An extensive assessment of network alignment algorithms for comparison of brain connectomes

Abstract

BackgroundRecently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic 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. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem.ResultsIn this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset.ConclusionThe results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.

Cognitive Control and Brain Network Dynamics during Word Generation Tasks Predicted Using a Novel Event-Related Deep Brain Activity Method

“…Such counterevidence to the conventional anti-correlation-based brain network architectures [27] is thought to originate from dynamic interactions [28]. Using a graph theory [29] [30], correlations across the entire brain have been investigated using fMRI data measured both during rest and while actively performing tasks to explore how the human brain is functionally organized [31] [32].…”

A review of protein–protein interaction network alignment: From pathway comparison to global alignment

“…It is obviously a challenge for algorithm design and even for physical limits of computing power. Nevertheless, network alignment could be applied to not only systems biology, but also many other fields, such as neural science, social network analysis and knowledge management [46] , [47] , [37] , [39] , [65] .…”

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner

“…Since network alignment is an NP-complete problem 3 , all such algorithms must use heuristics to navigate this enormous search space. Search methods abound; several good review papers exist (Clark and Kalita, 2014;Faisal et al, 2015b;Milano et al, 2017;Guzzi and Milenković, 2017); for an extensive comparison specifically showing that SANA outperforms about a dozen of the best existing algorithms, see Mamano and Hayes (2017). SANA is virtually unique in that it was designed from the start to be able to optimize any objective function, including the objective functions introduced by other researchers; a preliminary report shows that SANA outperforms over a dozen other algorithms at optimizing their own objective functions (Kanne and Hayes, 2017).…”

“…A biological network consists of a set of nodes representing entities, with edges connecting entities that are related in some way. They come in many varieties, such as protein-protein interaction (PPI) networks (Williamson and Sutcliffe, 2010;Jaenicke and Helmreich, 2012), gene regulatory networks (Davidson, 2010;Karlebach and Shamir, 2008), gene-µRNA networks (Chen and Rajewsky, 2007;Prescott, 2012;Farazi et al, 2013;Kotlyar et al, 2015;Tokar et al, 2017), metabolic networks (Fiehn, 2002), brain connectomes (Milano et al, 2017), and many others (Junker and Schreiber, 2011). It is believed that the structure of the networks, in the form of the network topology, is related to the function of the entities (Davidson, 2010;Davis et al, 2015;Sporns, 2010).…”