Neuroimaging studies of structural connectomes typically average the data from many subjects and analyse the average properties of the resulting network. We propose a new framework for individual brain-network structural abnormality detection. The framework uses a graph-based anomaly detection algorithm that allows to detect abnormal structural connectivity on a subject level. The proposed method is generic and can be adapted for a broad range of network abnormality detection problems. In this study, we apply our method to investigate the integrity of white matter tracts of 19-year-old extremely preterm born individuals. We show the feasibility to cast the network abnormality detection problem into a min-cut max-flow problem, and identify consistent abnormal white matter tracts in extremely preterm subjects, including a common network involving the bilateral thalamus and frontal gyri.
CITATION STYLE
Irzan, H., Fidon, L., Vercauteren, T., Ourselin, S., Marlow, N., & Melbourne, A. (2020). Min-Cut Max-Flow for Network Abnormality Detection: Application to Preterm Birth. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12443 LNCS, pp. 164–173). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60365-6_16
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