Abstract
Graph theory is able to provide quantitative parameters that describe structural and functional characteristics of human brain networks. Comparisons between subject populations have demonstrated topological disruptions in many neurological disorders; however interpreting network parameters and assessing the extent of the damage is challenging. The abstraction of brain connectivity to a set of nodes and edges in a graph is non-trivial, and factors from image acquisition, post-processing and network construction can all influence derived network parameters. We consider here the impact of edge weighting schemes in a comparative analysis of structural brain networks, using healthy control and relapsing-remitting multiple sclerosis subjects as test groups. We demonstrate that the choice of edge property can substantially affect inferences of network disruptions in disease, ranging from ‘primarily intact connectivity’ to ‘complete disruption’. Although study design should predominantly dictate the choice of edge weight, it is important to consider how study outcomes may be affected.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Geschwind, N.: Disconnexion syndromes in animal and man: Part I. Brain 88, 237–294 (1965)
Geschwind, N.: Disconnexion syndromes in animal and man: Part II. Brain 88, 237–294 (1965)
Bassett, D.S., Bullmore, E.: Small-world brain networks. Neuroscientist 12(6), 512–523 (2006)
van den Heuvel, M.P., Sporns, O.: Rich-club organization of the human connectome. J. Neurosci. 31(44), 15775–15786 (2011)
Bassett, D.S., Bullmore, E., Verchinski, B.A., Mattay, V.S., Weinberger, D.R., Meyer-Lindenberg, A.: Hierarchical organization of human cortical networks in health and schizophrenia. J. Neurosci. 28(37), 9239–9248 (2008)
He, Y., Dagher, A., Chen, Z., Charil, A., Zijdenbos, A., Worsley, K., Evans, A.: Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain 132(12), 3366–3379 (2009)
Zalesky, A., Fornito, A., Harding, I.H., Cocchi, L., Yücel, M., Pantelis, C., Bullmore, E.T.: Whole-brain anatomical networks: does the choice of nodes matter? Neuroimage 50(3), 970–983 (2010)
Bastiani, M., Shah, N., Goebel, R., Roebroeck, A.: Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm. Neuroimage 62(3), 1732–1749 (2012)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010)
Fornito, A., Zalesky, A., Breakspear, M.: Graph analysis of the human connectome: promise, progress, and pitfalls. Neuroimage 80, 426–444 (2013)
Bullmore, E.T., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci. 10, 186–198 (2009)
Cheng, H., Wang, Y., Sheng, J., Kronenberger, W.G., Mathews, V.P., Hummer, T.A., Saykin, A.J.: Characteristics and variability of structural networks derived from diffusion tensor imaging. Neuroimage 61(4), 1153–1164 (2012)
Andersson, J., Sotiropoulos, S.N.: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063–1078 (2015)
Bhushan, C., Haldar, J.P., Joshi, A.A., Leahy, R.M.: Correcting susceptibility-induced distortion in diffusion-weighted MRI using constrained nonrigid registration. In: APSIPA ASC, pp. 1–9 (2012)
Cardoso, M., Modat, M., Wolz, R., Melbourne, A., Cash, D., Rueckert, D., Ourselin, S.: Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE Trans. Med. Imaging. 34(9), 1976–1988 (2015)
Behrens, T., Berg, H., Jbabdi, S., Rushworth, M.F., Woolrich, M.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1), 144–155 (2007)
Clayden, J., Mu, S., Storkey, A., King, M., Bastin, M., Clark, C.: TractoR: magnetic resonance imaging and tractography with R. J. Stat. Softw. 44(8), 1–18 (2011)
Westfall, P., Young, S.: Resampling-Based Multiple Testing: Examples and Methods for p-value Adjustment. Wiley (2003)
Shu, N., Duan, Y., Xia, M., Schoonheim, M.M., Huang, J., Ren, Z., Sun, Z., Ye, J., Dong, H., Shi, F.D., Barkhof, F., Li, K., Liu, Y.: Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis. Sci. Rep. 6 (2016)
Llufriu, S., Martinez-Heras, E., Solana, E., Sola-Valls, N., Sepulveda, M., Blanco, Y., Martinez-Lapiscina, E.H., Andorra, M., Villoslada, P., Prats-Galino, A., Saiz, A.: Structural networks involved in attentional function in multiple sclerosis. Neuroimage Clin. 13, 288–296 (2017)
Zalesky, A., Fornito, A.: A DTI-derived measure of cortico-cortical connectivity. IEEE Trans Med. Imaging 28(7), 1023–1036 (2009)
Gamboa, O.L., Tagliazucchi, E., Von Wegner, F., Jurcoane, A., Wahl, M., Laufs, H., Ziemann, U.: Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks. Neuroimage 94, 385–395 (2014)
Baum, G.L., Roalf, D.R., Cook, P.A., Ciric, R., Rosen, A.F.G., Xia, C., Elliott, M.A., Ruparel, K., Verma, R., Tunç, B., Gur, R.C., Gur, R.E., Bassett, D.S., Satterthwaite, T.D.: The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 173, 275–286 (2018)
Jones, D.K., Knösche, T.R., Turner, R.: White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73, 239–254 (2013)
Acknowledgements
The NMR unit where this work was performed is supported by grants from the Multiple Sclerosis Society of Great Britain and Northern Ireland, Philips Healthcare, and supported by the UCL/UCLH NIHR (National Institute for Health Research) BRC (Biomedical Research Centre).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Powell, E., Prados, F., Chard, D., Toosy, A., Clayden, J.D., Wheeler-Kingshott, C.G.A.M. (2019). Edge Weights and Network Properties in Multiple Sclerosis . In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-05831-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-05831-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05830-2
Online ISBN: 978-3-030-05831-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)