Abstract
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.
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References
Andersson, J.L.R., Sotiropoulos, S.N.: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125, 1063–1078 (2016)
Ball, G., et al.: The effect of preterm birth on thalamic and cortical development. Cereb. Cortex 22(5), 1016–1024 (2012). (New York, N.Y.: 1991)
Ball, G., et al.: Thalamocortical connectivity predicts cognition in children born preterm. Cereb. Cortex 25(11), 4310–4318 (2015). (New York, N.Y.: 1991)
Cardoso, M.J., et al.: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging 34(9), 1976–1988 (2015)
Irzan, H., et al.: A network-based analysis of the preterm adolescent brain using PCA and graph theory. In: 2019 MICCAI International Workshop on Computational Diffusion MRI (CDMRI). Springer, Cham (2019). https://doi.org/10.1007/978-3-030-52893-5
Jeurissen, B., et al.: Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage 103, 411–426 (2014)
Kellner, E., et al.: Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med. 76(5), 1574–1581 (2019)
Fulkerson, D.R., Ford, L.R.: Flows in Networks. Rand Corporation Research Studies Series. PUP (1962)
O’Reilly, H., et al.: Neuropsychological outcomes at 19 years of age following extremely preterm birth. Pediatrics 145(2), e20192087 (2020)
Smith, R.E., et al.: Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage 62(3), 1924–1938 (2012)
Smith, R.E., et al.: Sift2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. NeuroImage 119, 338–351 (2015)
Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLOS Comput. Biol. 1(4), e42 (2005)
Tournier, J.D., et al.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4), 1459–1472 (2007)
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Veraart, J., et al.: Denoising of diffusion MRI using random matrix theory. NeuroImage 142, 394–406 (2016)
von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)
Acknowledgements
This work is supported by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1). We would like to acknowledge the MRC (MR/J01107X/1) and the National Institute for Health Research (NIHR).
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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: Sudre, C.H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis. UNSURE GRAIL 2020 2020. Lecture Notes in Computer Science(), vol 12443. Springer, Cham. https://doi.org/10.1007/978-3-030-60365-6_16
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DOI: https://doi.org/10.1007/978-3-030-60365-6_16
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