title: On the discovery of group-consistent graph substructure patterns from brain networks.
abstract: Complex networks constitute a recurring issue in the analysis of neuroimaging data. Recently, network motifs have been identified as patterns of interconnections since they appear in a significantly higher number than in randomized networks, in a given ensemble of anatomical or functional connectivity graphs. The current approach for detecting and enumerating motifs in brain networks requires a predetermined motif repertoire and can operate only with motifs of small size (consisting of few nodes). There is a growing interest in methodologies for frequent graph-based pattern mining in large graph datasets that can facilitate adaptive design of motifs. The results presented in this paper are based on the graph-based Substructure pattern mining (gSpan) algorithm and introduce a manifold of ways to exploit it for data-driven motif extraction in connectomics research. Functional connectivity graphs from electroencephalographic (EEG) recordings during resting state and mental calculations are used to demonstrate our approach. Relying on either time-invariant or time-evolving graphs, characteristic motifs associated with various frequency bands were derived and compared. With a suitable manipulation, the gSpan discovers motifs which are specific to performing mental arithmetics. Finally, the subject-dependent temporal signatures of motifs' appearance revealed the transient nature of the evolving functional connectivity (math-related motifs "come and go").
authors.affiliation: Data Engineering Laboratory, Department of Informatics, Aristotle University Thessaloniki, 54124, Greece. niakovid@csd.auth.gr
authors.forename: Nantia D
authors.initials: ND
authors.lastname: Iakovidou
authors.forename: Stavros I
authors.initials: SI
authors.lastname: Dimitriadis
authors.forename: Nikolaos A
authors.initials: NA
authors.lastname: Laskaris
authors.forename: Kostas
authors.initials: K
authors.lastname: Tsichlas
authors.forename: Yannis
authors.initials: Y
authors.lastname: Manolopoulos
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*