A growing area in neurosciences is focused on the modelling and analysis of connectome, i.e. the set of connections among the constitutive element of the brain. Among the representations, the use of graph theory is largely used. Such discipline uses neuroimaging techniques to derive the connections among the elements. Images represent the anatomical regions of the brain and images are usually compared to an atlas to identify them, and this process is usually referred to as parcellation. It fails in the presence of disease and for brains of children. Consequently, atlas-free random brain parcellation has been proposed that is based to divide the image into regions and to model each region as a node. Then connections among regions are represented by (un)weighted edges. To improve parcellation, a set of images is taken from the same subject, and then the resulting brain is derived by comparing resulting graphs of each image. Therefore, the question of comparison of the structure of networks arises using network alignment (NA) algorithms. In this paper, we first defined the NA problem formally, then we applied three existing states of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks, and we compared the performances. Our findings show that MNA algorithms may be applied.
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PHG,MM and MC have been partially supported by the following research project funded by the Italian Ministry of Education and Research (MIUR): BA2Know-Business Analytics to Know (PON03PE_00001_1). PHG was partially supported by GNCS project INdAM - GNCS Project 2017: Efficient Algorithms and Techniques for the Organization, Management and Analysis of Biological Big Data (Algoritmi e tecniche efficienti per lorganizzazione, la gestione e l’analisi di Big Data in ambito biologico) The authors wish to thank Olga Tymofiyeva for her suggestions to this research activity.
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Milano, M., Guzzi, P.H. & Cannataro, M. Using multiple network alignment for studying connectomes. Netw Model Anal Health Inform Bioinforma 8, 5 (2019). https://doi.org/10.1007/s13721-019-0182-8