detection are a well-known method to extract functionally specialized subnetwork in brain connectivity analysis. They find densely connected subregions with large modularity or high degree in brain connectivity studies. However, densely connected nodes are not the only representation of network shape. In this study, we propose a new method to extract abnormal holes, which are another representation of network shape. While densely connected component characterizes network's efficiency, abnormal holes characterize inefficiency. The proposed method differs from the existing hole detection in two respects. One is to use Hodge Laplacian to obtain a harmonic hole in the linear combination of edges, rather than a subset of edges. The other is to use the kernel density estimation of persistence diagram of random networks to determine the significance of a hole, rather than using the persistence of a hole. We applied the proposed method to find the abnormality of metabolic connectivity in the FDG PET data of ADNI. We found that, as AD severely progressed, the brain network had more abnormal holes. The localized holes showed how inefficient the structure of brain network became as the disease progressed.",
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Lee, H, Chung, MK, Kang, H, Choi, HY, Kim, YK & Lee, DS 2018, Abnormal hole detection in brain connectivity by kernel density of persistence diagram and Hodge Laplacian. in 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, IEEE Computer Society, pp. 20-23, 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018, Washington, United States, 4/04/18. https://doi.org/10.1109/ISBI.2018.8363514

Abnormal hole detection in brain connectivity by kernel density of persistence diagram and Hodge Laplacian. / Lee, Hyekyoung; Chung, Moo K.; Kang, Hyejin; Choi, Hong Yoon; Kim, Yu Kyeong; Lee, Dong Soo.

2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society, 2018. p. 20-23 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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Lee H, Chung MK, Kang H, Choi HY, Kim YK, Lee DS. Abnormal hole detection in brain connectivity by kernel density of persistence diagram and Hodge Laplacian. In 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018. IEEE Computer Society. 2018. p. 20-23. (Proceedings - International Symposium on Biomedical Imaging). https://doi.org/10.1109/ISBI.2018.8363514