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Med Image Anal. 2017 May;38:215-229. doi: 10.1016/j.media.2015.10.009. Epub 2015 Dec 1.

On characterizing population commonalities and subject variations in brain networks.

Author information

1
Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States . Electronic address: yasser.ghanbari@uphs.upenn.edu.
2
Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States ; Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.
3
Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.
4
Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.

Abstract

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.

KEYWORDS:

Autism spectrum disorder; Connectivity analysis; Multi-layer graph clustering; Non-negative matrix factorization; Population difference

PMID:
26674972
PMCID:
PMC4887425
DOI:
10.1016/j.media.2015.10.009
[Indexed for MEDLINE]
Free PMC Article

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