Version 1
: Received: 11 October 2017 / Approved: 12 October 2017 / Online: 12 October 2017 (04:07:33 CEST)
How to cite:
Milano, M.; Guzzi, P.H.; Cannataro, M. Application of Multi Network Alignment Algorithms for Connectomes Study. Preprints2017, 2017100073. https://doi.org/10.20944/preprints201710.0073.v1
Milano, M.; Guzzi, P.H.; Cannataro, M. Application of Multi Network Alignment Algorithms for Connectomes Study. Preprints 2017, 2017100073. https://doi.org/10.20944/preprints201710.0073.v1
Milano, M.; Guzzi, P.H.; Cannataro, M. Application of Multi Network Alignment Algorithms for Connectomes Study. Preprints2017, 2017100073. https://doi.org/10.20944/preprints201710.0073.v1
APA Style
Milano, M., Guzzi, P.H., & Cannataro, M. (2017). Application of Multi Network Alignment Algorithms for Connectomes Study. Preprints. https://doi.org/10.20944/preprints201710.0073.v1
Chicago/Turabian Style
Milano, M., Pietro Hiram Guzzi and Mario Cannataro. 2017 "Application of Multi Network Alignment Algorithms for Connectomes Study" Preprints. https://doi.org/10.20944/preprints201710.0073.v1
Abstract
A growing area in neurosciences is focused on the modeling and analysis the complex system of connections in neural systems, i.e. the connectome. Here we focus on the representation of connectomes by using graph theory formalisms. The human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. Recently, it has been proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space to define network nodes of individual brain networks. In the network domain, the question of comparison of the structure of networks arises. Such question is tackled by modeling the comparison of brain network as a network alignment (NA) problem. In this paper, we first defined the NA problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.
Keywords
graph alignment; brain network; human connectome
Subject
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
