Predictive Models in Multimodal Imaging

  • K. Mouridsen
  • L. Østergaard
Part of the Topics in Neuroscience book series (TOPNEURO)


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.


Multiple Sclerosis Multimodal Image Multivariate Adaptive Regression Spline Magnetization Transfer Ratio Receiver Operate Characteristic Curve Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 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 18:192–205PubMedCrossRefGoogle Scholar
  2. 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 8:272–284PubMedCrossRefGoogle Scholar
  3. 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 36:632–636PubMedCrossRefGoogle Scholar
  4. 4.
    Mardia KV, Kent JT, Bibby JM (1979) Multivariate analysis. Academic Press, LondonGoogle Scholar
  5. 5.
    Harrell FE Jr (2001) Regression modeling strategies: with applications to linear models, logistic regression and survival analysis. Springer, Berlin Heidelberg New YorkGoogle Scholar
  6. 6.
    Bowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations. Oxford University Press, Oxford, UKGoogle Scholar
  7. 7.
    Altman DG, Bland JM (1983) Measurement in medicine: the analysis of method comparison studies. Statistician 32:307–317CrossRefGoogle Scholar
  8. 8.
    Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1(8476):307–310PubMedGoogle Scholar
  9. 9.
    Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-PLUS. Springer, Berlin Heidelberg New YorkGoogle Scholar
  10. 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 20:765–771PubMedCrossRefGoogle Scholar
  11. 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 26:119–127PubMedGoogle Scholar
  12. 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 4:58–73CrossRefGoogle Scholar
  13. 13.
    Worsley KJ (1994) Local maxima and the expected Euler characteristic of excursion sets of X 2, F and t fields. Adv Appl Probab 26:13–42CrossRefGoogle Scholar
  14. 14.
    Bakshi R, Minagar A, Jaisani Z, Wolinsky JS (2005) Imaging of multiple sclerosis: role in neurotherapeutics. J Am Soc Exp Neurotherapeut 2:277–303Google Scholar
  15. 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 49:907–916PubMedCrossRefGoogle Scholar
  16. 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 215:824–830PubMedGoogle Scholar
  17. 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 8:479–484PubMedCrossRefGoogle Scholar
  18. 18.
    Laule C, Vavasour IM, Whittall KP et al (2003) Evolution of focal and diffuse magnetisation transfer abnormalities in multiple sclerosis. J Neurol 250:924–931PubMedCrossRefGoogle Scholar
  19. 19.
    Rocca MA, Cercignani M, Iannucci G et al (2000) Weekly diffusion-weighted imaging of normal-appearing white matter in MS. Neurology 55:882–884PubMedGoogle Scholar
  20. 20.
    Santos AC, Narayanan S, De Stefano N et al (2002) Magnetization transfer can predict clinical evolution in patients with multiple sclerosis. J Neurol 249:662–668PubMedCrossRefGoogle Scholar
  21. 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 129:2620–2627PubMedCrossRefGoogle Scholar
  22. 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 32:933–942PubMedGoogle Scholar
  23. 23.
    Wu O, Christensen S, Hjort N et al (2006) Characterizing physiological heterogeneity of infarction risk in acute human ischaemic stroke using MRI. Brain 129:2384–2393PubMedCrossRefGoogle Scholar
  24. 24.
    Hauck WW, Donner A (1977) Wald’s test as applied to hypothesis testing in logit analysis. J Am Stat Assoc 72:851–853CrossRefGoogle Scholar
  25. 25.
    Lawless JF, Singhal K (1978) Efficient screening of nonnormal regression models. Biometrics 34:318–327CrossRefGoogle Scholar
  26. 26.
    Lecessie S, Vanhouwelingen JC (1992) Ridge estimators in logistic-regression. JR Stat Soc Ser C 41:191–201Google Scholar
  27. 27.
    Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B 58:267–288Google Scholar
  28. 28.
    Jerome F (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141CrossRefGoogle Scholar
  29. 29.
    Glantz SA, Slinker BK (1990) Primer of applied regression and analysis of variance. McGraw-Hill, New YorkGoogle Scholar
  30. 30.
    Gross J (2003) Linear regression. Springer, Berlin Heidelberg New YorkGoogle Scholar
  31. 31.
    Chevan A, Sutherland M (1991) Hierarchical partitioning. Am Stat 45:90–96CrossRefGoogle Scholar
  32. 32.
    Kruskal W, Majors R (1989) Concepts of relative importance in recent scientific literature. Am Stat 43:2–6CrossRefGoogle Scholar
  33. 33.
    Kruskal W (1987) Relative importance by averaging over orderings. Am Stat 41:6–10CrossRefGoogle Scholar
  34. 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 31:595–625CrossRefGoogle Scholar
  35. 35.
    Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692CrossRefGoogle Scholar
  36. 36.
    Cragg JG, Uhler RS (1970) Demand for automobiles. Can J Econ 3:386–406CrossRefGoogle Scholar
  37. 37.
    Maddala GS (1983) Limited-dependent and qualitative variables in econometrics. Cambridge University Press, Cambridge, UKGoogle Scholar
  38. 38.
    Efron B (1983) Estimating the error rate of a prediction rule: improvement on crossvalidation. J Am Stat Assoc 78:316–331CrossRefGoogle Scholar
  39. 39.
    Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, London, pp 247–249Google Scholar
  40. 40.
    Box GEP, Jenkins GM, Reinsel GC (1994) Time series analysis: forecasting and control. 3rd edn. Holden-Day, San Francisco, CAGoogle Scholar
  41. 41.
    Jones RH (1993) Longitudinal data with serial correlation: a state-space approach. Chapman and Hall, LondonGoogle Scholar

Copyright information

© Springer-Verlag Itilia 2007

Authors and Affiliations

  • K. Mouridsen
    • 1
  • L. Østergaard
    • 1
  1. 1.Centre for Functionally Integrative Neuroscience (CFIN) Department of NeuroradiologyÅrhus University HospitalÅrhus CDenmark

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