Estimation of Clean and Centered Brain Network Atlases using Diffusive-Shrinking Graphs with Application to Developing Brains

• Published in: Information processing in medical imaging : proceedings of the ... conference Vol. 10265; p. 385
• Main Authors: Rekik, Islem, Li, Gang, Lin, Weili, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: Germany 01.06.2017
• ISSN: 1011-2499
• DOI: 10.1007/978-3-319-59050-9_31

Many methods have been developed to spatially normalize a population of for estimating a mean image as a population-average atlas. However, methods for deriving a network atlas from a set of sitting on a complex manifold are still absent. Learning how to average brain networks across subjects constitutes a key step in creating a reliable mean representation of a population of brain networks, which can be used to spot abnormal deviations from the healthy network atlas. In this work, we propose a novel network atlas estimation framework, which guarantees that the produced network atlas is (for tuning down noisy measurements) and (for being optimally close to all subjects and representing the individual traits of each subject in the population). Specifically, for a population of brain networks, we first build a tensor, where each of its frontal-views (i.e., frontal matrices) represents a connectivity network matrix of a single subject in the population. Then, we use tensor robust principal component analysis for jointly denoising all subjects' networks through cleaving a sparse noisy network population tensor from a clean low-rank network tensor. Second, we build a graph where each node represents a frontal-view of the unfolded clean tensor (network), to leverage the local manifold structure of these networks when fusing them. Specifically, we progressively shrink the graph of networks towards the centered mean network atlas through non-linear diffusion along the neighbors of each of its nodes. Our evaluation on the developing functional and morphological brain networks at 1, 3, 6, 9 and 12 months of age has showed a better centeredness of our network atlases, in comparison with the baseline network fusion method. Further cleaning of the population of networks produces even more centered atlases, especially for the noisy functional connectivity networks.