On characterizing population commonalities and subject variations in brain networks

• Published in: Medical image analysis Vol. 38; pp. 215 - 229
• Main Authors: Ghanbari, Yasser, Bloy, Luke, Tunc, Birkan, Shankar, Varsha, Roberts, Timothy P.L., Edgar, J. Christopher, Schultz, Robert T., Verma, Ragini
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.05.2017; Elsevier BV
• Subjects: Autism; Autism spectrum disorder; Brain; Children; Clustering; Connectivity analysis; Hubs; Multi-layer graph clustering; Network hubs; Neural networks; Neuroimaging; Non-negative matrix factorization; Pathology; Population (statistical); Population difference; Structure-function relationships; Population difference; Connectivity analysis; Non-negative matrix factorization; Multi-layer graph clustering; Autism spectrum disorder
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2015.10.009

•We propose a new method for analysis of brain functional and structural connectivity networks in a population.•We identify an atlas of network hubs that describe the population and are obtained from network commonalities across the subjects.•Subject-level weights are also obtained by quantifying the intra- and inter-connectivity of network hubs that best reconstruct a subject's network.•An NMF approach combined with multi-layer graph clustering is developed to find desired network hubs.•The proposed method was applied to MEG and DTI datasets in the study of autism using modeled within- and between-hub connectivity. Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called ‘network hubs’, which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs. [Display omitted] The aim of this work is (a) to create a hub atlas for a population by mapping a collection of multi-node brain networks into a system of network hubs, and (b) given a subject's network, quantify the contribution of each network hub at the subject level (illustrated by the size of the hub) as well as the strength of the inter-connectivity between pairs of hubs in the subject's network (illustrated by the thickness of hub inter-connections).