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Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load
Open accessAbstract
White matter tracts, which play a crucial role in the coordination of information flow between different regions of grey matter, are particularly vulnerable to multiple sclerosis. Many studies have shown that the white matter lesions in multiple sclerosis are associated with focal abnormalities of grey matter, but little is known about the alterations in the coordinated patterns of cortical morphology among regions in the disease. Here, we used cortical thickness measurements from structural magnetic resonance imaging to investigate the relationship between the white matter lesion load and the topological efficiency of structural cortical networks in multiple sclerosis. Network efficiency was defined using a 'small-world' network model that quantifies the effectiveness of information transfer within brain networks. In this study, we first classified patients (n = 330) into six subgroups according to their total white matter lesion loads, and identified structural brain networks for each multiple sclerosis group by thresholding the corresponding inter-regional cortical thickness correlation matrix, followed by a network efficiency analysis with graph theoretical approaches. The structural cortical networks in multiple sclerosis demonstrated efficient small-world architecture regardless of the lesion load, an organization that maximizes the information processing at a relatively low wiring cost. However, we found that the overall small-world network efficiency in multiple sclerosis was significantly disrupted in a manner proportional to the extent of total white matter lesions. Moreover, regional efficiency was also significantly decreased in specific brain regions, including the insula and precentral gyrus as well as regions of prefrontal and temporal association cortices. Finally, we showed that the lesions also altered many cortical thickness correlations in the frontal, temporal and parietal lobes. Our results suggest that the white matter lesions in multiple sclerosis might be associated with aberrant neuronal connectivity among widely distributed brain regions, and provide structural (morphological) evidence for the notion of multiple sclerosis as a disconnection syndrome.
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Cites
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.668698 · Cited by 4,731
Self-citationAutomatic 3D intersubject registration of MR volumetric data in standardized Talairach space
Collins, D. L., Neelin, P., Peters, T. M., & Evans, A. C. (1994) · Journal of Computer Assisted Tomography · doi:10.1097/00004728-199403000-00005 · Cited by 3,431
Self-citationMesulam, M. (1998) · Brain · doi:10.1093/brain/121.6.1013 · Cited by 3,072
Efficiency and Cost of Economical Brain Functional Networks
Achard, S., & Bullmore, E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030017 · Cited by 2,553
He, Y., Chen, Z., & Evans, A. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0141-08.2008 · Cited by 965
Self-citationMapping Human Whole-Brain Structural Networks with Diffusion MRI
Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V. J., Meuli, R., & Thiran, J.-P. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000597 · Cited by 788
Studying the human brain anatomical network via diffusion-weighted MRI and graph theory
Iturria-Medina, Y., Sotero, R. C., Canales-Rodríguez, E. J., Alemán-Gómez, Y., & Melie-García, L. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.060 · Cited by 506
Measurement of cortical thickness using an automated 3-D algorithm: a validation study
Kabani, N., Le Goualher, G., MacDonald, D., & Evans, A. C. (2001) · NeuroImage · doi:10.1006/nimg.2000.0652 · Cited by 250
Self-citationLowe, M. J., Beall, E. B., Sakaie, K. E., Koenig, K. A., Stone, L., Marrie, R. A., & Phillips, M. D. (2008) · Human Brain Mapping · doi:10.1002/hbm.20576 · Cited by 196
Cited by
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750
Experimental and theoretical approaches to conscious processing
Dehaene, S., & Changeux, J.-P. (2011) · Neuron · doi:10.1016/j.neuron.2011.03.018 · Cited by 2,573
Function in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529
Whole-brain anatomical networks: does the choice of nodes matter?
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.027 · Cited by 1,169
Imaging structural co-variance between human brain regions
Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3465 · Cited by 1,064
Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder
Zhang, J., Wang, J., Wu, Q., Kuang, W., Huang, X., He, Y., & Gong, Q. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.05.018 · Cited by 951
Self-citationSchizophrenia, neuroimaging and connectomics
Fornito, A., Zalesky, A., Pantelis, C., & Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.090 · Cited by 716
Graph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743
Liang, X., Zou, Q., He, Y., & Yang, Y. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1214900110 · Cited by 677
Self-citationGraph-based network analysis of resting-state functional MRI
Wang, J. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00016 · Cited by 614
Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis
Rocca, M. A., Amato, M. P., De Stefano, N., Enzinger, C., Geurts, J. J., Penner, I.-K., Rovira, A., Sumowski, J. F., Valsasina, P., & Filippi, M. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(14)70250-9 · Cited by 557
Topological organization of the human brain functional connectome across the lifespan
Cao, M., Wang, J.-H., Dai, Z.-J., Cao, X.-Y., Jiang, L.-L., Fan, F.-M., Song, X.-W., Xia, M.-R., Shu, N., Dong, Q., et al. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2013.11.004 · Cited by 440
Disrupted axonal fiber connectivity in schizophrenia
Zalesky, A., Fornito, A., Seal, M. L., Cocchi, L., Westin, C.-F., Bullmore, E. T., Egan, G. F., & Pantelis, C. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2010.08.022 · Cited by 439
Networks of anatomical covariance
Evans, A. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.054 · Cited by 429
Self-citationWang, J.-H., Zuo, X.-N., Gohel, S., Milham, M. P., Biswal, B. B., & He, Y. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021976 · Cited by 416
Machine learning in major depression: from classification to treatment outcome prediction
Gao, S., Calhoun, V. D., & Sui, J. (2018) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13048 · Cited by 370
As-Sanie, S., Harris, R. E., Napadow, V., Kim, J., Neshewat, G., Kairys, A., Williams, D., Clauw, D. J., & Schmidt-Wilcke, T. (2012) · Pain · doi:10.1016/j.pain.2012.01.032 · Cited by 268
Dehaene, S., Changeux, J.-P., & Naccache, L. (2011) · Research and Perspectives in Neurosciences · doi:10.1007/978-3-642-18015-6_4 · Cited by 209
Discrete neuroanatomical networks are associated with specific cognitive abilities in old age
Wen, W., Zhu, W., He, Y., Kochan, N. A., Reppermund, S., Slavin, M. J., Brodaty, H., Crawford, J., Xia, A., & Sachdev, P. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.4085-10.2011 · Cited by 204
Childhood maltreatment: altered network centrality of cingulate, precuneus, temporal pole and insula
Teicher, M. H., Anderson, C. M., Ohashi, K., & Polcari, A. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.09.016 · Cited by 202
Relationship Between White Matter Hyperintensities, Cortical Thickness, and Cognition
Tuladhar, A. M., Reid, A. T., Shumskaya, E., de Laat, K. F., van Norden, A. G. W., van Dijk, E. J., Norris, D. G., & de Leeuw, F.-E. (2015) · Stroke · doi:10.1161/strokeaha.114.007146 · Cited by 195
Graph theoretical framework of brain networks in multiple sclerosis: a review of concepts
Fleischer, V., Radetz, A., Ciolac, D., Muthuraman, M., Gonzalez-Escamilla, G., Zipp, F., & Groppa, S. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2017.10.033 · Cited by 151
Abnormal small-world architecture of top-down control networks in obsessive-compulsive disorder
Zhang, T. (2011) · Journal of Psychiatry & Neuroscience · doi:10.1503/jpn.100006 · Cited by 134
Ginestet, C. E., & Simmons, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.030 · Cited by 112
Gaiteri, C., Chen, M., Szymanski, B., Kuzmin, K., Xie, J., Lee, C., Blanche, T., Chaibub Neto, E., Huang, S.-C., Grabowski, T., et al. (2015) · Scientific Reports · doi:10.1038/srep16361 · Cited by 76
Evidence for disrupted gray matter structural connectivity in posttraumatic stress disorder
Mueller, S. G., Ng, P., Neylan, T., Mackin, S., Wolkowitz, O., Mellon, S., Yan, X., Flory, J., Yehuda, R., Marmar, C. R., et al. (2015) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2015.09.006 · Cited by 61
Markovič, R., Stožer, A., Gosak, M., Dolenšek, J., Marhl, M., & Rupnik, M. S. (2015) · Scientific Reports · doi:10.1038/srep07845 · Cited by 61
Hosseini, S. M. H., & Kesler, S. R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.032 · Cited by 59
liu, Z., Guo, H., Cao, X., Cheng, C., Yang, C., Xu, C., Zhang, A., Sun, N., Li, X., & Zhang, K. (2014) · Psychiatry Research: Neuroimaging · doi:10.1016/j.pscychresns.2014.05.010 · Cited by 50
Kim, H.-J., Shin, J.-H., Han, C. E., Kim, H. J., Na, D. L., Seo, S. W., & Seong, J.-K. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00394 · Cited by 44
Dong, D., Li, C., Ming, Q., Zhong, X., Zhang, X., Sun, X., Jiang, Y., Gao, Y., Wang, X., & Yao, S. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.03.030 · Cited by 26
Metadata
- Authors
- He, Y., Dagher, A., Chen, Z., Charil, A., Zijdenbos, A., Worsley, K., & Evans, A.
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-05-12
- Volume
- 132
- Issue
- 12
- Pages
- 3366-3379
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2165687585
- Cited by
- 452
- References
- 9
- FWCI
- 11.88
- Type
- journal article
- Language
- en
- Open access
- green
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