title: Functionally driven brain networks using multi-layer graph clustering.
abstract: Connectivity analysis of resting state brain has provided a novel means of investigating brain networks in the study of neurodevelpmental disorders. The study of functional networks, often represented by high dimensional graphs, predicates on the ability of methods in succinctly extracting meaningful representative connectivity information at the subject and population level. This need motivates the development of techniques that can extract underlying network modules that characterize the connectivity in a population, while capturing variations of these modules at the individual level. In this paper, we propose a multi-layer raph clustering technique that fuses the information from a collection of connectivity networks of a population to extract the underlying common network modules that serve as network hubs for the population. These hubs form a functional network atlas. In addition, our technique provides subject-specific factors designed to characterize and quantify the degree of intra- and inter- connectivity between hubs, thereby providing a representation that is amenable to group level statistical analyses. We demonstrate the utility of the technique by creating a population network atlas of connectivity by examining MEG based functional connectivity in typically developing children, and using this to describe the individualized variation in those diagnosed with autism spectrum disorder.
authors.forename: Yasser
authors.initials: Y
authors.lastname: Ghanbari
authors.forename: Luke
authors.initials: L
authors.lastname: Bloy
authors.forename: Varsha
authors.initials: V
authors.lastname: Shankar
authors.forename: J Christopher
authors.initials: JC
authors.lastname: Edgar
authors.forename: Timothy P L
authors.initials: TP
authors.lastname: Roberts
authors.forename: Robert T
authors.initials: RT
authors.lastname: Schultz
authors.forename: Ragini
authors.initials: R
authors.lastname: Verma
mesh_terms: D000293:Adolescent; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D002659:Child Development Disorders, Pervasive / Q000503:physiopathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015225:Magnetoencephalography; D008297:Male; D009415:Nerve Net / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity