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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

Abstract

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.

Keywords

Multiple sclerosis MRI Clinico-radiological paradox Disability Pattern recognition 

Notes

Acknowledgments

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.

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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

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