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MP2RAGE provides new clinically-compatible correlates of mild cognitive deficits in relapsing-remitting multiple sclerosis

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Abstract

Despite that cognitive impairment is a known early feature present in multiple sclerosis (MS) patients, the biological substrate of cognitive deficits in MS remains elusive. In this study, we assessed whether T1 relaxometry, as obtained in clinically acceptable scan times by the recent Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) sequence, may help identifying the structural correlate of cognitive deficits in relapsing-remitting MS patients (RRMS). Twenty-nine healthy controls (HC) and forty-nine RRMS patients underwent high-resolution 3T magnetic resonance imaging to obtain optimal cortical lesion (CL) and white matter lesion (WML) count/volume and T1 relaxation times. T1 z scores were then obtained between T1 relaxation times in lesion and the corresponding HC tissue. Patient cognitive performance was tested using the Brief Repeatable Battery of Neuro-psychological Tests. Multivariate analysis was applied to assess the contribution of MRI variables (T1 z scores, lesion count/volume) to cognition in patients and Bonferroni correction was applied for multiple comparison. T1 z scores were higher in WML (p < 0.001) and CL-I (p < 0.01) than in the corresponding normal-appearing tissue in patients, indicating relative microstructural loss. (1) T1 z scores in CL-I (p = 0.01) and the number of CL-II (p = 0.04) were predictors of long-term memory; (2) T1 z scores in CL-I (β = 0.3; p = 0.03) were independent determinants of long-term memory storage, and (3) lesion volume did not significantly influenced cognitive performances in patients. Our study supports evidence that T1 relaxometry from MP2RAGE provides information about microstructural properties in CL and WML and improves correlation with cognition in RRMS patients, compared to conventional measures of disease burden.

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References

  1. Peyser JM et al (1990) Guidelines for neuropsychological research in multiple sclerosis. Arch Neurol 47(1):94–97

    Article  CAS  PubMed  Google Scholar 

  2. Rao SM et al (1991) Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology 41(5):685–691

    Article  CAS  PubMed  Google Scholar 

  3. Chiaravalloti ND, DeLuca J (2008) Cognitive impairment in multiple sclerosis. Lancet Neurol 7(12):1139–1151

    Article  PubMed  Google Scholar 

  4. Amato MP, Zipoli V, Portaccio E (2008) Cognitive changes in multiple sclerosis. Expert Rev Neurother 8(10):1585–1596

    Article  PubMed  Google Scholar 

  5. Amato MP, Zipoli V, Portaccio E (2006) Multiple sclerosis-related cognitive changes: a review of cross-sectional and longitudinal studies. J Neurol Sci 245(1–2):41–46

    Article  PubMed  Google Scholar 

  6. Rovaris M, Comi G, Filippi M (2006) MRI markers of destructive pathology in multiple sclerosis-related cognitive dysfunction. J Neurol Sci 245(1–2):111–116

    Article  CAS  PubMed  Google Scholar 

  7. Berg D et al (2000) Lesion pattern in patients with multiple sclerosis and depression. Mult Scler 6(3):156–162

    Article  CAS  PubMed  Google Scholar 

  8. Calabrese M et al (2009) Cortical lesions and atrophy associated with cognitive impairment in relapsing-remitting multiple sclerosis. Arch Neurol 66(9):1144–1150

    Article  PubMed  Google Scholar 

  9. Mainero C et al (2004) fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis. Neuroimage 21(3):858–867

    Article  PubMed  Google Scholar 

  10. Rao SM et al (1989) Correlation of magnetic resonance imaging with neuropsychological testing in multiple sclerosis. Neurology 39(2 Pt 1):161–166

    Article  CAS  PubMed  Google Scholar 

  11. Swirsky-Sacchetti T et al (1992) Neuropsychological and structural brain lesions in multiple sclerosis: a regional analysis. Neurology 42(7):1291–1295

    Article  CAS  PubMed  Google Scholar 

  12. Geurts JJ et al (2005) Intracortical lesions in multiple sclerosis: improved detection with 3D double inversion-recovery MR imaging. Radiology 236(1):254–260

    Article  PubMed  Google Scholar 

  13. Calabrese M, Filippi M, Gallo P (2010) Cortical lesions in multiple sclerosis. Nat Rev Neurol 6(8):438–444

    Article  PubMed  Google Scholar 

  14. Roosendaal SD et al (2009) Accumulation of cortical lesions in MS: relation with cognitive impairment. Mult Scler 15(6):708–714

    Article  CAS  PubMed  Google Scholar 

  15. Parry A et al (2002) White matter and lesion T1 relaxation times increase in parallel and correlate with disability in multiple sclerosis. J Neurol 249(9):1279–1286

    Article  CAS  PubMed  Google Scholar 

  16. Nielsen AS et al (2013) Contribution of cortical lesion subtypes at 7T MRI to physical and cognitive performance in MS. Neurology 81(7):641–649

    Article  PubMed Central  PubMed  Google Scholar 

  17. Bakshi R et al (2005) Imaging of multiple sclerosis: role in neurotherapeutics. NeuroRx 2(2):277–303

    Article  PubMed Central  PubMed  Google Scholar 

  18. Castriota-Scanderbeg A et al (2004) T1 relaxation maps allow differentiation between pathologic tissue subsets in relapsing-remitting and secondary progressive multiple sclerosis. Mult Scler 10(5):556–561

    Article  CAS  PubMed  Google Scholar 

  19. McDonald WI et al (2001) Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 50(1):121–127

    Article  CAS  PubMed  Google Scholar 

  20. Deoni SC (2010) Quantitative relaxometry of the brain. Top Magn Reson Imaging 21(2):101–113

    Article  PubMed Central  PubMed  Google Scholar 

  21. Marques JP et al (2010) MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49(2):1271–1281

    Article  PubMed  Google Scholar 

  22. Kober T et al (2012) MP2RAGE multiple sclerosis magnetic resonance imaging at 3 T. Invest Radiol 47(6):346–352

    Article  PubMed  Google Scholar 

  23. Boringa JB et al (2001) The brief repeatable battery of neuropsychological tests: normative values allow application in multiple sclerosis clinical practice. Mult Scler 7(4):263–267

    Article  CAS  PubMed  Google Scholar 

  24. Polman CH et al (2005) Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann Neurol 58(6):840–846

    Article  PubMed  Google Scholar 

  25. Rao SM (1990) A manual for the brief repeatable battery of neuropsychological tests in multiple sclerosis. Medical College of Wisconsin Milwaukee, WI

    Google Scholar 

  26. Zigmond AS, Snaith RP (1983) The hospital anxiety and depression scale. Acta Psychiatr Scand 67(6):361–370

    Article  CAS  PubMed  Google Scholar 

  27. Penner IK et al (2009) The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue. Mult Scler 15(12):1509–1517

    Article  CAS  PubMed  Google Scholar 

  28. Mugler JP 3rd (2014) Optimized three-dimensional fast-spin-echo MRI. J Magn Reson Imaging 39:745–767

    Article  PubMed  Google Scholar 

  29. Roche A (2011) A four-dimensional registration algorithm with application to joint correction of motion and slice timing in fMRI. IEEE Trans Med Imaging 30(8):1546–1554

    Article  PubMed  Google Scholar 

  30. Roche A et al (2011) On the convergence of EM-like algorithms for image segmentation using Markov random fields. Med Image Anal 15(6):830–839

    Article  PubMed  Google Scholar 

  31. Bitsch A et al (2001) A longitudinal MRI study of histopathologically defined hypointense multiple sclerosis lesions. Ann Neurol 49(6):793–796

    Article  CAS  PubMed  Google Scholar 

  32. Larsson HB et al (1989) Assessment of demyelination, edema, and gliosis by in vivo determination of T1 and T2 in the brain of patients with acute attack of multiple sclerosis. Magn Reson Med 11(3):337–348

    Article  CAS  PubMed  Google Scholar 

  33. Laule C et al (2004) Water content and myelin water fraction in multiple sclerosis. A T2 relaxation study. J Neurol 251(3):284–293

    Article  CAS  PubMed  Google Scholar 

  34. Davies GR et al (2007) Normal-appearing grey and white matter T1 abnormality in early relapsing-remitting multiple sclerosis: a longitudinal study. Mult Scler 13(2):169–177

    Article  CAS  PubMed  Google Scholar 

  35. Manfredonia F et al (2007) Normal-appearing brain t1 relaxation time predicts disability in early primary progressive multiple sclerosis. Arch Neurol 64(3):411–415

    Article  PubMed  Google Scholar 

  36. Papadopoulos K et al (2010) TI-relaxation time changes over five years in relapsing-remitting multiple sclerosis. Mult Scler 16(4):427–433

    Article  PubMed  Google Scholar 

  37. Vrenken H et al (2006) Whole-brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology 240(3):811–820

    Article  PubMed  Google Scholar 

  38. Rao SM (1995) Neuropsychology of multiple sclerosis. Curr Opin Neurol 8(3):216–220

    Article  CAS  PubMed  Google Scholar 

  39. Allen G et al (1997) Attentional activation of the cerebellum independent of motor involvement. Science 275(5308):1940–1943

    Article  CAS  PubMed  Google Scholar 

  40. Audoin B et al (2003) Compensatory cortical activation observed by fMRI during a cognitive task at the earliest stage of MS. Hum Brain Mapp 20(2):51–58

    Article  PubMed  Google Scholar 

  41. Staffen W et al (2002) Cognitive function and fMRI in patients with multiple sclerosis: evidence for compensatory cortical activation during an attention task. Brain 125(Pt 6):1275–1282

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We thank Jaeseok Park for his kind help with the DIR sequence as well as Georgina Palau for her dedicated work.

This work was supported by the Swiss National Science Foundation under grant PZ00P3_131914/11, the Swiss MS Society and the Societé Académique Vaudoise.

The funding sources had no role in study design, in the collection, analysis, and interpretation of data, in the writing of the report and in the decision to submit the paper for publication.

Conflicts of interest

Dr Krueger and Dr Kober work for Siemens AG. The other authors have no competing interests and nothing to disclose.

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Correspondence to Fabio Amarù.

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S. Simioni and F. Amarù contributed equally and share first authorship.

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Simioni, S., Amarù, F., Bonnier, G. et al. MP2RAGE provides new clinically-compatible correlates of mild cognitive deficits in relapsing-remitting multiple sclerosis. J Neurol 261, 1606–1613 (2014). https://doi.org/10.1007/s00415-014-7398-4

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  • DOI: https://doi.org/10.1007/s00415-014-7398-4

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