Abstract
Extracting meaningful structures and data, thus unveiling the underlying base of knowledge, is a common challenging task in social, physical and life sciences. In this paper we apply a novel complex network approach based on the detection of salient links to reveal the effect of atrophy on brain connectivity. Starting from structural Magnetic Resonance Imaging (MRI) data, we firstly define a complex network model of brain connectivity, then we show how salient networks extracted from the original ones can emphasize the presence of the disease significantly reducing data complexity and computational requirements. As a proof of concept, we discuss the experimental results on a mixed cohort of 29 normal controls (NC) and 38 Alzheimer disease (AD) patients from the Alzheimer Disease Neuroimaging Initiative (ADNI). In particular, the proposed framework can reach state-of-the-art classification performances with an area under the curve \(\mathrm{AUC} = 0.93\, \pm \, 0.01\) for the NC-AD classification.
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Roosendaal, S., Geurts, J., Vrenken, H., Hulst, H., Cover, K.S., Castelijns, J., Pouwels, P.J., Barkhof, F.: Regional DTI differences in multiple sclerosis patients. Neuroimage 44(4), 1397–1403 (2009)
Barnes, D.E., Yaffe, K.: The projected effect of risk factor reduction on Alzheimer’s disease prevalence. Lancet Neurol. 10(9), 819–828 (2011)
Prince, M., Albanese, E., Guerchet, M., Prina, M.: World Alzheimer Report 2014. Dementia and Risk Reduction: An Analysis of Protective and Modifiable Factors. Alzheimers Disease International, Londres (2014)
West, M.J., Coleman, P.D., Flood, D.G., Troncoso, J.C.: Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. Lancet 344(8925), 769–772 (1994)
Chincarini, A., Bosco, P., Gemme, G., Morbelli, S., Arnaldi, D., Sensi, F., Solano, I., Amoroso, N., Tangaro, S., Longo, R., et al.: Alzheimers disease markers from structural MRI and FDG-PET brain images. Eur. Phys. J. Plus 127(11), 1–16 (2012)
Baron, J., Chetelat, G., Desgranges, B., Perchey, G., Landeau, B., De La Sayette, V., Eustache, F.: In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer’s disease. Neuroimage 14(2), 298–309 (2001)
Fischl, B.: FreeSurfer. Neuroimage 62(2), 774–781 (2012)
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782–790 (2012)
Amoroso, N., Errico, R., Bruno, S., Chincarini, A., Garuccio, E., Sensi, F., Tangaro, S., Tateo, A., Bellotti, R., Initiative, A.D.N., et al.: Hippocampal unified multi-atlas network (human): protocol and scale validation of a novel segmentation tool. Phys. Med. Biol. 60(22), 8851 (2015)
Chincarini, A., Sensi, F., Rei, L., Gemme, G., Squarcia, S., Longo, R., Brun, F., Tangaro, S., Bellotti, R., Amoroso, N., et al.: Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer’s disease. NeuroImage 125, 834–847 (2016)
Bron, E.E., Smits, M., Van Der Flier, W.M., Vrenken, H., Barkhof, F., Scheltens, P., Papma, J.M., Steketee, R.M., Orellana, C.M., Meijboom, R., et al.: Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. NeuroImage 111, 562–579 (2015)
Allen, G.I., Amoroso, N., Anghel, C., Balagurusamy, V., Bare, C.J., Beaton, D., Bellotti, R., Bennett, D.A., Boehme, K.L., Boutros, P.C., et al.: Crowdsourced estimation of cognitive decline and resilience in Alzheimer’s disease. Alzheimer’s Dement. 12(6), 645–653 (2016)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)
Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)
Grady, D., Thiemann, C., Brockmann, D.: Robust classification of salient links in complex networks. Nature Commun. 3, 864 (2012)
Menichetti, G., Remondini, D., Panzarasa, P., Mondragón, R.J., Bianconi, G.: Weighted multiplex networks. PloS One 9(6), e97857 (2014)
Boccardi, M., Bocchetta, M., Morency, F.C., Collins, D.L., Nishikawa, M., Ganzola, R., Grothe, M.J., Wolf, D., Redolfi, A., Pievani, M., et al.: Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimer’s Dement. 11(2), 175–183 (2015)
La Rocca, M., et al.: A multiplex network model to characterize brain atrophy in structural MRI. In: Mantica, G., Stoop, R., Stramaglia, S. (eds.) Emergent Complexity from Nonlinearity, in Physics, Engineering and the Life Sciences. Springer Proceedings in Physics, vol. 191. Springer, Heidelberg (2017)
Zhang, B., Horvath, S.: A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4(1), 1–17 (2005)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2013)
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Amoroso, N., Bellotti, R., Diacono, D., La Rocca, M., Tangaro, S. (2017). Salient Networks: A Novel Application to Study Brain Connectivity. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_39
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DOI: https://doi.org/10.1007/978-3-319-56148-6_39
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