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A Graph Representation and Similarity Measure for Brain Networks with Nodal Features
Book chapter   Peer reviewed

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 and JinWon Kim
Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities, pp 14-23
16 Sep 2018
DOI: https://doi.org/10.1007/978-3-030-00689-1_2

Abstract

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

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3 citations in Scopus

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