Journal of Neurology

, Volume 259, Issue 10, pp 2151–2160 | Cite as

Can we overcome the ‘clinico-radiological paradox’ in multiple sclerosis?

  • Kerstin Hackmack
  • Martin Weygandt
  • Jens Wuerfel
  • Caspar F. Pfueller
  • Judith Bellmann-Strobl
  • Friedemann Paul
  • John-Dylan Haynes
Original Communication


The association between common neuroradiological markers of multiple sclerosis (MS) and clinical disability is weak, a phenomenon known as the clinico-radiological paradox. Here, we investigated to which degree it is possible to predict individual disease profiles from conventional magnetic resonance imaging (MRI) using multivariate analysis algorithms. Specifically, we conducted cross-validated canonical correlation analyses to investigate the predictive information contained in conventional MRI data of 40 MS patients for the following clinical parameters: disease duration, motor disability (9-Hole Peg Test, Timed 25-Foot Walk Test), cognitive dysfunction (Paced Auditory Serial Addition Test), and the expanded disability status scale (EDSS). It turned out that the information in the spatial patterning of MRI data predicted the clinical scores with correlations of up to 0.80 (p < 10−9). Maximal predictive information for disease duration was identified in the precuneus and somatosensory cortex. Areas in the precuneus and precentral gyrus were maximally informative for motor disability. Cognitive dysfunction could best be predicted using data from the angular gyrus and superior parietal lobe. For EDSS, the inferior frontal gyrus was maximally informative. In conclusion, conventional MRI is highly predictive of clinical disability in MS when pattern-based algorithms are used for prediction. Thus, the so-called clinico-radiological paradox is not apparent when using suitable analysis techniques.


Multiple sclerosis MRI Clinico-radiological paradox Disability Pattern recognition 



This work was supported by the Max Planck Society, the Bernstein Computational Program of the German Federal Ministry of Education and Research [01GQ0411, 01GQ0851, and 01GQ1001C to J.-D.H., GRK 1,589/1 to K.H.] and the German Research Foundation [Exc 257 to C.P. and F.P., KFO 218/1 to M.W.].

Conflicts of interest

The authors declare that they have no conflicts of interest.


  1. 1.
    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 50:121–127PubMedCrossRefGoogle Scholar
  2. 2.
    Bakshi R, Thompson AJ, Rocca MA et al (2008) MRI in multiple sclerosis: current status and future prospects. Lancet Neurol 7:615–625PubMedCrossRefGoogle Scholar
  3. 3.
    Barkhof F (1999) MRI in multiple sclerosis: correlation with expanded disability status scale (EDSS). Mult Scler 5:283–286PubMedGoogle Scholar
  4. 4.
    Barkhof F (2002) The clinico-radiological paradox in multiple sclerosis revisited [Review]. Curr Opin Neurol 15:239–245PubMedCrossRefGoogle Scholar
  5. 5.
    Filippi M, Grossman RI (2002) MRI techniques to monitor MS evolution: the present and the future. Neurology 58:1147–1153PubMedCrossRefGoogle Scholar
  6. 6.
    Filippi M, Rocca MA (2005) MRI evidence for multiple sclerosis as a diffuse disease of the central nervous system. J Neurol 252(Suppl):16–24CrossRefGoogle Scholar
  7. 7.
    Klöppel S, Chu C, Tan GC et al (2009) Automatic detection of preclinical neurodegeneration: presymptomatic Huntington disease. Neurology 72:426–431PubMedCrossRefGoogle Scholar
  8. 8.
    Klöppel S, Stonnington CM, Chu C et al (2008) Automatic classification of MR scans in Alzheimer’s disease. Brain 131:681–689PubMedCrossRefGoogle Scholar
  9. 9.
    Stonnington CM, Chu C, Klöppel S et al (2010) Predicting clinical scores from magnetic resonance scans in Alzheimer’s disease. Neuroimage 51:1405–1413PubMedCrossRefGoogle Scholar
  10. 10.
    Wang Y, Fan Y, Bhatt P, Davatzikos C (2010) High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. Neuroimage 50:1519–1535PubMedCrossRefGoogle Scholar
  11. 11.
    Cutter GR, Baier ML, Rudick RA et al (1999) Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain 122:871–882PubMedCrossRefGoogle Scholar
  12. 12.
    Kurtzke JF (1983) Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 33:1444–1452PubMedCrossRefGoogle Scholar
  13. 13.
    Weygandt M, Hackmack K, Pfueller C et al (2011) MRI pattern recognition in multiple sclerosis normal-appearing brain areas. PLoS One 6:e21138PubMedCrossRefGoogle Scholar
  14. 14.
    Paul F, Waiczies S, Wuerfel J et al (2008) Oral high-dose atorvastatin treatment in relapsing-remitting multiple sclerosis. PLoS One 3:e1928PubMedCrossRefGoogle Scholar
  15. 15.
    Wuerfel J, Bellmann-Strobl J, Brunecker P et al (2004) Changes in cerebral perfusion precede plaque formation in multiple sclerosis: a longitudinal perfusion MRI study. Brain 127:111–119PubMedCrossRefGoogle Scholar
  16. 16.
    Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26:839–851PubMedCrossRefGoogle Scholar
  17. 17.
    Weygandt M, Schaefer A, Schienle A, Haynes JD (2011) Diagnosing different binge-eating disorders based on reward-related brain activation patterns. Hum Brain Mapp. doi: 10.1002/hbm.21345 PubMedGoogle Scholar
  18. 18.
    Kriegeskorte N, Goebel R, Bandettini P (2006) Information-based functional brain mapping. Proc Natl Acad Sci USA 103:3863–3868PubMedCrossRefGoogle Scholar
  19. 19.
    Haynes JD, Sakai K, Rees G et al (2007) Reading hidden intentions in the human brain. Curr Biol 17:323–328PubMedCrossRefGoogle Scholar
  20. 20.
    Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16:2639–2664PubMedCrossRefGoogle Scholar
  21. 21.
    Jolliffe IT (2002) Principal component analysis. Springer, BerlinGoogle Scholar
  22. 22.
    Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12:535–540PubMedCrossRefGoogle Scholar
  23. 23.
    Tzourio-Mazoyer N, Landeau B, Papthanassiou D et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289PubMedCrossRefGoogle Scholar
  24. 24.
    Dehaene S, Molko N, Cohen L, Wilson AJ (2004) Arithmetic and the brain. Curr Opin Neurobiol 14:218–224PubMedCrossRefGoogle Scholar
  25. 25.
    Chard D, Miller D (2009) Grey matter pathology in clinically early multiple sclerosis: evidence from magnetic resonance imaging. J Neurol Sci 282:5–11PubMedCrossRefGoogle Scholar
  26. 26.
    Filippi M, Agosta F (2010) Imaging biomarkers in multiple sclerosis. [Review]. J Magn Reson Imaging 31:770–788PubMedCrossRefGoogle Scholar
  27. 27.
    Filippi M, Rocca MA, Colombo B et al (2002) Functional magnetic resonance imaging correlates of fatigue in multiple sclerosis. Neuroimage 15:559–567PubMedCrossRefGoogle Scholar
  28. 28.
    Prinster A, Quarantelli M, Orefice G et al (2006) Grey matter loss in relapsing-remitting multiple sclerosis: a voxel-based morphometry study. Neuroimage 29:859–867PubMedCrossRefGoogle Scholar
  29. 29.
    Wylezinska M, Cifelli A, Jezzard P et al (2003) Thalamic neurodegeneration in relapsing-remitting multiple sclerosis. Neurology 60:1949–1954PubMedCrossRefGoogle Scholar
  30. 30.
    Harirchian MH, Rezvanizadeh A, Fakhri M et al (2010) Non-invasive brain mapping of motor-related areas of four limbs in patients with clinically isolated syndrome compared to healthy normal controls. J Clin Neurosci 17:736–741PubMedCrossRefGoogle Scholar
  31. 31.
    Morgen K, Sammer G, Courtney SM et al (2007) Distinct mechanisms of altered brain activation in patients with multiple sclerosis. Neuroimage 37:937–946PubMedCrossRefGoogle Scholar
  32. 32.
    Bodini B, Khaleeli Z, Cercignani M et al (2009) Exploring the relationship between white matter and gray matter damage in early primary progressive multiple sclerosis: an in vivo study with TBSS and VBM. Hum Brain Mapp 30:2852–2861PubMedCrossRefGoogle Scholar
  33. 33.
    Morgen K, Sammer G, Courtney SM et al (2006) Evidence for a direct association between cortical atrophy and cognitive impairment in relapsing-remitting MS. Neuroimage 30:891–898PubMedCrossRefGoogle Scholar
  34. 34.
    Dineen RA, Vilisaar J, Hlinka J et al (2009) Disconnection as a mechanism for cognitive dysfunction in multiple sclerosis. Brain 132:239–249PubMedCrossRefGoogle Scholar
  35. 35.
    Bakshi R, Benedict RH, Bermel RA, Jacobs L (2001) Regional brain atrophy is associated with physical disability in multiple sclerosis: semiquantitative magnetic resonance imaging and relationship to clinical findings. J Neuroimaging 11:129–136PubMedCrossRefGoogle Scholar
  36. 36.
    Sailer M, Fischl B, Salat D et al (2003) Focal thinning of the cerebral cortex in multiple sclerosis. Brain 126:1734–1744PubMedCrossRefGoogle Scholar
  37. 37.
    Rudick RA, Lee JC, Nakamura K, Fisher E (2009) Gray matter atrophy correlates with MS disability progression measured with MSFC but not EDSS. J Neurol Sci 282:106–111PubMedCrossRefGoogle Scholar
  38. 38.
    Stevenson VL, Leary SM, Losseff NA et al (1998) Spinal cord atrophy and disability in MS: a longitudinal study. Neurology 51:234–238PubMedCrossRefGoogle Scholar
  39. 39.
    Schmierer K, Parkes HG, So PW et al (2010) High field (9.4 Tesla) magnetic resonance imaging of cortical grey matter lesions in multiple sclerosis. Brain 133:858–867PubMedCrossRefGoogle Scholar
  40. 40.
    Van Walderveen MA, Lycklama A, Nijeholt GJ et al (2001) Hypointense lesions on T1-weighted spin-echo magnetic resonance imaging: relation to clinical characteristics in subgroups of patients with multiple sclerosis. Arch Neurol 58:76–81PubMedCrossRefGoogle Scholar
  41. 41.
    Vrenken H, Geurts JJ, Knol DL et al (2006) Whole-brain T1 mapping in multiple sclerosis: global changes of normal-appearing gray and white matter. Radiology 240:811–820PubMedCrossRefGoogle Scholar
  42. 42.
    Neema M, Stankiewicz J, Arora A et al (2007) T1- and T2-based MRI measures of diffuse gray matter and white matter damage in patients with multiple sclerosis. J Neuroimaging 17(Suppl 1):16S–21SPubMedCrossRefGoogle Scholar
  43. 43.
    Lucchinetti C, Brück W, Noseworthy J (2001) Multiple sclerosis: recent developments in neuropathology, pathogenesis, magnetic resonance imaging studies and treatment. Curr Opin Neurol 14:259–269PubMedCrossRefGoogle Scholar
  44. 44.
    Roosendaal SD, Geurts JJ, Vrenken H et al (2009) Regional DTI differences in multiple sclerosis patients. Neuroimage 44:1397–1403PubMedCrossRefGoogle Scholar
  45. 45.
    Ge Y, Grossman RI, Babb JS et al (2003) Dirty-appearing white matter in multiple sclerosis: volumetric MR imaging and magnetization transfer ratio histogram analysis. Am J Neuroradiol 24:1935–1940PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Kerstin Hackmack
    • 1
  • Martin Weygandt
    • 1
    • 2
  • Jens Wuerfel
    • 2
    • 5
  • Caspar F. Pfueller
    • 2
    • 3
  • Judith Bellmann-Strobl
    • 2
    • 3
    • 4
  • Friedemann Paul
    • 2
    • 4
  • John-Dylan Haynes
    • 1
    • 2
  1. 1.Bernstein Center for Computational Neuroscience, Berlin Center for Advanced NeuroimagingCharité-Universitätsmedizin BerlinBerlinGermany
  2. 2.Cluster of Excellence NeuroCureCharité-Universitätsmedizin BerlinBerlinGermany
  3. 3.Experimental Multiple Sclerosis Research CenterCharité-Universitätsmedizin BerlinBerlinGermany
  4. 4.Experimental and Clinical Research Center, Max Delbrueck Center for Molecular MedicineCharité-Universitätsmedizin BerlinBerlinGermany
  5. 5.Institute of NeuroradiologyUniversity of LübeckLübeckGermany

Personalised recommendations