Semin Neurol 2017; 37(05): 538-545
DOI: 10.1055/s-0037-1607278
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Advances in Imaging Multiple Sclerosis

Antje Bischof
1   Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
2   Department of Neurology and Immunology Clinic, University Hospital Basel, Basel, Switzerland
,
Eduardo Caverzasi
1   Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
,
Christian Cordano
1   Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
,
Stephen L. Hauser
1   Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
,
Roland G. Henry
1   Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California
› Author Affiliations
Further Information

Publication History

Publication Date:
05 December 2017 (online)

Abstract

Neuroimaging has emerged as a powerful technology that has enabled visualization of the impact of multiple sclerosis (MS) on the central nervous system in vivo with unprecedented precision. It has played a crucial role in disentangling the chronology of inflammation and neurodegeneration, developing and understanding mechanisms of novel therapeutics, and diagnosing and monitoring the disease in the clinical setting. However, challenges pertaining to the limited resolution, lack of specificity, inherent technological biases, and processing of increasingly big datasets have hindered comprehensive insights into the pathology underlying disability.

Here, we review the advances in neuroimaging for MS that have moved the field forward in recent years by addressing the above-mentioned issues, thereby enhancing our knowledge of this yet enigmatic disease. We discuss complementary imaging technologies, including magnetic resonance imaging, positron emission tomography, and optical coherence tomography, the most recent tool in the MS imaging armamentarium that holds promise to act as a surrogate of pathological changes in the central nervous system in a more easily accessible way.

 
  • References

  • 1 Polman CH, Reingold SC, Banwell B. , et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011; 69 (02) 292-302
  • 2 Gafson A, Giovannoni G, Hawkes CH. The diagnostic criteria for multiple sclerosis: from Charcot to McDonald. Mult Scler Relat Disord 2012; 1 (01) 9-14
  • 3 Solomon AJ, Klein EP, Bourdette D. “Undiagnosing” multiple sclerosis: the challenge of misdiagnosis in MS. Neurology 2012; 78 (24) 1986-1991
  • 4 Hammond KE, Lupo JM, Xu D. , et al. Development of a robust method for generating 7.0 T multichannel phase images of the brain with application to normal volunteers and patients with neurological diseases. Neuroimage 2008; 39 (04) 1682-1692
  • 5 Hammond KE, Metcalf M, Carvajal L. , et al. Quantitative in vivo magnetic resonance imaging of multiple sclerosis at 7 Tesla with sensitivity to iron. Ann Neurol 2008; 64 (06) 707-713
  • 6 Bagnato F, Hametner S, Pennell D. , et al. 7T MRI-histologic correlation study of low specific absorption rate T2-weighted GRASE Sequences in the detection of white matter involvement in multiple sclerosis. J Neuroimaging 2015; 25 (03) 370-378
  • 7 Dal-Bianco A, Grabner G, Kronnerwetter C. , et al. Slow expansion of multiple sclerosis iron rim lesions: pathology and 7 T magnetic resonance imaging. Acta Neuropathol 2017; 133 (01) 25-42
  • 8 Absinta M, Sati P, Gaitán MI. , et al. Seven-tesla phase imaging of acute multiple sclerosis lesions: a new window into the inflammatory process. Ann Neurol 2013; 74 (05) 669-678
  • 9 Absinta M, Sati P, Schindler M. , et al. Persistent 7-tesla phase rim predicts poor outcome in new multiple sclerosis patient lesions. J Clin Invest 2016; 126 (07) 2597-2609
  • 10 Frischer JM, Weigand SD, Guo Y. , et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann Neurol 2015; 78 (05) 710-721
  • 11 Park E, Gallezot JD, Delgadillo A. , et al. (11)C-PBR28 imaging in multiple sclerosis patients and healthy controls: test-retest reproducibility and focal visualization of active white matter areas. Eur J Nucl Med Mol Imaging 2015; 42 (07) 1081-1092
  • 12 Vowinckel E, Reutens D, Becher B. , et al. PK11195 binding to the peripheral benzodiazepine receptor as a marker of microglia activation in multiple sclerosis and experimental autoimmune encephalomyelitis. J Neurosci Res 1997; 50 (02) 345-353
  • 13 Oh U, Fujita M, Ikonomidou VN. , et al. Translocator protein PET imaging for glial activation in multiple sclerosis. J Neuroimmune Pharmacol 2011; 6 (03) 354-361
  • 14 Banati RB, Newcombe J, Gunn RN. , et al. The peripheral benzodiazepine binding site in the brain in multiple sclerosis: quantitative in vivo imaging of microglia as a measure of disease activity. Brain 2000; 123 (Pt 11): 2321-2337
  • 15 Debruyne JC, Versijpt J, Van Laere KJ. , et al. PET visualization of microglia in multiple sclerosis patients using [11C]PK11195. Eur J Neurol 2003; 10 (03) 257-264
  • 16 Rissanen E, Tuisku J, Rokka J. , et al. In vivo detection of diffuse inflammation in secondary progressive multiple sclerosis using PET imaging and the radioligand 11C-PK11195. J Nucl Med 2014; 55 (06) 939-944
  • 17 Politis M, Giannetti P, Su P. , et al. Increased PK11195 PET binding in the cortex of patients with MS correlates with disability. Neurology 2012; 79 (06) 523-530
  • 18 Cosenza-Nashat M, Zhao ML, Suh HS. , et al. Expression of the translocator protein of 18 kDa by microglia, macrophages and astrocytes based on immunohistochemical localization in abnormal human brain. Neuropathol Appl Neurobiol 2009; 35 (03) 306-328
  • 19 Zrzavy T, Hametner S, Wimmer I, Butovsky O, Weiner HL, Lassmann H. Loss of ‘homeostatic’ microglia and patterns of their activation in active multiple sclerosis. Brain 2017; 140 (07) 1900-1913
  • 20 Herder V, Iskandar CD, Kegler K. , et al. Dynamic changes of microglia/macrophage M1 and M2 polarization in Theiler's murine encephalomyelitis. Brain Pathol 2015; 25 (06) 712-723
  • 21 Miron VE, Boyd A, Zhao JW. , et al. M2 microglia and macrophages drive oligodendrocyte differentiation during CNS remyelination. Nat Neurosci 2013; 16 (09) 1211-1218
  • 22 Filippi M, Rocca MA, Ciccarelli O. , et al; MAGNIMS Study Group. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol 2016; 15 (03) 292-303
  • 23 Traboulsee A, Simon JH, Stone L. , et al. Revised recommendations of the Consortium of MS Centers Task Force for a Standardized MRI Protocol and Clinical Guidelines for the Diagnosis and Follow-Up of Multiple Sclerosis. AJNR Am J Neuroradiol 2016; 37 (03) 394-401
  • 24 Sati P, Oh J, Constable RT. , et al; NAIMS Cooperative. The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol 2016; 12 (12) 714-722
  • 25 Lummel N, Boeckh-Behrens T, Schoepf V, Burke M, Brückmann H, Linn J. Presence of a central vein within white matter lesions on susceptibility weighted imaging: a specific finding for multiple sclerosis?. Neuroradiology 2011; 53 (05) 311-317
  • 26 Samaraweera AP, Clarke MA, Whitehead A. , et al. The central vein sign in multiple sclerosis lesions is present irrespective of the T2* sequence at 3 T. J Neuroimaging 2017; 27 (01) 114-121
  • 27 Magliozzi R, Howell O, Vora A. , et al. Meningeal B-cell follicles in secondary progressive multiple sclerosis associate with early onset of disease and severe cortical pathology. Brain 2007; 130 (Pt 4): 1089-1104
  • 28 Lucchinetti CF, Popescu BF, Bunyan RF. , et al. Inflammatory cortical demyelination in early multiple sclerosis. N Engl J Med 2011; 365 (23) 2188-2197
  • 29 Absinta M, Vuolo L, Rao A. , et al. Gadolinium-based MRI characterization of leptomeningeal inflammation in multiple sclerosis. Neurology 2015; 85 (01) 18-28
  • 30 Absinta M, Cortese IC, Vuolo L. , et al. Leptomeningeal gadolinium enhancement across the spectrum of chronic neuroinflammatory diseases. Neurology 2017; 88 (15) 1439-1444
  • 31 Dousset V, Grossman RI, Ramer KN. , et al. Experimental allergic encephalomyelitis and multiple sclerosis: lesion characterization with magnetization transfer imaging. Radiology 1992; 182 (02) 483-491
  • 32 Campi A, Filippi M, Comi G, Scotti G, Gerevini S, Dousset V. Magnetisation transfer ratios of contrast-enhancing and nonenhancing lesions in multiple sclerosis. Neuroradiology 1996; 38 (02) 115-119
  • 33 Filippi M, Rocca MA, Martino G, Horsfield MA, Comi G. Magnetization transfer changes in the normal appearing white matter precede the appearance of enhancing lesions in patients with multiple sclerosis. Ann Neurol 1998; 43 (06) 809-814
  • 34 Crespy L, Zaaraoui W, Lemaire M. , et al. Prevalence of grey matter pathology in early multiple sclerosis assessed by magnetization transfer ratio imaging. PLoS One 2011; 6 (09) e24969
  • 35 Brown JW, Pardini M, Brownlee WJ. , et al. An abnormal periventricular magnetization transfer ratio gradient occurs early in multiple sclerosis. Brain 2017; 140 (Pt 2): 387-398
  • 36 Rudko DA, Derakhshan M, Maranzano J, Nakamura K, Arnold DL, Narayanan S. Delineation of cortical pathology in multiple sclerosis using multi-surface magnetization transfer ratio imaging. Neuroimage Clin 2016; 12: 858-868
  • 37 Kearney H, Yiannakas MC, Samson RS, Wheeler-Kingshott CA, Ciccarelli O, Miller DH. Investigation of magnetization transfer ratio-derived pial and subpial abnormalities in the multiple sclerosis spinal cord. Brain 2014; 137 (Pt 9): 2456-2468
  • 38 Schmierer K, Tozer DJ, Scaravilli F. , et al. Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain. J Magn Reson Imaging 2007; 26 (01) 41-51
  • 39 Taso M, Girard OM, Duhamel G. , et al. Tract-specific and age-related variations of the spinal cord microstructure: a multi-parametric MRI study using diffusion tensor imaging (DTI) and inhomogeneous magnetization transfer (ihMT). NMR Biomed 2016; 29 (06) 817-832
  • 40 Lema A, Bishop C, Malik O. , et al. A comparison of magnetization transfer methods to assess brain and cervical cord microstructure in multiple sclerosis. J Neuroimaging 2017; 27 (02) 221-226
  • 41 Cercignani M, Symms MR, Schmierer K. , et al. Three-dimensional quantitative magnetisation transfer imaging of the human brain. Neuroimage 2005; 27 (02) 436-441
  • 42 Pierpaoli C, Barnett A, Pajevic S. , et al. Water diffusion changes in Wallerian degeneration and their dependence on white matter architecture. Neuroimage 2001; 13 (6 Pt 1): 1174-1185
  • 43 Wheeler-Kingshott CA, Cercignani M. About “axial” and “radial” diffusivities. Magn Reson Med 2009; 61 (05) 1255-1260
  • 44 Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 2012; 61 (04) 1000-1016
  • 45 By S, Xu J, Box BA, Bagnato FR, Smith SA. Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients. Neuroimage Clin 2017; 15: 333-342
  • 46 Rothman DL, Petroff OA, Behar KL, Mattson RH. Localized 1H NMR measurements of gamma-aminobutyric acid in human brain in vivo. Proc Natl Acad Sci U S A 1993; 90 (12) 5662-5666
  • 47 Mescher M, Merkle H, Kirsch J, Garwood M, Gruetter R. Simultaneous in vivo spectral editing and water suppression. NMR Biomed 1998; 11 (06) 266-272
  • 48 Cawley N, Solanky BS, Muhlert N. , et al. Reduced gamma-aminobutyric acid concentration is associated with physical disability in progressive multiple sclerosis. Brain 2015; 138 (Pt 9): 2584-2595
  • 49 Choi IY, Lee P, Hughes AJ, Denney DR, Lynch SG. Longitudinal changes of cerebral glutathione (GSH) levels associated with the clinical course of disease progression in patients with secondary progressive multiple sclerosis. Mult Scler 2017; 23 (07) 956-962
  • 50 MacKay A, Whittall K, Adler J, Li D, Paty D, Graeb D. In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med 1994; 31 (06) 673-677
  • 51 Laule C, Leung E, Lis DK. , et al. Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult Scler 2006; 12 (06) 747-753
  • 52 Kitzler HH, Su J, Zeineh M. , et al. Deficient MWF mapping in multiple sclerosis using 3D whole-brain multi-component relaxation MRI. Neuroimage 2012; 59 (03) 2670-2677
  • 53 Kolind S, Matthews L, Johansen-Berg H. , et al. Myelin water imaging reflects clinical variability in multiple sclerosis. Neuroimage 2012; 60 (01) 263-270
  • 54 Vargas WS, Monohan E, Pandya S. , et al. Measuring longitudinal myelin water fraction in new multiple sclerosis lesions. Neuroimage Clin 2015; 9: 369-375
  • 55 Lansley J, Mataix-Cols D, Grau M, Radua J, Sastre-Garriga J. Localized grey matter Atrophy in multiple sclerosis: a meta-analysis of voxel-based morphometry studies and associations with functional disability. Neurosci Biobehav Rev 2013; 37 (05) 819-830
  • 56 Bishop CA, Newbould RD, Lee JS. , et al. Analysis of ageing-associated grey matter volume in patients with multiple sclerosis shows excess atrophy in subcortical regions. Neuroimage Clin 2016; 13: 9-15
  • 57 Vidal-Jordana A, Sastre-Garriga J, Pareto D. , et al. Brain atrophy 15 years after CIS: Baseline and follow-up clinico-radiological correlations. Mult Scler 2017; (Epub ahead of print) DOI: 10.1177/1352458517707070.
  • 58 Kappos L, De Stefano N, Freedman MS. , et al. Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing-remitting multiple sclerosis. Mult Scler 2016; 22 (10) 1297-1305
  • 59 Schlaeger R, Papinutto N, Panara V. , et al. Spinal cord gray matter atrophy correlates with multiple sclerosis disability. Ann Neurol 2014; 76 (04) 568-580
  • 60 Daams M, Weiler F, Steenwijk MD. , et al. Mean upper cervical cord area (MUCCA) measurement in long-standing multiple sclerosis: relation to brain findings and clinical disability. Mult Scler 2014; 20 (14) 1860-1865
  • 61 Kearney H, Rocca MA, Valsasina P. , et al. Magnetic resonance imaging correlates of physical disability in relapse onset multiple sclerosis of long disease duration. Mult Scler 2014; 20 (01) 72-80
  • 62 Cawley N, Tur C, Prados F. , et al. Spinal cord atrophy as a primary outcome measure in phase II trials of progressive multiple sclerosis. Mult Scler 2017; (Epub ahead of print) DOI: 10.1177/1352458517709954.
  • 63 Lukas C, Knol DL, Sombekke MH. , et al. Cervical spinal cord volume loss is related to clinical disability progression in multiple sclerosis. J Neurol Neurosurg Psychiatry 2015; 86 (04) 410-418
  • 64 De Stefano N, Stromillo ML, Giorgio A. , et al. Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J Neurol Neurosurg Psychiatry 2016; 87 (01) 93-99
  • 65 Schlaeger R, Papinutto N, Zhu AH. , et al. Association between thoracic spinal cord gray matter atrophy and disability in multiple sclerosis. JAMA Neurol 2015; 72 (08) 897-904
  • 66 Bagci AM, Shahidi M, Ansari R, Blair M, Blair NP, Zelkha R. Thickness profiles of retinal layers by optical coherence tomography image segmentation. Am J Ophthalmol 2008; 146 (05) 679-687
  • 67 Podoleanu AG, Rosen RB. Combinations of techniques in imaging the retina with high resolution. Prog Retin Eye Res 2008; 27 (04) 464-499
  • 68 Frohman EM, Costello F, Stüve O. , et al. Modeling axonal degeneration within the anterior visual system: implications for demonstrating neuroprotection in multiple sclerosis. Arch Neurol 2008; 65 (01) 26-35
  • 69 Grazioli E, Zivadinov R, Weinstock-Guttman B. , et al. Retinal nerve fiber layer thickness is associated with brain MRI outcomes in multiple sclerosis. J Neurol Sci 2008; 268 (1–2): 12-17
  • 70 Henderson AP, Trip SA, Schlottmann PG. , et al. An investigation of the retinal nerve fibre layer in progressive multiple sclerosis using optical coherence tomography. Brain 2008; 131 (Pt 1): 277-287
  • 71 Ratchford JN, Saidha S, Sotirchos ES. , et al. Active MS is associated with accelerated retinal ganglion cell/inner plexiform layer thinning. Neurology 2013; 80 (01) 47-54
  • 72 Dörr J, Wernecke KD, Bock M. , et al. Association of retinal and macular damage with brain atrophy in multiple sclerosis. PLoS One 2011; 6 (04) e18132
  • 73 Saidha S, Al-Louzi O, Ratchford JN. , et al. Optical coherence tomography reflects brain atrophy in multiple sclerosis: A four-year study. Ann Neurol 2015; 78 (05) 801-813
  • 74 Martinez-Lapiscina EH, Arnow S, Wilson JA. , et al; IMSVISUAL consortium. Retinal thickness measured with optical coherence tomography and risk of disability worsening in multiple sclerosis: a cohort study. Lancet Neurol 2016; 15 (06) 574-584
  • 75 Saidha S, Sotirchos ES, Ibrahim MA. , et al. Microcystic macular oedema, thickness of the inner nuclear layer of the retina, and disease characteristics in multiple sclerosis: a retrospective study. Lancet Neurol 2012; 11 (11) 963-972
  • 76 Knier B, Schmidt P, Aly L. , et al. Retinal inner nuclear layer volume reflects response to immunotherapy in multiple sclerosis. Brain 2016; (Epub ahead of print) DOI: 10.1093/brain/aww219.
  • 77 Button J, Al-Louzi O, Lang A. , et al. Disease-modifying therapies modulate retinal atrophy in multiple sclerosis: a retrospective study. Neurology 2017; 88 (06) 525-532
  • 78 Klein A, Ghosh SS, Bao FS. , et al. Mindboggling morphometry of human brains. PLOS Comput Biol 2017; 13 (02) e1005350
  • 79 Rovira À, Wattjes MP, Tintoré M. , et al; MAGNIMS study group. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis-clinical implementation in the diagnostic process. Nat Rev Neurol 2015; 11 (08) 471-482
  • 80 Wattjes MP, Rovira À, Miller D. , et al; MAGNIMS study group. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis--establishing disease prognosis and monitoring patients. Nat Rev Neurol 2015; 11 (10) 597-606
  • 81 Cotton F, Kremer S, Hannoun S, Vukusic S, Dousset V. ; Imaging Working Group of the Observatoire Français de la Sclérose en Plaques. OFSEP, a nationwide cohort of people with multiple sclerosis: consensus minimal MRI protocol. J Neuroradiol 2015; 42 (03) 133-140
  • 82 Keshavan A, Paul F, Beyer MK. , et al; International Multiple Sclerosis Genetics Consortium. Electronic address: AIVINSON@PARTNERS.ORG. Power estimation for non-standardized multisite studies. Neuroimage 2016; 134: 281-294
  • 83 Biberacher V, Schmidt P, Keshavan A. , et al. Intra- and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis. Neuroimage 2016; 142: 188-197
  • 84 Rocca MA, Battaglini M, Benedict RH. , et al. Brain MRI atrophy quantification in MS: From methods to clinical application. Neurology 2017; 88 (04) 403-413
  • 85 Giannetti P, Politis M, Su P. , et al. Increased PK11195-PET binding in normal-appearing white matter in clinically isolated syndrome. Brain 2015; 138 (Pt 1): 110-119
  • 86 Endres CJ, Pomper MG, James M. , et al. Initial evaluation of 11C-DPA-713, a novel TSPO PET ligand, in humans. J Nucl Med 2009; 50 (08) 1276-1282
  • 87 Takano A, Piehl F, Hillert J. , et al. In vivo TSPO imaging in patients with multiple sclerosis: a brain PET study with [18F]FEDAA1106. EJNMMI Res 2013; 3 (01) 30
  • 88 Colasanti A, Guo Q, Muhlert N. , et al. In vivo assessment of brain white matter inflammation in multiple sclerosis with (18)F-PBR111 PET. J Nucl Med 2014; 55 (07) 1112-1118
  • 89 Gerwien H, Hermann S, Zhang X. , et al. Imaging matrix metalloproteinase activity in multiple sclerosis as a specific marker of leukocyte penetration of the blood-brain barrier. Sci Transl Med 2016; 8 (364) 364ra152
  • 90 Dickens AM, Vainio S, Marjamäki P. , et al. Detection of microglial activation in an acute model of neuroinflammation using PET and radiotracers 11C-(R)-PK11195 and 18F-GE-180. J Nucl Med 2014; 55 (03) 466-472
  • 91 Mohan S, Verma A, Lim CC, Hui F, Kumar S. Lipid resonance on in vivo proton mr spectroscopy: value of other metabolites in differential diagnosis. Neuroradiol J 2010; 23 (03) 269-278
  • 92 Muhlert N, Atzori M, De Vita E. , et al. Memory in multiple sclerosis is linked to glutamate concentration in grey matter regions. J Neurol Neurosurg Psychiatry 2014; 85 (08) 833-839
  • 93 Kirov II, Liu S, Tal A. , et al. Proton MR spectroscopy of lesion evolution in multiple sclerosis: steady-state metabolism and its relationship to conventional imaging. Hum Brain Mapp 2017; 38 (08) 4047-4063
  • 94 Llufriu S, Kornak J, Ratiney H. , et al. Magnetic resonance spectroscopy markers of disease progression in multiple sclerosis. JAMA Neurol 2014; 71 (07) 840-847
  • 95 Wolinsky JS, Narayana PA, Fenstermacher MJ. Proton magnetic resonance spectroscopy in multiple sclerosis. Neurology 1990; 40 (11) 1764-1769
  • 96 Hattingen E, Magerkurth J, Pilatus U, Hübers A, Wahl M, Ziemann U. Combined (1)H and (31)P spectroscopy provides new insights into the pathobiochemistry of brain damage in multiple sclerosis. NMR Biomed 2011; 24 (05) 536-546