Dynamic Changes in Long-Standing Multiple Sclerosis Revealed by Longitudinal Structural Network Analysis Using Diffusion Tensor Imaging

REFERENCES

1. 1.↵
   1. Reich DS,
   2. Lucchinetti CF,
   3. Calabresi PA

   . Multiple sclerosis. N Engl J Med 2018;378:169–80 doi:10.1056/NEJMra1401483 pmid:29320652

   CrossRefPubMed
2. 2.↵
   1. Ge Y,
   2. Law M,
   3. Grossman RI

   . Applications of diffusion tensor MR imaging in multiple sclerosis. Ann NY Acad Sci 2006;1064:202–19 doi:10.1196/annals.1340.039 pmid:16394158

   CrossRefPubMed
3. 3.↵
   1. Inglese M,
   2. Bester M

   . Diffusion imaging in multiple sclerosis: research and clinical implications. NMR Biomed 2010;23:865–72 doi:10.1002/nbm.1515 pmid:20882528

   CrossRefPubMed
4. 4.↵
   1. Soares JM,
   2. Marques P,
   3. Alves V, et al

   . A hitchhiker’s guide to diffusion tensor imaging. Front Neurosci 2013;7:31 doi:10.3389/fnins.2013.00031 pmid:23486659

   CrossRefPubMed
5. 5.↵
   1. Ontaneda D,
   2. Sakaie K,
   3. Lin J, et al

   . Identifying the start of multiple sclerosis injury: a serial DTI study. J Neuroimaging 2014;24:569–76 doi:10.1111/jon.12082 pmid:25370339

   CrossRefPubMed
6. 6.↵
   1. Huang J,
   2. Liu Y,
   3. Zhao T, et al

   . White matter microstructural alterations in clinically isolated syndrome and multiple sclerosis. J Clin Neurosci 2018;53:27–33 doi:10.1016/j.jocn.2018.01.007 pmid:29754967

   CrossRefPubMed
7. 7.↵
   1. Klistorner A,
   2. Wang C,
   3. Yiannikas C, et al

   . Evidence of progressive tissue loss in the core of chronic MS lesions: a longitudinal DTI study. Neuroimage Clin 2018;17:1028–35 doi:10.1016/j.nicl.2017.12.010 pmid:29387524

   CrossRefPubMed
8. 8.↵
   1. Yu FF,
   2. Chiang FL,
   3. Stephens N, et al

   . Characterization of normal-appearing white matter in multiple sclerosis using quantitative susceptibility mapping in conjunction with diffusion tensor imaging. Neuroradiology 2019;61:71–79 doi:10.1007/s00234-018-2137-7 pmid:30539215

   CrossRefPubMed
9. 9.↵
   1. de Kouchkovsky I,
   2. Fieremans E,
   3. Fleysher L, et al

   . Quantification of normal-appearing white matter tract integrity in multiple sclerosis: a diffusion kurtosis imaging study. J Neurol 2016;263:1146–55 doi:10.1007/s00415-016-8118-z pmid:27094571

   CrossRefPubMed
10. 10.↵
    1. Rubinov M,
    2. Sporns O

    . Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010;52:1059–69 doi:10.1016/j.neuroimage.2009.10.003 pmid:19819337

    CrossRefPubMedWeb of Science
11. 11.↵
    1. Fleischer V,
    2. Radetz A,
    3. Ciolac D, et al

    . Graph theoretical framework of brain networks in multiple sclerosis: a review of concepts. Neuroscience 2019;403:35–53 doi:10.1016/j.neuroscience.2017.10.033 pmid:29101079

    CrossRefPubMed
12. 12.↵
    1. Van Essen DC

    . A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 1997;385:313–18 doi:10.1038/385313a0 pmid:9002514

    CrossRefPubMedWeb of Science
13. 13.↵
    1. Newman ME

    . Modularity and community structure in networks. Proc Natl Acad Sci USA 2006;103:8577–82 doi:10.1073/pnas.0601602103 pmid:16723398

    Abstract/FREE Full Text
14. 14.↵
    1. Llufriu S,
    2. Martinez-Heras E,
    3. Solana E, et al

    . Structural networks involved in attention and executive functions in multiple sclerosis. Neuroimage Clin 2017;13:288–96 doi:10.1016/j.nicl.2016.11.026 pmid:28050344

    CrossRefPubMed
15. 15.↵
    1. Kocevar G,
    2. Stamile C,
    3. Hannoun S, et al

    . Graph theory-based brain connectivity for automatic classification of multiple sclerosis clinical courses. Front Neurosci 2016;10:478 doi:10.3389/fnins.2016.00478 pmid:27826224

    CrossRefPubMed
16. 16.↵
    1. Shu N,
    2. Liu Y,
    3. Li K, et al

    . Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cereb Cortex 2011;21:2565–77 doi:10.1093/cercor/bhr039 pmid:21467209

    CrossRefPubMedWeb of Science
17. 17.↵
    1. Hawkins R,
    2. Shatil AS,
    3. Lee L, et al

    . Reduced global efficiency and random network features in patients with relapsing-remitting multiple sclerosis with cognitive impairment. AJNR Am J Neuroradiol 2020;41:449–55 doi:10.3174/ajnr.A6435 pmid:32079601

    Abstract/FREE Full Text
18. 18.↵
    1. Welton T,
    2. Constantinescu CS,
    3. Auer DP, et al

    . Graph theoretic analysis of brain connectomics in multiple sclerosis: reliability and relationship with cognition. Brain Connect 2020;10:95–104 doi:10.1089/brain.2019.0717 pmid:32079409

    CrossRefPubMed
19. 19.↵
    1. Charalambous T,
    2. Tur C,
    3. Prados F, et al

    . Structural network disruption markers explain disability in multiple sclerosis. J Neurol Neurosurg Psychiatry 2019;90:219–26 doi:10.1136/jnnp-2018-318440 pmid:30467210

    Abstract/FREE Full Text
20. 20.↵
    1. Thompson AJ,
    2. Banwell BL,
    3. Barkhof F, et al

    . Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 2018;17:162–73 doi:10.1016/S1474-4422(17)30470-2 pmid:29275977

    CrossRefPubMed
21. 21.↵
    1. Lublin FD,
    2. Coetzee T,
    3. Cohen JA

    , International Advisory Committee on Clinical Trials in MS, et al. The 2013 clinical course descriptors for multiple sclerosis. Neurology 2020;94:1088–92 doi:10.1212/WNL.0000000000009636 pmid:32471886

    CrossRefPubMed
22. 22.↵
    1. Schmidt P

    . Bayesian inference for structured additive regression models for large-scale problems with applications to medical imaging, Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics 2017
23. 23.↵
    1. Schmidt P,
    2. Gaser C,
    3. Arsic M, et al

    . An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage 2012;59:3774–83 doi:10.1016/j.neuroimage.2011.11.032 pmid:22119648

    CrossRefPubMed
24. 24.↵
    1. Battaglini M,
    2. Jenkinson M,
    3. De Stefano N

    . Evaluating and reducing the impact of white matter lesions on brain volume measurements. Hum Brain Mapp 2011;33:2062–71 doi:10.1002/hbm.21344 pmid:21882300

    CrossRefPubMed
25. 25.↵
    1. Smith SM,
    2. Jenkinson M,
    3. Woolrich MW, et al

    . Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23 Suppl 1:S208–19 doi:10.1016/j.neuroimage.2004.07.051 pmid:15501092

    CrossRefPubMed
26. 26.↵
    1. Jenkinson M,
    2. Beckmann CF,
    3. Behrens TE, et al

    . FSL. NeuroImage 2012;62:782–90 doi:10.1016/j.neuroimage.2011.09.015 pmid:21979382

    CrossRefPubMedWeb of Science
27. 27.↵
    1. Tzourio-Mazoyer N,
    2. Landeau B,
    3. Papathanassiou D, et al

    . Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273–89 doi:10.1006/nimg.2001.0978 pmid:11771995

    CrossRefPubMedWeb of Science
28. 28.↵
    1. Gong G,
    2. He Y,
    3. Concha L, et al

    . Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 2009;19:524–36 doi:10.1093/cercor/bhn102 pmid:18567609

    CrossRefPubMedWeb of Science
29. 29.↵
    1. Liu Y,
    2. Duan Y,
    3. Dong H, et al

    . Disrupted module efficiency of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis. Front Hum Neurosci 2018;12:138 doi:10.3389/fnhum.2018.00138 pmid:29692717

    CrossRefPubMed
30. 30.↵
    1. Wang R,
    2. Benner T,
    3. Sorensen AG, et al

    . Diffusion toolkit: a software package for diffusion imaging data processing and tractography. Proc Intl Soc Mag Reson Med 2007;15
31. 31.↵
    1. Mori S,
    2. Crain BJ,
    3. Chacko VP, et al

    . Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 1999;45:265–69

    CrossRefPubMedWeb of Science
32. 32.↵
    1. Basser PJ,
    2. Pajevic S,
    3. Pierpaoli C, et al

    . In vivo fiber tractography using DT-MRI data. Magn Reson Med 2000;44:625–32 doi:10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O

    CrossRefPubMedWeb of Science
33. 33.↵
    1. Shu N,
    2. Duan Y,
    3. Xia M, et al

    . Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis. Sci Rep 2016;6:29383 doi:10.1038/srep29383 pmid:27403924

    CrossRefPubMed
34. 34.↵
    1. Xu X,
    2. Lau KK,
    3. Wong YK, et al

    . The effect of the total small vessel disease burden on the structural brain network. Sci Rep 2018;8:7442 doi:10.1038/s41598-018-25917-4 pmid:29748646

    CrossRefPubMed
35. 35.↵
    1. Wang J,
    2. Wang X,
    3. Xia M, et al

    . GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 2015;9:386

    CrossRefPubMed
36. 36.↵
    1. Schneider R,
    2. Genc E,
    3. Ahlborn C, et al

    . Temporal dynamics of diffusion metrics in early multiple sclerosis and clinically isolated syndrome: a 2-year follow-up tract-based spatial statistics study. Front Neurol 2019;10:1165 doi:10.3389/fneur.2019.01165 pmid:31749760

    CrossRefPubMed
37. 37.↵
    1. Zivadinov R,
    2. Bergsland N,
    3. Hagemeier J, et al

    . Effect of teriflunomide on gray and white matter brain pathology in multiple sclerosis using volumetric and diffusion-tensor imaging MRI measures. J Neurol Sci 2018;388:175–81 doi:10.1016/j.jns.2018.03.028 pmid:29627017

    CrossRefPubMed
38. 38.↵
    1. Rashid W,
    2. Hadjiprocopis A,
    3. Davies G, et al

    . Longitudinal evaluation of clinically early relapsing-remitting multiple sclerosis with diffusion tensor imaging. J Neurol 2008;255:390–97 doi:10.1007/s00415-008-0678-0 pmid:18350361

    CrossRefPubMed
39. 39.↵
    1. Toschi N,
    2. De Santis S,
    3. Granberg T, et al

    . Evidence for progressive microstructural damage in early multiple sclerosis by multi-shell diffusion magnetic resonance imaging. Neuroscience 2019;403:27–34 doi:10.1016/j.neuroscience.2019.01.022 pmid:30708049

    CrossRefPubMed
40. 40.↵
    1. Fox RJ,
    2. Cronin T,
    3. Lin J, et al

    . Measuring myelin repair and axonal loss with diffusion tensor imaging. AJNR Am J Neuroradiol 2011;32:85–91 doi:10.3174/ajnr.A2238 pmid:20947644

    Abstract/FREE Full Text
41. 41.↵
    1. Lemus HN,
    2. Warrington AE,
    3. Rodriguez M

    . Multiple sclerosis. Neurol Clin 2018;36:1–11 doi:10.1016/j.ncl.2017.08.002 pmid:29157392

    CrossRefPubMed
42. 42.↵
    1. Tsagkas C,
    2. Chakravarty MM,
    3. Gaetano L, et al

    . Longitudinal patterns of cortical thinning in multiple sclerosis. Hum Brain Mapp 2020;41:2198–215 doi:10.1002/hbm.24940 pmid:32067281

    CrossRefPubMed