Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Imaging outcomes for neuroprotection and repair in multiple sclerosis trials

Nature Reviews Neurology volume 5, pages 256–266 (2009)Cite this article

Abstract

Multiple sclerosis (MS) is commonly regarded as an inflammatory disease, but it also has a neurodegenerative component, which represents an additional target for treatment. The use of MRI to evaluate the inflammatory disease component in 'proof-of concept' clinical trials is well established, but no systematic assessment of imaging outcomes to evaluate neuroprotection or repair in MS has been performed. In this Review, we examine the potential of traditional and novel imaging parameters to serve as primary outcomes in phase II clinical trials of neuroprotective and reparative strategies in MS. We present the conclusions of an international meeting of imaging, clinical and statistical experts, as well as a review of relevant literature. The available imaging techniques are appraised in five categories of performance: pathological specificity, reproducibility, sensitivity to change, clinical relevance, and response to treatment. At present, the three most promising primary outcomes in phase II trials of neuroprotective and/or reparative strategies in MS are: changes in whole-brain volume to gauge general cerebral atrophy; T1 hypointensity and magnetization transfer ratio to monitor the evolution of lesion damage; and optical coherence tomography findings to evaluate the anterior visual pathway. Power calculations show that these outcome measures can be applied with attainable sample sizes.

Key Points

  • Multiple sclerosis (MS) is not only an inflammatory demyelinating disease; it also involves neurodegeneration, which starts early in the disease process

  • Development of new neuroprotective and reparative treatments for MS has been hampered by lack of sensitive clinical response criteria

  • Imaging outcomes provide the best current in vivo measures of neuroprotection, and possibly also of repair, in MS

  • Brain-volume change on serial MRI provides a sensitive overall measure of neuroprotection in MS trials over the course of a year

  • Magnetization transfer ratio and T1 hypointensity provide lesional measures of neuroprotection and repair over the course of 6–9 months

  • Optical coherence tomography is a robust measure of axonal damage in the anterior visual pathways

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Measurement of whole-brain atrophy by sequential MRI.
Figure 2: Evolution of new lesions into persistent black holes on sequential T1-weighted spin-echo MRI in two patients with multiple sclerosis.
Figure 3: Assessment of RNFL thickness by OCT.
Figure 4: Criteria supporting the value of imaging outcomes during clinical development of a new agent.

Similar content being viewed by others

References

  1. Compston, A. & Coles, A. Multiple sclerosis. Lancet 372, 1502–1517 (2008).

    Article  CAS  Google Scholar 

  2. Lopez-Diego, R. S. & Weiner, H. L. Novel therapeutic strategies for multiple sclerosis—a multifaceted adversary. Nat. Rev. Drug Discov. 7, 909–925 (2008).

    Article  CAS  Google Scholar 

  3. Miller, D. H. et al. Guidelines for the use of magnetic resonance techniques in monitoring the treatment of multiple sclerosis. US National MS Society Task Force. Ann. Neurol. 39, 6–16 (1996).

    Article  CAS  Google Scholar 

  4. Edan, G. et al. Therapeutic effect of mitoxantrone combined with methylprednisolone in multiple sclerosis: a randomised multicentre study of active disease using MRI and clinical criteria. J. Neurol. Neurosurg. Psychiatry 62, 112–118 (1997).

    Article  CAS  Google Scholar 

  5. Miller, D. H. et al. A controlled trial of natalizumab for relapsing multiple sclerosis. N. Engl. J. Med. 348, 68–72 (2003).

    Article  Google Scholar 

  6. Polman, C. et al. Treatment with laquinimod reduces development of active MRI lesions in relapsing MS. Neurology 64, 987–991 (2005).

    Article  CAS  Google Scholar 

  7. Kappos, L. et al. Oral fingolimod (FTY720) for relapsing multiple sclerosis. N. Engl. J. Med. 255, 1124–1140 (2006).

    Article  Google Scholar 

  8. O'Connor, P. W. et al. A phase II study of the safety and efficacy of teriflunomide in multiple sclerosis with relapses. Neurology 66, 894–900 (2006).

    Article  CAS  Google Scholar 

  9. Comi, G. et al. Effect of laquinimod on MRI-monitored disease activity in patients with relapsing–remitting multiple sclerosis: a multicentre, randomized, double-blind, placebo-controlled phase IIb study. Lancet 371, 2085–2092 (2008).

    Article  CAS  Google Scholar 

  10. Kappos, L. et al. Efficacy and safety of oral fumarate in patients with relapsing-remitting multiple sclerosis: a multicentre, randomised, double-blind, placebo-controlled phase IIb study. Lancet 372, 1463–1472 (2008).

    Article  CAS  Google Scholar 

  11. Zivadinov, R. et al. Mechanisms of action of disease-modifying agents and brain volume changes in multiple sclerosis. Neurology 71, 136–144 (2008).

    Article  CAS  Google Scholar 

  12. Simon, J. H. Brain atrophy in multiple sclerosis: what we know and would like to know. Mult. Scler. 12, 679–687 (2006).

    Article  CAS  Google Scholar 

  13. Anderson, V. M., Bartlett, J. W., Fox, N. C., Fisniku, L. & Miller, D. H. Detecting treatment effects on brain atrophy in relapsing remitting multiple sclerosis: sample size estimates. J. Neurol. 254, 1588–1594 (2007).

    Article  Google Scholar 

  14. Altmann, D. R. et al. Sample sizes for brain atrophy outcomes in trials for secondary progressive multiple sclerosis. Neurology 72, 595–601 (2009).

    Article  CAS  Google Scholar 

  15. Chen, J. T. et al. Brain atrophy after immunoablation and stem cell transplantation in multiple sclerosis. Neurology 66, 1935–1937 (2006).

    Article  CAS  Google Scholar 

  16. Tiberio, M. et al. Gray and white matter volume changes in early RRMS: a 2-year longitudinal study. Neurology 64, 1001–1007 (2005).

    Article  CAS  Google Scholar 

  17. Sastre-Garriga, J. et al. Metabolic changes in normal-appearing gray and white matter are linked with disability in early primary progressive multiple sclerosis. Arch. Neurol. 62, 569–573 (2005).

    Article  CAS  Google Scholar 

  18. Fisher, E., Lee, J.-C., Nakamura, K. & Rudick, R. A. Gray matter atrophy in multiple sclerosis: a longitudinal study. Ann. Neurol. 64, 255–265 (2008).

    Article  Google Scholar 

  19. Nakamura, K. & Fisher, E. Segmentation of brain magnetic resonance images for measurement of gray matter atrophy in multiple sclerosis patients. Neuroimage 44, 769–776 (2009).

    Article  Google Scholar 

  20. Barkhof, F. Assessing treatment effects on axonal loss – evidence from MRI monitored clinical trials. J. Neurol. 251 (Suppl. 4), IV6–IV12 (2004).

    Google Scholar 

  21. Bö, L., Geurts, J. J., Ravid, R. & Barkhof, F. Magnetic resonance imaging as a tool to examine the neuropathology of multiple sclerosis. Neuropath. Appl. Neurobiol. 30, 106–117 (2004).

    Article  Google Scholar 

  22. Schmierer, K., Scaravilli, F., Altmann, D. R., Barker, G. J. & Miller, D. H. Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann. Neurol. 56, 407–415 (2004).

    Article  Google Scholar 

  23. Schmierer, K. et al. Quantitative magnetization transfer imaging in postmortem multiple sclerosis brain. J. Magn. Reson. Imaging 26, 41–51 (2007).

    Article  Google Scholar 

  24. Giacomini, P. S. & Arnold, D. L. Non-conventional MRI techniques for measuring neuroprotection, repair and plasticity in multiple sclerosis. Curr. Opin. Neurol. 21, 272–277 (2008).

    Article  Google Scholar 

  25. van den Elskamp, I. J. et al. Persistent T1 hypointensity as an MRI marker for treatment efficacy in multiple sclerosis. Mult. Scler. 14, 464–469 (2008).

    Article  Google Scholar 

  26. MacKay, A. L. et al. MR relaxation in multiple sclerosis. Neuroimaging Clin. N. Am. 19, 1–26 (2009).

    Article  CAS  Google Scholar 

  27. Bjartmar, C. & Trapp, B. D. Axonal degeneration and progressive neurological disability in multiple sclerosis. Neurotox. Res. 5, 157–164 (2003).

    Article  Google Scholar 

  28. Ceccarelli, A. et al. Normal-appearing white and grey matter damage in MS. A volumetric and diffusion tensor MRI study at 3.0 Tesla. J. Neurol. 254, 513–518 (2007).

    Article  Google Scholar 

  29. Davies, G. R. et al. Normal-appearing grey and white matter T1 abnormality in early relapsing–remitting multiple sclerosis: a longitudinal study. Mult. Scler. 13, 169–177 (2007).

    Article  CAS  Google Scholar 

  30. Roosendaal, S. D. et al. Regional DTI differences in multiple sclerosis patients. Neuroimage 44, 1397–1403 (2009).

    Article  CAS  Google Scholar 

  31. Reich, D. S. et al. Damage to the optic radiation in multiple sclerosis is associated with retinal injury and low contrast visual acuity. Arch. Neurol. in press (2009).

  32. Miller, D. H., Thompson, A. J. & Filippi, M. Magnetic resonance studies of abnormalities in the normal appearing white matter and grey matter in multiple sclerosis. J. Neurol. 250, 1407–1419 (2003).

    Article  CAS  Google Scholar 

  33. Rovaris, M. et al. Optimizing diffusion measurements for large-scale, multicenter multiple sclerosis trials: a pan-European study. Mult. Scler. 14 (Suppl.), S216 (2008).

    Google Scholar 

  34. Narayanan, S. et al. Axonal metabolic recovery in multiple sclerosis patients treated with interferon beta-1b. J. Neurol. 248, 979–986 (2001).

    Article  CAS  Google Scholar 

  35. Khan, O. et al. Axonal metabolic recovery and potential neuroprotective effect of glatiramer acetate in relapsing-remitting multiple sclerosis. Mult. Scler. 11, 646–651 (2005).

    Article  CAS  Google Scholar 

  36. Kutzelnigg, A. et al. Cortical demyelination and diffuse white matter injury in multiple sclerosis. Brain 128, 2705–2712 (2005).

    Article  Google Scholar 

  37. Stevenson, V. L. et al. Spinal cord atrophy and disability in MS: a longitudinal study. Neurology 51, 234–238 (1998).

    Article  CAS  Google Scholar 

  38. Gilmore, C. et al. Spinal cord grey matter lesions in multiple sclerosis detected by post-mortem high field MR imaging. Mult. Scler. 15, 180–188 (2009).

    Article  CAS  Google Scholar 

  39. Bot, J. C. et al. Spinal cord abnormalities in recently diagnosed MS patients: added value of spinal MRI examination. Neurology 62, 226–233 (2004).

    Article  CAS  Google Scholar 

  40. Jacobi, C. et al. Prospective combined brain and spinal cord MRI in clinically isolated syndromes and possible early multiple sclerosis: impact on dissemination in space and time. Eur. J. Neurol. 15, 1359–1364 (2008).

    Article  CAS  Google Scholar 

  41. Agosta, F. et al. In vivo assessment of cervical cord damage in MS patients: a longitudinal diffusion tensor MRI study. Brain 130, 2211–2219 (2007).

    Article  CAS  Google Scholar 

  42. Ciccarelli, O. et al. Spinal cord spectroscopy and diffusion-based tractography to assess acute disability in multiple sclerosis. Brain 130, 2220–2231 (2007).

    Article  CAS  Google Scholar 

  43. DeBoy, C. A. et al. High resolution diffusion tensor imaging of axonal damage in focal inflammatory and demyelinating lesions in rat spinal cord. Brain 130, 2199–2210 (2007).

    Article  Google Scholar 

  44. Budde, M. D. et al. Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magn. Reson. Med. 57, 688–695 (2007).

    Article  Google Scholar 

  45. Sun, S. W., Liang, H. F., Cross, A. H. & Song, S. K. Evolving Wallerian degeneration after transient retinal ischemia in mice characterized by diffusion tensor imaging. Neuroimage 40, 1–10 (2008).

    Article  CAS  Google Scholar 

  46. Sun, S. W., Liang, H. F., Xie, M., Oyoyo, U. & Lee, A. Fixation, not death, reduces sensitivity of DTI in detecting optic nerve damage. Neuroimage 44, 611–619 (2009).

    Article  Google Scholar 

  47. Kalkers, N. F., Barkhof, F., Bergers, E., van Schijndel, R. & Polman, C. H. The effect of the neuroprotective agent riluzole on MRI parameters in primary progressive multiple sclerosis: a pilot study. Mult. Scler. 8, 532–533 (2002).

    Article  CAS  Google Scholar 

  48. Balcer, L. J. et al. New low-contrast vision charts: reliability and test characteristics in patients with multiple sclerosis. Mult. Scler. 6, 163–171 (2000).

    Article  CAS  Google Scholar 

  49. Baier, M. L. et al. Low-contrast letter acuity testing captures visual dysfunction in patients with multiple sclerosis. Neurology 64, 992–995 (2005).

    Article  CAS  Google Scholar 

  50. Wu, G. F. et al. Relation of vision to global and regional brain MRI in multiple sclerosis. Neurology 69, 2128–2135 (2007).

    Article  CAS  Google Scholar 

  51. Balcer, L. J. et al. Natalizumab reduces visual loss in patients with relapsing multiple sclerosis. Neurology 68, 1299–1304 (2007).

    Article  CAS  Google Scholar 

  52. Trip, S. A. et al. Retinal nerve fiber layer axonal loss and visual dysfunction in optic neuritis. Ann. Neurol. 58, 383–391 (2005).

    Article  Google Scholar 

  53. Fisher, J. B. et al. Relation of visual function to retinal nerve fiber layer thickness in multiple sclerosis. Ophthalmology 113, 324–332 (2006).

    Article  Google Scholar 

  54. Pulicken, M. et al. Optical coherence tomography and disease subtype in multiple sclerosis. Neurology 69, 2085–2092 (2007).

    Article  CAS  Google Scholar 

  55. Gordon-Lipkin, E. et al. Retinal nerve fiber layer is associated with brain atrophy in multiple sclerosis. Neurology 69, 1603–1609 (2007).

    Article  CAS  Google Scholar 

  56. Cettomai, D. et al. Reproducibility of optical coherence tomography in multiple sclerosis. Arch. Neurol. 65, 1218–1222 (2008).

    Article  Google Scholar 

  57. Costello, F. et al. Quantifying axonal loss after optic neuritis with optical coherence tomography. Ann. Neurol. 59, 963–969 (2006).

    Article  Google Scholar 

  58. Ciccarelli, O. et al. Optic radiation changes after optic neuritis detected by tractography-based group mapping. Hum. Brain Mapp. 25, 308–316 (2005).

    Article  Google Scholar 

  59. Audoin, B. et al. Selective magnetization transfer ratio decrease in the visual cortex following optic neuritis. Brain 129, 1031–1039 (2006).

    Article  Google Scholar 

  60. Hickman, S. J. et al. Optic nerve diffusion measurement from diffusion-weighted imaging in optic neuritis. Am. J. Neuroradiol. 26, 951–956 (2005).

    Google Scholar 

  61. Trip, S. A. et al. Optic nerve atrophy and retinal nerve fibre layer thinning following optic neuritis: evidence that axonal loss is a substrate of MRI-detected atrophy. Neuroimage 31, 286–293 (2006).

    Article  Google Scholar 

  62. Trip, S. A. et al. Optic nerve magnetization transfer imaging and measures of axonal loss and demyelination in optic neuritis. Mult. Scler. 13, 875–879 (2007).

    Article  CAS  Google Scholar 

  63. Wegner, C. et al. Relating functional changes during hand movement to clinical parameters in patients with multiple sclerosis in a multi-centre fMRI study. Eur. J. Neurol. 15, 113–122 (2008).

    Article  CAS  Google Scholar 

  64. Bosnell, R. et al. Reproducibility of fMRI in the clinical setting: implications for trial designs. Neuroimage 42, 603–610 (2008).

    Article  CAS  Google Scholar 

  65. Filippi, M. & Rocca, M. A. Application of fMRI to the study of multiple sclerosis: an update. Future Neurol. 3, 141–151 (2008).

    Article  Google Scholar 

  66. Parry, A. M., Scott, R. B., Palace, J., Smith, S. & Matthews, P. M. Potentially adaptive functional changes in cognitive processing for patients with multiple sclerosis and their acute modulation by rivastigmine. Brain 126, 2750–2760 (2003).

    Article  Google Scholar 

  67. Rocca, M. A. et al. fMRI changes in relapsing–remitting multiple sclerosis patients complaining of fatigue after IFNbeta-1a injection. Hum. Brain Mapp. 28, 373–382 (2007).

    Article  Google Scholar 

  68. Xiang, Z. et al. Detection of myelination using a novel histological probe. J. Histochem. Cytochem. 53, 1511–1516 (2005).

    Article  CAS  Google Scholar 

  69. Stankoff, B. et al. Imaging of CNS myelin by positron-emission tomography. Proc. Natl Acad. Sci. USA 103, 9304–9309 (2006).

    Article  CAS  Google Scholar 

  70. Wu, C. et al. A novel fluorescent probe that is brain permeable and selectively binds to myelin. J. Histochem. Cytochem. 54, 997–1004 (2006).

    Article  CAS  Google Scholar 

  71. Vowinckel, E. 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. 50, 345–353 (1997).

    Article  CAS  Google Scholar 

  72. Banati, R. B. 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 123, 2321–2337 (2000).

    Article  Google Scholar 

  73. Debruyne, J. C. et al. PET visualization of microglia in multiple sclerosis patients using [11C]PK11195. Eur. J. Neurol. 10, 257–264 (2003).

    Article  CAS  Google Scholar 

  74. Rashid, W. et al. Abnormalities of cerebral perfusion in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 75, 1288–1293 (2004).

    Article  CAS  Google Scholar 

  75. Adhya, S. et al. Pattern of hemodynamic impairment in multiple sclerosis: dynamic susceptibility contrast perfusion MR imaging at 3.0 T. Neuroimage 33, 1029–1035 (2006).

    Article  Google Scholar 

  76. Wuerfel, J., Paul, F. & Zipp, F. Cerebral blood perfusion changes in multiple sclerosis. J. Neurol. Sci. 259, 16–20 (2007).

    Article  Google Scholar 

  77. Inglese, M. et al. Perfusion magnetic resonance imaging correlates of neuropsychological impairment in multiple sclerosis. J. Cereb. Blood Flow Metab. 28, 164–171 (2008).

    Article  Google Scholar 

  78. Ben-Hur, T. et al. Serial in vivo MR tracking of magnetically labeled neural spheres transplanted in chronic EAE mice. Magn. Reson. Med. 57, 164–171 (2007).

    Article  Google Scholar 

  79. Zhu J., Zhou, L. & XingWu, F. Tracking neural stem cells in patients with brain trauma. N. Engl. J. Med. 355, 2376–2378 (2006).

    Article  CAS  Google Scholar 

  80. Walczak, P. & Bulte, J. W. M. The role of noninvasive cellular imaging in developing cell-based therapies for neurodegenerative disorders. Neurodegenerative Dis. 4, 306–313 (2007).

    Article  Google Scholar 

  81. Gilad, A. A. et al. Artificial reporter gene providing MRI contrast based on proton exchange. Nat. Biotechnol. 25, 217–219 (2007).

    Article  CAS  Google Scholar 

  82. Vavasour, I. M. et al. Longitudinal changes in myelin water fraction in two MS patients with active disease. J. Neurol. Sci. 276, 49–53 (2009).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The workshop on Imaging Outcomes for Protection and Repair in Multiple Sclerosis in Amsterdam, The Netherlands (28–29 August 2008) was supported by the National Multiple Sclerosis Society (New York, USA) as an activity of its International Advisory Committee on Clinical Trials in Multiple Sclerosis. The Amsterdam MS Center is supported by the Dutch Foundation for MS Research (Voorschoten, The Netherlands). The Johns Hopkins Multiple Sclerosis Center is supported by the National MS Society (USA), the Nancy Davis Foundation and the National Institute of Neurological Disorders and Stroke, NIH (Bethesda, MD, USA). The NMR Research Unit at the Institute of Neurology, London, UK is supported by the Multiple Sclerosis Society of Great Britain and Northern Ireland. We thank Hanneke Hulst for providing Figure 2 and Paul Matthews for providing a template from which Figure 4 was adapted. We also thank Douglas Arnold and Laura Balcer for providing details about the power calculations.

Author information

Authors and Affiliations

  1. Department of Radiology and Amsterdam MS Center, VU University Medical Center, Amsterdam, The Netherlands

    Frederik Barkhof

  2. Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA

    Peter A. Calabresi

  3. Department of Neuroinflammation and NMR Research Unit, Institute of Neurology, University College London, London, UK

    David H. Miller

  4. National MS Society and Scientific and Clinical Review Associates, LLC, New York, NY, USA

    Stephen C. Reingold

Authors
  1. Frederik Barkhof
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter A. Calabresi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David H. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Stephen C. Reingold
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frederik Barkhof.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Box 1

Participant list for Imaging Outcomes for Protection and Repair in Multiple Sclerosis, 28–29 August 2008, Amsterdam, The Netherlands (DOC 32 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Barkhof, F., Calabresi, P., Miller, D. et al. Imaging outcomes for neuroprotection and repair in multiple sclerosis trials. Nat Rev Neurol 5, 256–266 (2009). https://doi.org/10.1038/nrneurol.2009.41

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrneurol.2009.41

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing