Abstract
Objective
Both normal gray matter atrophy and brain tissue relaxation rates, in addition to total lesion volume, have shown significant correlations with cognitive test scores in multiple sclerosis (MS). Aim of the study was to assess the relative contributions of macro- and microstructural changes of both normal and abnormal brain tissues, probed, respectively, by their volumes and relaxation rates, to the cognitive status and physical disability of MS patients.
Methods
MRI studies from 241 patients with relapsing–remitting MS were retrospectively analyzed by fully automated multiparametric relaxometric segmentation. Ordinal backward regression analysis was applied to the resulting volumes and relaxation rates of both normal (gray matter, normal-appearing white matter and CSF) and abnormal (T2-weighted lesions) brain tissues, controlling for age, sex and disease duration, to identify the main independent contributors to the cognitive status, as measured by the percentage of failed tests at a cognitive test battery (Rao’s Brief Repeatable Battery and Stroop test, available in 186 patients), and to the physical disability, as assessed by the Expanded Disability Status Scale (EDSS).
Results
The R1 relaxation rate (a putative marker of tissue disruption) of the MS lesions appeared the single most significant contributor to cognitive impairment (p < 0.001). On the contrary, the EDSS appeared mainly affected by the decrease in R2 of the gray matter (p < 0.0001), (possibly influenced by cortical plaques, edema and inflammation).
Conclusions
In RR-MS the tissue damage in white matter lesions appears the single main determinant of the cognitive status of patients, likely through disconnection phenomena, while the physical disability appears related to the involvement of gray matter.
Similar content being viewed by others
References
Chiaravalloti ND, DeLuca J (2008) Cognitive impairment in multiple sclerosis. Lancet Neurol 7:1139–1151. https://doi.org/10.1016/S1474-4422(08)70259-X
Rocca MA, Amato MP, De Stefano N et al (2015) Clinical and imaging assessment of cognitive dysfunction in multiple sclerosis. Lancet Neurol 14:302–417. https://doi.org/10.1016/S1474-4422(14)70250-9
Jacobsen CO, Farbu E (2014) MRI evaluation of grey matter atrophy and disease course in multiple sclerosis: an overview of current knowledge. Acta Neurol Scand Suppl 129:32–36. https://doi.org/10.1111/ane.12234
Calabrese M, Rinaldi F, Mattisi I et al (2010) Widespread cortical thinning characterizes patients with MS with mild cognitive impairment. Neurology 74:321–328. https://doi.org/10.1212/WNL.0b013e3181cbcd03
Benedict RHB, Weinstock-Guttman B, Fishman I et al (2004) Prediction of neuropsychological impairment in multiple sclerosis: comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Arch Neurol 61:226–230. https://doi.org/10.1001/archneur.61.2.226
Sanfilipo MP, Benedict RHB, Weinstock-Guttman B, Bakshi R (2006) Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology 66:685–692. https://doi.org/10.1212/01.wnl.0000201238.93586.d9
Amato MP, Portaccio E, Stromillo ML et al (2008) Cognitive assessment and quantitative magnetic resonance metrics can help to identify benign multiple sclerosis. Neurology 71:632–638. https://doi.org/10.1212/01.wnl.0000324621.58447.00
Does MD (2018) Inferring brain tissue composition and microstructure via MR relaxometry. NeuroImage. https://doi.org/10.1016/j.neuroimage.2017.12.087
Kleine LJ, Mulkern RV, Guttmann CR et al (1995) In vivo characterization of cytotoxic intracellular edema by multicomponent analysis of transverse magnetization decay curves. Acad Radiol 2:365–372
Stanisz GJ, Webb S, Munro CA et al (2004) MR properties of excised neural tissue following experimentally induced inflammation. Magn Reson Med 51:473–479. https://doi.org/10.1002/mrm.20008
Mottershead JP, Schmierer K, Clemence M et al (2003) High field MRI correlates of myelin content and axonal density in multiple sclerosis. J Neurol 250:1293–1301. https://doi.org/10.1007/s00415-003-0192-3
Weiskopf N, Mohammadi S, Lutti A, Callaghan MF (2015) Advances in MRI-based computational neuroanatomy. Curr Opin Neurol 28:313–322. https://doi.org/10.1097/WCO.0000000000000222
Groeschel S, Hagberg GE, Schultz T et al (2016) Assessing white matter microstructure in brain regions with different myelin architecture using MRI. PLoS One 11:e0167274. https://doi.org/10.1371/journal.pone.0167274
Bonnier G, Maréchal B, Fartaria MJ et al (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. Front Neurol 8:506. https://doi.org/10.3389/fneur.2017.00506
Schmierer K, Scaravilli F, Altmann DR et al (2004) Magnetization transfer ratio and myelin in postmortem multiple sclerosis brain. Ann Neurol 56:407–415. https://doi.org/10.1002/ana.20202
Schmierer K, Parkes HG, So P-W et al (2010) High field (9.4 T) magnetic resonance imaging of cortical grey matter lesions in multiple sclerosis. Brain 133:858–867. https://doi.org/10.1093/brain/awp335
Tardif CL, Bedell BJ, Eskildsen SF et al (2012) Quantitative magnetic resonance imaging of cortical multiple sclerosis pathology. Mult Scler Int 2012:1–13. https://doi.org/10.1155/2012/742018
Bonnier G, Roche A, Romascano D et al (2014) Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis. Ann Clin Transl Neurol 1:423–432. https://doi.org/10.1002/acn3.68
Steenwijk MD, Vrenken H, Jonkman LE et al (2016) High-resolution T1-relaxation time mapping displays subtle, clinically relevant, gray matter damage in long-standing multiple sclerosis. Mult Scler 22:1279–1288. https://doi.org/10.1177/1352458515615953
Simioni S, Amarù F, Bonnier G et al (2014) MP2RAGE provides new clinically-compatible correlates of mild cognitive deficits in relapsing–remitting multiple sclerosis. J Neurol 261:1606–1613. https://doi.org/10.1007/s00415-014-7398-4
Bonnier G, Roche A, Romascano D et al (2015) Multicontrast MRI quantification of focal inflammation and degeneration in multiple sclerosis. Biomed Res Int 2015:569123. https://doi.org/10.1155/2015/569123
Wen J, Yablonskiy DA, Luo J et al (2015) Detection and quantification of regional cortical gray matter damage in multiple sclerosis utilizing gradient echo MRI. NeuroImage Clin 9:164–175. https://doi.org/10.1016/j.nicl.2015.08.003
Pinter D, Khalil M, Pichler A et al (2015) Predictive value of different conventional and non-conventional MRI-parameters for specific domains of cognitive function in multiple sclerosis. NeuroImage Clin 7:715–720. https://doi.org/10.1016/j.nicl.2015.02.023
McDonald WI, Compston A, Edan G et al (2001) Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 50:121–127
Rao SM (1991) A manual for the brief, repeatable battery of neuropsychological tests in multiple sclerosis. National Multiple Sclerosis Society, New York
Barbarotto R, Laiacona M, Frosio R et al (1998) A normative study on visual reaction times and two Stroop colour-word tests. Ital J Neurol Sci 19:161–170. https://doi.org/10.1007/BF00831566
Amato MP, Portaccio E, Goretti B et al (2006) The Rao’s Brief Repeatable Battery and Stroop test: normative values with age, education and gender corrections in an Italian population. Mult Scler 12:787–793. https://doi.org/10.1177/1352458506070933
Lanzillo R, Orefice G, Quarantelli M et al (2010) Atorvastatin combined to interferon to verify the efficacy (ACTIVE) in relapsing–remitting active multiple sclerosis patients: a longitudinal controlled trial of combination therapy. Mult Scler 16:450–454. https://doi.org/10.1177/1352458509358909
Lanzillo R, Quarantelli M, Pozzilli C et al (2016) No evidence for an effect on brain atrophy rate of atorvastatin add-on to interferon β1b therapy in relapsing–remitting multiple sclerosis (the ARIANNA study). Mult Scler 22:1163–1173. https://doi.org/10.1177/1352458515611222
Alfano B, Brunetti A, Larobina M, et al (2000) Automated segmentation and measurement of global white matter lesion volume in patients with multiple sclerosis. J Magn Reson Imaging 12:799–807. https://doi.org/10.1002/1522-2586(200012)12:6<799::AID-JMRI2>3.0.CO;2-#
Prinster A, Quarantelli M, Lanzillo R et al (2010) A voxel-based morphometry study of disease severity correlates in relapsing–remitting multiple sclerosis. Mult Scler 16:45–54. https://doi.org/10.1177/1352458509351896
Alfano B, Brunetti A, Ciarmiello A, Salvatore M (1992) Simultaneous display of multiple mr parameters with “quantitative magnetic color imaging. J Comput Assist Tomogr 16:634–640. https://doi.org/10.1097/00004728-199207000-00025
Prinster A, Quarantelli M, Orefice G et al (2006) Grey matter loss in relapsing–remitting multiple sclerosis: a voxel-based morphometry study. NeuroImage 29:859–867. https://doi.org/10.1016/j.neuroimage.2005.08.034
Comi G, Rovaris M, Leocani L et al (2001) Clinical and MRI assessment of brain damage in MS. Neurol Sci 22:S123–S127
Brück W, Bitsch A, Kolenda H et al (1997) Inflammatory central nervous system demyelination: correlation of magnetic resonance imaging findings with lesion pathology. Ann Neurol 42:783–793. https://doi.org/10.1002/ana.410420515
McGowan JC, Filippi M, Campi A, Grossman RI (1998) Magnetisation transfer imaging: theory and application to multiple sclerosis. J Neurol Neurosurg Psychiatry 64:S66–S69
Tievsky AL, Ptak T, Farkas J (1999) Investigation of apparent diffusion coefficient and diffusion tensor anisotropy in acute and chronic multiple sclerosis lesions. Am J Neuroradiol 20:1491–1499
Summers M, Swanton J, Fernando K et al (2008) Cognitive impairment in multiple sclerosis can be predicted by imaging early in the disease. J Neurol Neurosurg Psychiatry 79:955–958. https://doi.org/10.1136/jnnp.2007.138685
Palma G, Tedeschi E, Borrelli P et al (2015) A novel multiparametric approach to 3D quantitative MRI of the brain. PLoS One 10:e0134963. https://doi.org/10.1371/journal.pone.0134963
MacKay AL, Vavasour IM, Rauscher A et al (2009) MR relaxation in multiple sclerosis. Neuroimaging Clin N Am 19:1–26. https://doi.org/10.1016/j.nic.2008.09.007
Laule C, Leung E, Li DK et al (2006) Myelin water imaging in multiple sclerosis: quantitative correlations with histopathology. Mult Scler J 12:747–753. https://doi.org/10.1177/1352458506070928
Yu HJ, Christodoulou C, Bhise V et al (2012) Multiple white matter tract abnormalities underlie cognitive impairment in RRMS. NeuroImage 59:3713–3722. https://doi.org/10.1016/j.neuroimage.2011.10.053
Acknowledgements
Funding by the European Union’s Seventh Framework Programme (FP7/2007–2013) under Grant agreement no. HEALTH-F2-2011-278850 (INMiND), and by the CNR Strategic Project “The Aging: Technological and Molecular Innovations Aiming to Improve the Health of Older Citizens” (http://www.progettoinvecchiamento.it) is gratefully acknowledged.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical standards
All human studies have been approved by the Institutional Review Board of the university “Federico II” and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.
Informed consent
All participants gave informed written consent.
Conflicts of interest
R.L. received personal fees for public speaking or consultancy from Merck, Novartis, Biogen, Genzyme, Teva and Almirall. V.B.M. received personal fees for public speaking or consultancy from Bayer, Mylan, Merck, Novartis, Biogen, Genzyme, Teva and Almirall. M.M. declares that he has received honoraria and support for travelling from Almirall, Coloplast, Genzyme and Merck Serono. R.M., B.A., T.C., M.C., G.V., A.C., A.P., G.S., and M.Q. declare that they have no conflict of interest.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Megna, R., Alfano, B., Lanzillo, R. et al. Brain tissue volumes and relaxation rates in multiple sclerosis: implications for cognitive impairment. J Neurol 266, 361–368 (2019). https://doi.org/10.1007/s00415-018-9139-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00415-018-9139-6