A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease

Maryam Ghanbari; Li Ming Hsu; Zhen Zhou; Amir Ghanbari; Zhanhao Mo; Pew Thian Yap; Han Zhang; Dinggang Shen

doi:10.1007/978-3-030-59728-3_1

A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease

Maryam Ghanbari, Li Ming Hsu, Zhen Zhou, Amir Ghanbari, Zhanhao Mo, Pew Thian Yap, Han Zhang, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Graph theory has been used extensively to investigate information exchange efficiency among brain regions represented as graph nodes. In this work, we propose a new metric to measure how the brain network is robust or resilient to any attack on its nodes and edges. The metric measures redundancy in the sense that it calculates the minimum number of independent, not necessarily shortest, paths between every pair of nodes. We adopt this metric for characterizing (i) the redundancy of time-varying brain networks, i.e., chronnectomes, computed along the progression of Alzheimer’s disease (AD), including early mild cognitive impairment (EMCI), and (ii) changes in progressive MCI compared to stable MCI by calculating the probabilities of having at least 2 (or 3) independent paths between every pair of brain regions in a short period of time. Finally, we design a learning-based early AD detection framework, coined “REdundancy Analysis of Dynamic functional connectivity for Disease Diagnosis (READ³)”, and show its superiority over other AD early detection methods. With the ability to measure dynamic resilience and robustness of brain networks, the metric is complementary to the commonly used “cost-efficiency” in brain network analysis.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
Editors	Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	3-12
Number of pages	10
ISBN (Print)	9783030597276
DOIs	https://doi.org/10.1007/978-3-030-59728-3_1
Publication status	Published - 2020
Event	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 2020 Oct 4 → 2020 Oct 8

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12267 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	20/10/4 → 20/10/8

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Complex brain networks
Disease diagnosis
Graph theory

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-59728-3_1

Cite this

Ghanbari, M., Hsu, L. M., Zhou, Z., Ghanbari, A., Mo, Z., Yap, P. T., Zhang, H., & Shen, D. (2020). A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings (pp. 3-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12267 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59728-3_1

A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease. / Ghanbari, Maryam; Hsu, Li Ming; Zhou, Zhen et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. p. 3-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12267 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ghanbari, M, Hsu, LM, Zhou, Z, Ghanbari, A, Mo, Z, Yap, PT, Zhang, H & Shen, D 2020, A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease. in AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12267 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 3-12, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 20/10/4. https://doi.org/10.1007/978-3-030-59728-3_1

Ghanbari M, Hsu LM, Zhou Z, Ghanbari A, Mo Z, Yap PT et al. A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease. In Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 3-12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59728-3_1

Ghanbari, Maryam ; Hsu, Li Ming ; Zhou, Zhen et al. / A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. editor / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. pp. 3-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application to Early Detection of Alzheimer’s Disease

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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