Skip to main content
SpringerLink
Log in

Predictive Models in Multimodal Imaging

  • Chapter
Neurodegeneration in Multiple Sclerosis

Part of the book series: Topics in Neuroscience ((TOPNEURO))

  • 1325 Accesses

Abstract

The disease mechanism of multiple sclerosis (MS) causes progressive subcellular and cellular changes that may ultimately be detected by magnetic resonance imaging (MRI): for instance, in normal-appearing white matter (NAWM) the effects of the disease gradually alter the macromolecular and cellular compartmentalization of water, causing subtle changes in magnetization transfer ratio (MTR) and diffusion-weighted imaging (DWI). Similarly, MS lesions are characterized by serial image changes in several MR image modalities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Collins DL, Neelin P, Peters TM, Evans AC (1994) Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18:192–205

    Article  PubMed  CAS  Google Scholar 

  2. Fischl B, Sereno MI, Tootell RBH, Dale AM (1999) High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 8:272–284

    Article  PubMed  CAS  Google Scholar 

  3. vanBuchem MA, McGowan JC, Kolson DL et al (1996) Quantitative volumetric magnetization transfer analysis in multiple sclerosis: estimation of macroscopic and microscopic disease burden. Magn Reson Med 36:632–636

    Article  PubMed  Google Scholar 

  4. Mardia KV, Kent JT, Bibby JM (1979) Multivariate analysis. Academic Press, London

    Google Scholar 

  5. Harrell FE Jr (2001) Regression modeling strategies: with applications to linear models, logistic regression and survival analysis. Springer, Berlin Heidelberg New York

    Google Scholar 

  6. Bowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. Oxford University Press, Oxford, UK

    Google Scholar 

  7. Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison studies. Statistician 32:307–317

    Article  Google Scholar 

  8. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476):307–310

    PubMed  CAS  Google Scholar 

  9. Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-PLUS. Springer, Berlin Heidelberg New York

    Google Scholar 

  10. Audoin B, Ranjeva JP, Duong MVA et al (2004) Voxel-based analysis of MTR images: a method to locate gray matter abnormalities in patients at the earliest stage of multiple sclerosis. J Magn Reson Imaging 20:765–771

    Article  PubMed  Google Scholar 

  11. Ranjeva JP, Audoin B, Duong MVA et al (2005) Local tissue damage assessed with statistical mapping analysis of brain magnetization transfer ratio: relationship with functional status of patients in the earliest stage of multiple sclerosis. Am J Neuroradiol 26:119–127

    PubMed  Google Scholar 

  12. Worsley KJ, Marrett S, Neelin P et al (1996) A unified statistical approach for determining significant signals in images of cerebral activation. Hum Brain Mapp 4:58–73

    Article  Google Scholar 

  13. Worsley KJ (1994) Local maxima and the expected Euler characteristic of excursion sets of X 2, F and t fields. Adv Appl Probab 26:13–42

    Article  Google Scholar 

  14. Bakshi R, Minagar A, Jaisani Z, Wolinsky JS (2005) Imaging of multiple sclerosis: role in neurotherapeutics. J Am Soc Exp Neurotherapeut 2:277–303

    Google Scholar 

  15. Sun GW, Shook TL, Kay GL (1996) Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 49:907–916

    Article  PubMed  CAS  Google Scholar 

  16. Pike GB, De Stefano N, Narayanan S et al (2000) Multiple sclerosis: magnetization transfer MR imaging of white matter before lesion appearance on T2-weighted images. Radiology 215:824–830

    PubMed  CAS  Google Scholar 

  17. Fazekas F, Ropele S, Enzinger C et al (2002) Quantitative magnetization transfer imaging of pre-lesional white-matter changes in multiple sclerosis. Mult Scler 8:479–484

    Article  PubMed  CAS  Google Scholar 

  18. Laule C, Vavasour IM, Whittall KP et al (2003) Evolution of focal and diffuse magnetisation transfer abnormalities in multiple sclerosis. J Neurol 250:924–931

    Article  PubMed  Google Scholar 

  19. Rocca MA, Cercignani M, Iannucci G et al (2000) Weekly diffusion-weighted imaging of normal-appearing white matter in MS. Neurology 55:882–884

    PubMed  CAS  Google Scholar 

  20. Santos AC, Narayanan S, De Stefano N et al (2002) Magnetization transfer can predict clinical evolution in patients with multiple sclerosis. J Neurol 249:662–668

    Article  PubMed  Google Scholar 

  21. Agosta F, Rovaris M, Pagani E, Sormani MP et al (2006) Magnetization transfer MRI metrics predict the accumulation of disability 8 years later in patients with multiple sclerosis. Brain 129:2620–2627

    Article  PubMed  Google Scholar 

  22. Wu O, Koroshetz WJ, Østergaard L et al (2001) Predicting tissue outcome in acute human cerebral ischemia using combined diffusion-and perfusion-weighted MR imaging. Stroke 32:933–942

    PubMed  CAS  Google Scholar 

  23. Wu O, Christensen S, Hjort N et al (2006) Characterizing physiological heterogeneity of infarction risk in acute human ischaemic stroke using MRI. Brain 129:2384–2393

    Article  PubMed  Google Scholar 

  24. Hauck WW, Donner A (1977) Wald’s test as applied to hypothesis testing in logit analysis. J Am Stat Assoc 72:851–853

    Article  Google Scholar 

  25. Lawless JF, Singhal K (1978) Efficient screening of nonnormal regression models. Biometrics 34:318–327

    Article  Google Scholar 

  26. Lecessie S, Vanhouwelingen JC (1992) Ridge estimators in logistic-regression. JR Stat Soc Ser C 41:191–201

    Google Scholar 

  27. Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B 58:267–288

    Google Scholar 

  28. Jerome F (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141

    Article  Google Scholar 

  29. Glantz SA, Slinker BK (1990) Primer of applied regression and analysis of variance. McGraw-Hill, New York

    Google Scholar 

  30. Gross J (2003) Linear regression. Springer, Berlin Heidelberg New York

    Google Scholar 

  31. Chevan A, Sutherland M (1991) Hierarchical partitioning. Am Stat 45:90–96

    Article  Google Scholar 

  32. Kruskal W, Majors R (1989) Concepts of relative importance in recent scientific literature. Am Stat 43:2–6

    Article  Google Scholar 

  33. Kruskal W (1987) Relative importance by averaging over orderings. Am Stat 41:6–10

    Article  Google Scholar 

  34. Soofi ES, Retzer JJ, Yasai-Ardekani M (2000) A framework for measuring the importance of variables with applications to management research and decision models. Decision Sci 31:595–625

    Article  Google Scholar 

  35. Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692

    Article  Google Scholar 

  36. Cragg JG, Uhler RS (1970) Demand for automobiles. Can J Econ 3:386–406

    Article  Google Scholar 

  37. Maddala GS (1983) Limited-dependent and qualitative variables in econometrics. Cambridge University Press, Cambridge, UK

    Google Scholar 

  38. Efron B (1983) Estimating the error rate of a prediction rule: improvement on crossvalidation. J Am Stat Assoc 78:316–331

    Article  Google Scholar 

  39. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, London, pp 247–249

    Google Scholar 

  40. Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting and control. 3rd edn. Holden-Day, San Francisco, CA

    Google Scholar 

  41. Jones RH (1993) Longitudinal data with serial correlation: a state-space approach. Chapman and Hall, London

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Functionally Integrative Neuroscience (CFIN) Department of Neuroradiology, Århus University Hospital, Århus C, Denmark

    K. Mouridsen & L. Østergaard

Authors
  1. K. Mouridsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Østergaard
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Neuroimaging Research Unit Department of Neurology, Scientific Institute and University Ospedale San Raffaele, Milan, Italy

    Massimo Filippi  & Marco Rovaris  & 

  2. Department of Neurology, Scientific Institute and University Ospedale San Raffaele, Milan, Italy

    Giancarlo Comi

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Itilia

About this chapter

Cite this chapter

Mouridsen, K., Østergaard, L. (2007). Predictive Models in Multimodal Imaging. In: Filippi, M., Rovaris, M., Comi, G. (eds) Neurodegeneration in Multiple Sclerosis. Topics in Neuroscience. Springer, Milano. https://doi.org/10.1007/978-88-470-0391-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-88-470-0391-0_12

  • Publisher Name: Springer, Milano

  • Print ISBN: 978-88-470-0390-3

  • Online ISBN: 978-88-470-0391-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation