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A Graph Representation and Similarity Measure for Brain Networks with Nodal Features

  • Yusuf Osmanlıoğlu
  • Birkan Tunç
  • Jacob A. Alappatt
  • Drew Parker
  • Junghoon Kim
  • Ali Shokoufandeh
  • Ragini Verma
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11044)

Abstract

The human brain demonstrates a network structure that is commonly represented using graphs with pseudonym connectome. Traditionally, connectomes encode only inter-regional connectivity as edges, while regional information, such as centrality of a node that may be crucial to the analysis, is usually handled as statistical covariates. This results in an incomplete encoding of valuable information. In order to alleviate such problems, we propose an enriched connectome encoding regional properties of the brain network, such as structural node degree, strength, and centrality, as node features in addition to representing structural connectivity between regions as weighted edges. We further present an efficient graph matching algorithm, providing two measures to quantify similarity between enriched connectomes. We demonstrate the utility of our graph representation and similarity measures on classifying a traumatic brain injury dataset. Our results show that the enriched representation combining nodal features and structural connectivity information with the graph matching based similarity measures is able to differentiate the groups better than the traditional connectome representation.

Keywords

Annotated brain networks Brain graphs Multi-feature representation Graph matching 
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Notes

Acknowledgements

This work was funded by NIH grants R01HD089390-01A1, 1 R01 NS096606, 5R01NS092398, and 5R01NS065980.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yusuf Osmanlıoğlu
    • 1
  • Birkan Tunç
    • 2
  • Jacob A. Alappatt
    • 1
  • Drew Parker
    • 1
  • Junghoon Kim
    • 3
  • Ali Shokoufandeh
    • 4
  • Ragini Verma
    • 1
  1. 1.Center for Biomedical Image Computing and Analytics, Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Center for Autism Research, Children’s Hospital of PhiladelphiaPhiladelphiaUSA
  3. 3.CUNY School of Medicine, The City College of New YorkNew YorkUSA
  4. 4.Department of Computer ScienceDrexel UniversityPhiladelphiaUSA

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