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Dynamic Imaging of Individual Remyelination Profiles in Multiple Sclerosis

Abstract

Background: Quantitative in vivo imaging of myelin loss and repair in patients with multiple sclerosis (MS) is essential to understand the pathogenesis of the disease and to evaluate promyelinating therapies. Selectively binding myelin in the central nervous system white matter, Pittsburgh compound B ([ C]PiB) can be used as a positron emission tomography (PET) tracer to explore myelin dynamics in MS.

Methods: Patients with active relapsing-remitting MS (n = 20) and healthy controls (n = 8) were included in a longitudinal trial combining PET with [ C]PiB and magnetic resonance imaging. Voxel-wise maps of [ C]PiB distribution volume ratio, reflecting myelin content, were derived. Three dynamic indices were calculated for each patient: the global index of myelin content change; the index of demyelination; and the index of remyelination.

Results: At baseline, there was a progressive reduction in [ C]PiB binding from the normal-appearing white matter to MS lesions, reflecting a decline in myelin content. White matter lesions were characterized by a centripetal decrease in the tracer binding at the voxel level. During follow-up, high between-patient variability was found for all indices of myelin content change. Dynamic remyelination was inversely correlated with clinical disability (p = 0.006 and beta-coefficient = -0.67 with the Expanded Disability Status Scale; p = 0.003 and beta-coefficient = -0.68 with the MS Severity Scale), whereas no significant clinical correlation was found for the demyelination index.

Interpretation: [ C]PiB PET allows quantification of myelin dynamics in MS and enables stratification of patients depending on their individual remyelination potential, which significantly correlates with clinical disability. This technique should be considered to assess novel promyelinating drugs. Ann Neurol 2016;79:726-738.

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References
1.
Chard D, Jackson J, Miller D, Wheeler-Kingshott C . Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging. 2010; 32(1):223-8. DOI: 10.1002/jmri.22214. View

2.
Ikoma Y, Edison P, Ramlackhansingh A, Brooks D, Turkheimer F . Reference region automatic extraction in dynamic [(11)C]PIB. J Cereb Blood Flow Metab. 2013; 33(11):1725-31. PMC: 3824180. DOI: 10.1038/jcbfm.2013.133. View

3.
Moore G, Laule C, Mackay A, Leung E, Li D, Zhao G . Dirty-appearing white matter in multiple sclerosis: preliminary observations of myelin phospholipid and axonal loss. J Neurol. 2008; 255(11):1802-11. DOI: 10.1007/s00415-008-0002-z. View

4.
Roxburgh R, Seaman S, Masterman T, Hensiek A, Sawcer S, Vukusic S . Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity. Neurology. 2005; 64(7):1144-51. DOI: 10.1212/01.WNL.0000156155.19270.F8. View

5.
Seewann A, Vrenken H, van der Valk P, Blezer E, Knol D, Castelijns J . Diffusely abnormal white matter in chronic multiple sclerosis: imaging and histopathologic analysis. Arch Neurol. 2009; 66(5):601-9. DOI: 10.1001/archneurol.2009.57. View