[en] Introduction: Quantitative MRI quantifies tissue microstructural properties and supports the characterization of cerebral tissue damages. With an MPM protocol, 4 parameter maps are constructed: MTsat, PD, R1 and R2*, reflecting tissue physical properties associated with iron and myelin contents. Thus, qMRI is a good candidate for in vivo monitoring of cerebral damage and repair mechanisms related to MS. Here, we used qMRI to investigate the longitudinal microstructural changes in MS brain. Methods: Seventeen MS patients (age 25-65, 11 RRMS) were scanned on a 3T MRI, in two sessions separated with a median of 30 months, and the parameters evolution was evaluated within several tissue classes: NAWM, NACGM and NADGM, as well as focal WM lesions. An individual annual rate of change for each qMRI parameter was computed, and its correlation to clinical status was evaluated. For WM plaques, three areas were defined, and a GLMM tested the effect of area, time points, and their interaction on each median qMRI parameter value. Results: Patients with a better clinical evolution, that is, clinically stable or improving state, showed positive annual rate of change in MTsat and R2* within NAWM and NACGM, suggesting repair mechanisms in terms of increased myelin content and/or axonal density as well as edema/inflammation resorption. When examining WM lesions, qMRI parameters within surrounding NAWM showed microstructural modifications, even before any focal lesion is visible on conventional FLAIR MRI. Conclusion: The results illustrate the benefit of multiple qMRI data in monitoring subtle changes within normal appearing brain tissues and plaque dynamics in relation with tissue repair or disease progression. Emilie Lommers and Christophe Phillips equally contributed to the work.
Centre/Unité de recherche :
GIGA CRC In vivo Imaging-Neuroimaging, data acquisition and processing - ULiège
Vandeleene, Nora ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing
Guillemin, Camille ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory ; Université de Liège - ULiège > Psychologie et Neuroscience Cognitives (PsyNCog)
Dauby, Solène ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Requier, Florence ; Université de Liège - ULiège > Psychologie et Neuroscience Cognitives (PsyNCog) ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Charonitis, Maëlle ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Aging & Memory
Chylinski, Daphné ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Balteau, Evelyne ; Université de Liège - ULiège > Département des sciences de la vie > Virologie - Immunologie
Maquet, Pierre ; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Lommers, Emilie ✱; Centre Hospitalier Universitaire de Liège - CHU > > Service de neurologie ; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Sleep and chronobiology
Phillips, Christophe ✱; Université de Liège - ULiège > GIGA > GIGA CRC In vivo Imaging - Neuroimaging, data acquisition and processing
✱ Ces auteurs ont contribué de façon équivalente à la publication.
Langue du document :
Anglais
Titre :
Using quantitative magnetic resonance imaging to track cerebral alterations in multiple sclerosis brain: A longitudinal study
Andersen, S. M., Rapcsak, S. Z., & Beeson, P. M. (2010). Cost function masking during normalization of brains with focal lesions: Still a necessity? Neuroimage, 53(1), 78–84. https://doi.org/10.1016/j.neuroimage.2010.06.003
Anderson, M. J. (2001). Permutation tests for univariate or multivariate analysis of variance and regression. Canadian Journal of Fisheries and Aquatic Sciences, 58(3), 626–639. https://doi.org/10.1139/f01-004
Andica, C., Hagiwara, A., Kamagata, K., Yokoyama, K., Shimoji, K., Saito, A., Takenaka, Y., Nakazawa, M., Hori, M., Cohen-Adad, J., Takemura, M. Y., Hattori, N., & Aoki, S. (2019). Gray matter alterations in early and late relapsing-remitting multiple sclerosis evaluated with synthetic quantitative magnetic resonance imaging. Scientific Reports, 9(1), 8147. https://doi.org/10.1038/s41598-019-44615-3
Ashburner, J., & Ridgway, G. R. (2013). Symmetric diffeomorphic modeling of longitudinal structural MRI. Frontiers in Neuroscience, 6, https://doi.org/10.3389/fnins.2012.00197
Ashburner, J., & Friston, K. J. (2005). Unified segmentation. Neuroimage, 26(3), 839–851. https://doi.org/10.1016/j.neuroimage.2005.02.018
Bagnato, F., Hametner, S., Boyd, E., Endmayr, V., Shi, Y., Ikonomidou, V., Chen, G., Pawate, S., Lassmann, H., Smith, S., & Welch, E. B. (2018). Untangling the R2* contrast in multiple sclerosis: A combined MRI-Histology study at 7.0 tesla. PLoS ONE, 13(3), e0193839. https://doi.org/10.1371/journal.pone.0193839
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300.
Bermel, R. A., & Bakshi, R. (2006). The measurement and clinical relevance of brain atrophy in multiple sclerosis. The Lancet Neurology, 5(2), 158–170. https://doi.org/10.1016/S1474-4422(06)70349-0
Bodini, B., Veronese, M., García-Lorenzo, D., Battaglini, M., Poirion, E., Chardain, A., Freeman, L., Louapre, C., Tchikviladze, M., Papeix, C., Dollé, F., Zalc, B., Lubetzki, C., Bottlaender, M., Turkheimer, F., & Stankoff, B. (2016). Dynamic imaging of individual remyelination profiles in multiple sclerosis. Annals of Neurology, 79(5), 726–738. https://doi.org/10.1002/ana.24620
Bonnier, G., Maréchal, B., Fartaria, M. J., Falkowskiy, P., Marques, J. P., Simioni, S., Schluep, M., Pasquier, R. D. u., Thiran, J. -P., Krueger, G., & Granziera, C. (2017). The combined quantification and interpretation of multiple quantitative magnetic resonance imaging metrics enlightens longitudinal changes compatible with brain repair in relapsing-remitting multiple sclerosis patients. Frontiers in Neurology, 8, 506. https://doi.org/10.3389/fneur.2017.00506
Bonnier, G., Roche, A., Romascano, D., Simioni, S., Meskaldji, D., Rotzinger, D., Lin, Y. -C., Menegaz, G., Schluep, M., Pasquier, R. D. u., Sumpf, T. J., Frahm, J., Thiran, J. -P., Krueger, G., & Granziera, C. (2014). Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis. Annals of Clinical and Translational Neurology, 1(6), 423–432. https://doi.org/10.1002/acn3.68
Brown, R. A., Narayanan, S., & Arnold, D. L. (2014). Imaging of repeated episodes of demyelination and remyelination in multiple sclerosis. NeuroImage: Clinical, 6, 20–25. https://doi.org/10.1016/j.nicl.2014.06.009
Callaghan, M. F., Freund, P., Draganski, B., Anderson, E., Cappelletti, M., Chowdhury, R., Diedrichsen, J., FitzGerald, T. H. B., Smittenaar, P., Helms, G., Lutti, A., & Weiskopf, N. (2014). Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiology of Aging, 35(8), 1862–1872. https://doi.org/10.1016/j.neurobiolaging.2014.02.008
Callaghan, M. F., Helms, G., Lutti, A., Mohammadi, S., & Weiskopf, N. (2015). A general linear relaxometry model of r 1 using imaging data: General linear relaxometry model of r1. Magnetic Resonance in Medicine, 73(3), 1309–1314. https://doi.org/10.1002/mrm.25210
Carey, D., Caprini, F., Allen, M., Lutti, A., Weiskopf, N., Rees, G., Callaghan, M. F., & Dick, F. (2018). Quantitative MRI provides markers of intra-, inter-regional, and age-related differences in young adult cortical microstructure. Neuroimage, 182, 429–440. https://doi.org/10.1016/j.neuroimage.2017.11.066
Chawla, S., Kister, I., Sinnecker, T., Wuerfel, J., Brisset, J. -C., Paul, F., & Ge, Y. (2018). Longitudinal study of multiple sclerosis lesions using ultra-high field (7T) multiparametric MR imaging. PLoS ONE, 13(9), e0202918. https://doi.org/10.1371/journal.pone.0202918
Depierreux, F., Parmentier, E., Mackels, L., Baquero, K., Degueldre, C., Balteau, E., Salmon, E., Phillips, C., Bahri, M. A., Maquet, P., & Garraux, G. (2021). Parkinson's disease multimodal imaging: F-DOPA PET, neuromelanin-sensitive and quantitative iron-sensitive MRI. Npj Parkinson's Disease, 7(1), 57. https://doi.org/10.1038/s41531-021-00199-2
De Stefano, N., Giorgio, A., Battaglini, M., Rovaris, M., Sormani, M. P., Barkhof, F., Korteweg, T., Enzinger, C., Fazekas, F., Calabrese, M., Dinacci, D., Tedeschi, G., Gass, A., Montalban, X., Rovira, A., Thompson, A., Comi, G., Miller, D. H., & Filippi, M. (2010). Assessing brain atrophy rates in a large population of untreated multiple sclerosis subtypes. Neurology, 74(23), 1868–1876. https://doi.org/10.1212/WNL.0b013e3181e24136
De Stefano, N., Stromillo, M. L., Giorgio, A., Bartolozzi, M. L., Battaglini, M., Baldini, M., Portaccio, E., Amato, M. P., & Sormani, M. P. (2015). Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. Journal of Neurology, Neurosurgery & Psychiatry, 87(1), 93–99. https://doi.org/10.1136/jnnp-2014-309903
Dousset, V., Gayou, A., Brochet, B., & Caille, J. -M. (1998). Early structural changes in acute MS lesions assessed by serial magnetization transfer studies. Neurology, 51(4), 1150–1155. https://doi.org/10.1212/WNL.51.4.1150
Draganski, B., Ashburner, J., Hutton, C., Kherif, F., Frackowiak, R. S. J., Helms, G., & Weiskopf, N. (2011). Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ). Neuroimage, 55(4), 1423–1434. https://doi.org/10.1016/j.neuroimage.2011.01.052
Dutta, R., & Trapp, B. D. (2007). Pathogenesis of axonal and neuronal damage in multiple sclerosis. Neurology, 68(22), S22–S31. https://doi.org/10.1212/01.wnl.0000275229.13012.32
Edwards, L. J., Kirilina, E., Mohammadi, S., & Weiskopf, N. (2018). Microstructural imaging of human neocortex in vivo. Neuroimage, 182, 184–206. https://doi.org/10.1016/j.neuroimage.2018.02.055
Elkady, A. M., Cobzas, D., Sun, H., Blevins, G., & Wilman, A. H. (2018). Discriminative analysis of regional evolution of iron and myelin/calcium in deep gray matter of multiple sclerosis and healthy subjects: Analysis of iron and myelin in mS. Journal of Magnetic Resonance Imaging, 48(3), 652–668. https://doi.org/10.1002/jmri.26004
Elkady, A. M., Cobzas, D., Sun, H., Seres, P., Blevins, G., & Wilman, A. H. (2019). Five year iron changes in relapsing-remitting multiple sclerosis deep gray matter compared to healthy controls. Multiple Sclerosis and Related Disorders, 33, 107–115. https://doi.org/10.1016/j.msard.2019.05.028
Elskamp, I. j., Knol, D. l., Vrenken, H., Karas, G., Meijerman, A., Filippi, M., Kappos, L., Wagner, K., Pohl, C., Sandbrink, R., Polman, C. H., Uitdehaag, B. M. J., & Barkhof, F. (2010). Lesional magnetization transfer ratio: A feasible outcome for remyelinating treatment trials in multiple sclerosis. Multiple Sclerosis Journal, 16(6), 660–669. https://doi.org/10.1177/1352458510364630
Engström, M., Warntjes, J. B. M., Tisell, A., Landtblom, A. -M., & Lundberg, P. (2014). Multi-parametric representation of voxel-based quantitative magnetic resonance imaging. PLoS ONE, 9(11), e111688. https://doi.org/10.1371/journal.pone.0111688
Enzinger, C., Barkhof, F., Ciccarelli, O., Filippi, M., Kappos, L., Rocca, M. A., Rovira, À., Schneider, T., de Stefano, N., Vrenken, H., Wheeler-Kingshott, C., Wuerfel, J., & Fazekas, F. (2015). Nonconventional MRI and microstructural cerebral changes in multiple sclerosis. Nature Reviews Neurology, 11(12), 676–686. https://doi.org/10.1038/nrneurol.2015.194
Eshaghi, A., Prados, F., Brownlee, W. J., Altmann, D. R., Tur, C., Cardoso, M. J., De Angelis, F., van de Pavert, S. H., Cawley, N., De Stefano, N., Stromillo, M. L., Battaglini, M., Ruggieri, S., Gasperini, C., Filippi, M., Rocca, M. A., Rovira, A., Sastre-Garriga, J., Vrenken, H., … Ciccarelli, O. (2018). Deep gray matter volume loss drives disability worsening in multiple sclerosis: Deep gray matter volume loss. Annals of Neurology, 83(2), 210–222. https://doi.org/10.1002/ana.25145
Fazekas, F., Ropele, S., Enzinger, C., Seifert, T., & Strasser-Fuchs, S. (2002). Quantitative magnetization transfer imaging of pre-lesional white-matter changes in multiple sclerosis. Multiple Sclerosis Journal, 8(6), 479–484. https://doi.org/10.1191/1352458502ms860oa
Filippi, M., Rocca, M. A., Horsfield, M. A., Hametner, S., Geurts, J. J., Comi, G., & Lassmann, H. (2013). Imaging cortical damage and dysfunction in multiple sclerosis. JAMA Neurology, 70(5), 556–564. https://doi.org/10.1001/jamaneurol.2013.1954
Filippi, M., Rocca, M. A., Martino, G., Horsfield, M. A., & Comi, G. (1998). Magnetization transfer changes in the normal appearing white matter precede the appearance of enhancing lesions in patients with multiple sclerosis. Annals of Neurology, 43(6), 809–814. https://doi.org/10.1002/ana.410430616
Frischer, J. M., Bramow, S., Dal-Bianco, A., Lucchinetti, C. F., Rauschka, H., Schmidbauer, M., Laursen, H., Sorensen, P. S., & Lassmann, H. (2009). The relation between inflammation and neurodegeneration in multiple sclerosis brains. Brain, 132(5), 1175–1189. https://doi.org/10.1093/brain/awp070
Frischer, J. M., Weigand, S. D., Guo, Y., Kale, N., Parisi, J. E., Pirko, I., Mandrekar, J., Bramow, S., Metz, I., Brück, W., Lassmann, H., & Lucchinetti, C. F. (2015). Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque: Dynamic nature of MS plaque. Annals of Neurology, 78(5), 710–721. https://doi.org/10.1002/ana.24497
Gracien, R. -M., Jurcoane, A., Wagner, M., Reitz, S. C., Mayer, C., Volz, S., Hof, S. -M., Fleischer, V., Droby, A., Steinmetz, H., Groppa, S., Hattingen, E., Deichmann, R., & Klein, J. C. (2016). Multimodal quantitative MRI assessment of cortical damage in relapsing-remitting multiple sclerosis: Cortical quantitative MRI in RRMS. Journal of Magnetic Resonance Imaging, 44(6), 1600–1607. https://doi.org/10.1002/jmri.25297
Gracien, R. -M., Reitz, S. C., Hof, S. -M., Fleischer, V., Droby, A., Wahl, M., Steinmetz, H., Groppa, S., Deichmann, R., & Klein, J. C. (2017). Longitudinal quantitative MRI assessment of cortical damage in multiple sclerosis: A pilot study: Longitudinal cortical QMRI in mS. Journal of Magnetic Resonance Imaging, 46(5), 1485–1490. https://doi.org/10.1002/jmri.25685
Gracien, R. M., Nürnberger, L., Hok, P., Hof, S. -M., Reitz, S. C., Rüb, U., Steinmetz, H., Hilker-Roggendorf, R., Klein, J. C., Deichmann, R., & Baudrexel, S. (2017). Evaluation of brain ageing: A quantitative longitudinal MRI study over 7 years. European Radiology, 27, 1568–1576. https://doi.org/10.1007/s00330-016-4485-1
Granziera, C., Wuerfel, J., Barkhof, F., Calabrese, M., De Stefano, N., Enzinger, C., Evangelou, N., Granziera, C., Wuerfel, J., Barkhof, F., Calabrese, M., De Stefano, N., Enzinger, C., Evangelou, N., Filippi, M., Geurts, J. J. G., Reich, D. S., Rocca, M. A., Ropele, S., … Kappos, L. (2021). Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis. Brain, 144(5), 1296–1311. https://doi.org/10.1093/brain/awab029
Guillemin, C., Lommers, E., Delrue, G., Gester, E., Maquet, P., & Collette, F. (2022). The complex interplay between trait fatigue and cognition in multiple sclerosis. Psychologica Belgica, 62(1), 108. https://doi.org/10.5334/pb.1125
Haider, L., Zrzavy, T., Hametner, S., Höftberger, R., Bagnato, F., Grabner, G., Trattnig, S., Pfeifenbring, S., Brück, W., & Lassmann, H. (2016). The topograpy of demyelination and neurodegeneration in the multiple sclerosis brain. Brain, 139(3), 807–815. https://doi.org/10.1093/brain/awv398
Hametner, S., Endmayr, V., Deistung, A., Palmrich, P., Prihoda, M., Haimburger, E., Menard, C., Feng, X., Haider, T., Leisser, M., Köck, U., Kaider, A., Höftberger, R., Robinson, S., Reichenbach, J. R., Lassmann, H., Traxler, H., Trattnig, S., & Grabner, G. (2018). The influence of brain iron and myelin on magnetic susceptibility and effective transverse relaxation—A biochemical and histological validation study. Neuroimage, 179, 117–133. https://doi.org/10.1016/j.neuroimage.2018.06.007
Hametner, S., Wimmer, I., Haider, L., Pfeifenbring, S., Brück, W., & Lassmann, H. (2013). Iron and neurodegeneration in the multiple sclerosis brain. Annals of Neurology, 74(6), 848–861. https://doi.org/10.1002/ana.23974
Hayton, T., Furby, J., Smith, K. J., Altmann, D. R., Brenner, R., Chataway, J., Hunter, K., Tozer, D. J., Miller, D. H., & Kapoor, R. (2012). Longitudinal changes in magnetisation transfer ratio in secondary progressive multiple sclerosis: Data from a randomised placebo controlled trial of lamotrigine. Journal of Neurology, 259(3), 505–514. https://doi.org/10.1007/s00415-011-6212-9
Helms, G., Dathe, H., & Dechent, P. (2010). Modeling the influence of TR and excitation flip angle on the magnetization transfer ratio (MTR) in human brain obtained from 3D spoiled gradient echo MRI. Magnetic Resonance in Medicine, 64(1), 177–185. https://doi.org/10.1002/mrm.22379
Hulst, H. E., & Geurts, J. J. G. (2011). Gray matter imaging in multiple sclerosis: What have we learned? BMC Neurology, 11(1), 153. https://doi.org/10.1186/1471-2377-11-153
Khalil, M., Langkammer, C., Pichler, A., Pinter, D., Gattringer, T., Bachmaier, G., Ropele, S., Fuchs, S., Enzinger, C., & Fazekas, F. (2015). Dynamics of brain iron levels in multiple sclerosis: A longitudinal 3T MRI study. Neurology, 84(24), 2396–2402. https://doi.org/10.1212/WNL.0000000000001679
Klein, J. C., Rolinski, M., Griffanti, L., Szewczyk-Krolikowski, K., Baig, F., Ruffmann, C., Groves, A. R., Menke, R. A. L., Hu, M. T., & Mackay, C. (2018). Cortical structural involvement and cognitive dysfunction in early Parkinson's disease. NMR in Biomedicine, 31(4), e3900. https://doi.org/10.1002/nbm.3900
Kolb, H., Absinta, M., Beck, E. S., Ha, S. -K., Song, Y., Norato, G., Cortese, I., Sati, P., Nair, G., & Reich, D. S. (2021). 7T MRI differentiates remyelinated from demyelinated multiple sclerosis lesions. Annals of Neurology, 90(4), 612–626. https://doi.org/10.1002/ana.26194
Kuhlmann, T., Ludwin, S., Prat, A., Antel, J., Brück, W., & Lassmann, H. (2017). An updated histological classification system for multiple sclerosis lesions. Acta Neuropathologica, 133(1), 13–24. https://doi.org/10.1007/s00401-016-1653-y
Kutzelnigg, A., Lucchinetti, C. F., Stadelmann, C., Brück, W., Rauschka, H., Bergmann, M., Schmidbauer, M., Parisi, J. E., & Lassmann, H. (2005). Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain, 128(11), 2705–2712. https://doi.org/10.1093/brain/awh641
Lassmann, H. (2013). Pathology and disease mechanisms in different stages of multiple sclerosis. Journal of the Neurological Sciences, 333(1–2), 1–4. https://doi.org/10.1016/j.jns.2013.05.010
Lassmann, H., Brück, W., & Lucchinetti, C. (2001). Heterogeneity of multiple sclerosis pathogenesis: Implications for diagnosis and therapy. Trends in Molecular Medicine, 7(3), 115–121. https://doi.org/10.1016/S1471-4914(00)01909-2
Lassmann, H., Brück, W., & Lucchinetti, C. F. (2007). The immunopathology of multiple sclerosis: An overview. Brain Pathology, 17(2), 210–218. https://doi.org/10.1111/j.1750-3639.2007.00064.x
Laule, C., Vavasour, I. M., Whittall, K. P., Oger, J., Paty, D. W., Li, D. K. B., MacKay, A. L., & Arnold, D. L. (2003). Evolution of focal and diffuse magnetisation transfer abnormalities in multiple sclerosis. Journal of Neurology, 250(8), 924–931. https://doi.org/10.1007/s00415-003-1115-z
Lema, A., Bishop, C., Malik, O., Mattoscio, M., Ali, R., Nicholas, R., Muraro, P. A., Matthews, P. M., Waldman, A. D., & Newbould, R. D. (2017). A comparison of magnetization transfer methods to assess brain and cervical cord microstructure in multiple sclerosis: MT of brain and c-spine in mS. Journal of Neuroimaging, 27(2), 221–226. https://doi.org/10.1111/jon.12377
Leutritz, T., Seif, M., Helms, G., Samson, R. S., Curt, A., Freund, P., & Weiskopf, N. (2020). Multiparameter mapping of relaxation (R1, R2 *), proton density and magnetization transfer saturation at 3 T: A multicenter dual-vendor reproducibility and repeatability study. Human Brain Mapping, 41(15), 4232–4247. https://doi.org/10.1002/hbm.25122
Levesque, I. R., Giacomini, P. S., Narayanan, S., Ribeiro, L. T., Sled, J. G., Arnold, D. L., & Bruce Pike, G. (2010). Quantitative magnetization transfer and myelin water imaging of the evolution of acute multiple sclerosis lesions: Quantitative MT and t 2 in Acute MS lesions. Magnetic Resonance in Medicine, 63(3), 633–640. https://doi.org/10.1002/mrm.22244
Lieury, A., Chanal, M., Androdias, G., Reynolds, R., Cavagna, S., Giraudon, P., Confavreux, C., & Nataf, S. (2014). Tissue remodeling in periplaque regions of multiple sclerosis spinal cord lesions: Glia remodeling in MS spinal cord. Glia, 62(10), 1645–1658. https://doi.org/10.1002/glia.22705
Lommers, E., Guillemin, C., Reuter, G., Fouarge, E., Delrue, G., Collette, F., Degueldre, C., Balteau, E., Maquet, P., & Phillips, C. (2021). Voxel-Based quantitative MRI reveals spatial patterns of grey matter alteration in multiple sclerosis. Human Brain Mapping, 42(4), 1003–1012. https://doi.org/10.1002/hbm.25274
Lommers, E., Simon, J., Reuter, G., Delrue, G., Dive, D., Degueldre, C., Balteau, E., Phillips, C., & Maquet, P. (2019). Multiparameter MRI quantification of microstructural tissue alterations in multiple sclerosis. NeuroImage: Clinical, 23, 101879. https://doi.org/10.1016/j.nicl.2019.101879
Lorio, S., Fresard, S., Adaszewski, S., Kherif, F., Chowdhury, R., Frackowiak, R. S., Ashburner, J., Helms, G., Weiskopf, N., Lutti, A., & Draganski, B. (2016). New tissue priors for improved automated classification of subcortical brain structures on MRI. Neuroimage, 130, 157–166. https://doi.org/10.1016/j.neuroimage.2016.01.062
Lucchinetti, C., Brück, W., Parisi, J., Scheithauer, B., Rodriguez, M., & Lassmann, H. (2000). Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination. Annals of Neurology, 47(6), 707–717. https://doi.org/10.1002/1531-8249(200006)47:6(707::AID-ANA3)3.0.CO;2-Q
Lutti, A., Hutton, C., Finsterbusch, J., Helms, G., & Weiskopf, N. (2010). Optimization and validation of methods for mapping of the radiofrequency transmit field at 3T: Optimized RF transmit field mapping at 3T. Magnetic Resonance in Medicine, 64(1), 229–238. https://doi.org/10.1002/mrm.22421
Lutti, A., Stadler, J., Josephs, O., Windischberger, C., Speck, O., Bernarding, J., Hutton, C., & Weiskopf, N. (2012). Robust and fast whole brain mapping of the RF transmit field B1 at 7T. PLoS ONE, 7(3), e32379. https://doi.org/10.1371/journal.pone.0032379
Mangeat, G., Govindarajan, S. T., Mainero, C., & Cohen-Adad, J. (2015). Multivariate combination of magnetization transfer, t 2 * and B0 orientation to study the myelo-architecture of the in vivo human cortex. Neuroimage, 119, 89–102. https://doi.org/10.1016/j.neuroimage.2015.06.033
Moon, N., Bullitt, E., Leemput, K., & Gerig, G. (2002). Automatic brain and tumor segmentation. In Medical image computing and computer-assisted intervention—MICCAI 2002. T. Dohi & R. Kikinis (Eds.), Lecture Notes in Computer Science (Vol. 2488, pp. 372–379). Berlin, Heidelberg: Springer Berlin Heidelberg, https://doi.org/10.1007/3-540-45786-0_46
Mottershead, J. P., Schmierer, K., Clemence, M., Thornton, J. S., Scaravilli, F., Barker, G. J., Tofts, P. S., Newcombe, J., Cuzner, M. L., Ordidge, R. J., McDonald, W. I., & Miller, D. H. (2003). High field MRI correlates of myelin content and axonal density in multiple sclerosis. Journal of Neurology, 250(11), 1293–1301. https://doi.org/10.1007/s00415-003-0192-3
Neema, M., Stankiewicz, J., Arora, A., Dandamudi, V. S. R., Batt, C. E., Guss, Z. D., Al-Sabbagh, A., & Bakshi, R. (2007). T1- and T2-based MRI measures of diffuse gray matter and white matter damage in patients with multiple sclerosis. Journal of Neuroimaging, 17, 16S–21S. https://doi.org/10.1111/j.1552-6569.2007.00131.x
Nürnberger, L., Gracien, R. -M., Hok, P., Hof, S. -M., Rüb, U., Steinmetz, H., Hilker, R., Klein, J. C., Deichmann, R., & Baudrexel, S. (2017). Longitudinal changes of cortical microstructure in Parkinson's disease assessed with T1 relaxometry. NeuroImage: Clinical, 13, 405–414. https://doi.org/10.1016/j.nicl.2016.12.025
Pandit, L. (2019). No evidence of disease activity (NEDA) in multiple sclerosis—Shifting the goal posts. Annals of Indian Academy of Neurology, 22(3), 261. https://doi.org/10.4103/aian.AIAN_159_19
Patrikios, P., Stadelmann, C., Kutzelnigg, A., Rauschka, H., Schmidbauer, M., Laursen, H., Sorensen, P. S., Bruck, W., Lucchinetti, C., & Lassmann, H. (2006). Remyelination is extensive in a subset of multiple sclerosis patients. Brain, 129(12), 3165–3172. https://doi.org/10.1093/brain/awl217
Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., Fujihara, K., Havrdova, E., Hutchinson, M., Kappos, L., Lublin, F. D., Montalban, X., O'Connor, P., Sandberg-Wollheim, M., Thompson, A. J., Waubant, E., Weinshenker, B., & Wolinsky, J. S. (2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Annals of Neurology, 69(2), 292–302. https://doi.org/10.1002/ana.22366
Popescu, B. F., Frischer, J. M., Webb, S. M., Tham, M., Adiele, R. C., Robinson, C. A., Fitz-Gibbon, P. D., Weigand, S. D., Metz, I., Nehzati, S., George, G. N., Pickering, I. J., Brück, W., Hametner, S., Lassmann, H., Parisi, J. E., Yong, G., & Lucchinetti, C. F. (2017). Pathogenic implications of distinct patterns of iron and zinc in chronic MS lesions. Acta Neuropathologica, 134(1), 45–64. https://doi.org/10.1007/s00401-017-1696-8
Popescu, G. h., Bogdan, F., & Lucchinetti, C. F. (2012). Meningeal and cortical grey matter pathology in multiple sclerosis. BMC Neurology, 12(1), 11. https://doi.org/10.1186/1471-2377-12-11
Preibisch, C., & Deichmann, R. (2009). Influence of RF spoiling on the stability and accuracy of t 1 mapping based on spoiled FLASH with varying flip angles: Influence of RF spoiling on t 1 mapping. Magnetic Resonance in Medicine, 61(1), 125–135. https://doi.org/10.1002/mrm.21776
Reitz, S. C., Hof, S. -M., Fleischer, V., Brodski, A., Gröger, A., Gracien, R. -M., Droby, A., Steinmetz, H., Ziemann, U., Zipp, F., Deichmann, R., & Klein, J. C. (2017). Multi-parametric quantitative MRI of normal appearing white matter in multiple sclerosis, and the effect of disease activity on t2. Brain Imaging and Behavior, 11(3), 744–753. https://doi.org/10.1007/s11682-016-9550-5
Reuter, G., Lommers, E., Balteau, E., Simon, J., Phillips, C., Scholtes, F., Martin, D., Lombard, A., & Maquet, P. (2020). Multiparameter quantitative histological MRI values in high-grade gliomas: A potential biomarker of tumor progression. Neuro-Oncology Practice, 7(6), 646–655. https://doi.org/10.1093/nop/npaa047
Rocca, M. A., Mastronardo, G., Rodegher, M., Comi, G., & Filippi, M. (1999). Long-term changes of magnetization transfer-derived measures from patients with relapsing-remitting and secondary progressive multiple sclerosis. Ajnr American Journal of Neuroradiology, 20, 21–827.
Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V. J., Zimmer, C., Hemmer, B., & Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. Neuroimage, 59(4), 3774–3783. https://doi.org/10.1016/j.neuroimage.2011.11.032
Schmierer, K., Scaravilli, F., Altmann, D. R., Barker, G. J., & Miller, D. H. (2004). Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Annals of Neurology, 56(3), 407–415. https://doi.org/10.1002/ana.20202
Stankiewicz, J. M., Neema, M., & Ceccarelli, A. (2014). Iron and multiple sclerosis. Neurobiology of Aging, 35, S5158. https://doi.org/10.1016/j.neurobiolaging.2014.03.039
Stevenson, V. L., Parker, G. J. M., Barker, G. J., Birnie, K., Tofts, P. S., Miller, D. H., & Thompson, A. J. (2000). Variations in T1 and T2 relaxation times of normal appearing white matter and lesions in multiple sclerosis. Journal of the Neurological Sciences, 178(2), 81–87. https://doi.org/10.1016/S0022-510X(00)00339-7
Stüber, C., Morawski, M., Schäfer, A., Labadie, C., Wähnert, M., Leuze, C., Streicher, M., Barapatre, N., Reimann, K., Geyer, S., Spemann, D., & Turner, R. (2014). Myelin and iron concentration in the human brain: A quantitative study of MRI contrast. Neuroimage, 93, 95–106. https://doi.org/10.1016/j.neuroimage.2014.02.026
Tabelow, K., Balteau, E., Ashburner, J., Callaghan, M. F., Draganski, B., Helms, G., Kherif, F., Leutritz, T., Lutti, A., Phillips, C., Reimer, E., Ruthotto, L., Seif, M., Weiskopf, N., Ziegler, G., & Mohammadi, S. (2019). HMRI—A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage, 194, 191–210. https://doi.org/10.1016/j.neuroimage.2019.01.029
Thompson, A. J., Banwell, B. L., Barkhof, F., Carroll, W. M., Coetzee, T., Comi, G., Correale, J., Fazekas, F., Filippi, M., Freedman, M. S., Fujihara, K., Galetta, S. L., Hartung, H. P., Kappos, L., Lublin, F. D., Marrie, R. A., Miller, A. E., Miller, D. H., & Montalban, X. (2018). Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. The Lancet Neurology, 17(2), 162–173. https://doi.org/10.1016/S1474-4422(17)30470-2
Trapp, B. D., Peterson, J., Ransohoff, R. M., Rudick, R., Mörk, S., & Bö, L. (1998). Axonal transection in the lesions of multiple sclerosis. New England Journal of Medicine, 338(5), 278–285. https://doi.org/10.1056/NEJM199801293380502
Van Waesberghe, J. H. T. M., Kamphorst, W., De Groot, C. J. A., Van Walderveen, M. A. A., Castelijns, J. A., Ravid, R., Lycklama a Nijeholt, G. J., van der Valk, P., Polman, C. H., Thompson, A. J., & Barkhof, F. (1999). Axonal loss in multiple sclerosis lesions: Magnetic resonance imaging insights into substrates of disability. Annals of Neurology, 46(5), 747–754. https://doi.org/10.1002/1531-8249(199911)46:5(747::AID-ANA10)3.0.CO;2-4
Wang, C. T., Barnett, M., & Barnett, Y. (2019). Imaging the multiple sclerosis lesion: Insights into pathogenesis, progression and repair. Current Opinion in Neurology, 32(3), 338–345. https://doi.org/10.1097/WCO.0000000000000698
Weiskopf, N., Callaghan, M. F., Josephs, O., Lutti, A., & Mohammadi, S. (2014). Estimating the apparent transverse relaxation time (R2*) from images with different contrasts (ESTATICS) reduces motion artifacts. Frontiers in Neuroscience, 8, https://doi.org/10.3389/fnins.2014.00278
Weiskopf, N., Mohammadi, S., Lutti, A., & Callaghan, M. F. (2015). Advances in MRI-Based computational neuroanatomy: From morphometry to in-Vivo histology. Current Opinion in Neurology, 28(4), 313–322. https://doi.org/10.1097/WCO.0000000000000222
Zheng, Y., Lee, J. -C., Rudick, R., & Fisher, E. (2018). Long-term magnetization transfer ratio evolution in multiple sclerosis white matter lesions: Long-term MTR evolution in MS WM lesions. Journal of Neuroimaging, 28(2), 191–198. https://doi.org/10.1111/jon.12480
Zivadinov, R., Reder, A. T., Filippi, M., Minagar, A., Stuve, O., Lassmann, H., Racke, M. K., Dwyer, M. G., Frohman, E. M., & Khan, O. (2008). Mechanisms of action of disease-modifying agents and brain volume changes in multiple sclerosis. Neurology, 71(2), 136–144. https://doi.org/10.1212/01.wnl.0000316810.01120.05
