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Introduction: The lesion-centered view on MS

Introduction: The lesion-centered view on MS __________________________________________________________________. Specific Aims : To derive myelin water fraction (MWF) maps using a new multi-component relaxometric imaging method (mcDESPOT) in a cohort of MS patients, and

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Introduction: The lesion-centered view on MS

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  1. Introduction: The lesion-centered view on MS __________________________________________________________________ Specific Aims: To derive myelin water fraction (MWF) maps using a new multi-component relaxometric imaging method (mcDESPOT) in a cohort of MS patients, and To test the hypothesis that MWF in normal appearing white matter (NAWM) correlates with disability in MS RRI/TUD/StanU – HH Kitzler

  2. Material – MS Patients & Healthy Controls __________________________________________________________________ • Case-controlledstudydesign • Explorative whole-brain mcDESPOT in clinically relevant time: • Clinically definite MS Subtypes and Clinically Isolated Syndrome (CIS) • MS/CIS patients (n=26) vs. healthy controls (n=26) • Expanded Disability Status Scale (EDSS) registered RRI/TUD/StanU – HH Kitzler

  3. Methods - mcDESPOT __________________________________________________________________ Multi-component Driven Equilibrium Single Pulse Observation of T1/T2 (mcDESPOT)* Non-linear co-registration to MNI standard brain space (2mm2 MNI152 T1 template) • * Deoni SC, Rutt BK, et al. MRM. 60:1372-1387, 2008. • MR Data Acquisition*1.5T (GE Signa HDx), 8-ch.RF • mcDESPOT: 2mm3isotropic covering whole brain, TA: ~15min • SPGR: TE/TR = 2.1/6.7ms, α = {3,4,5,6,7,8,11,13,18}° • bSSFP: TE/TR = 1.8/3.6ms, α = {11,14,20,24,28,34,41,51,67}° • FLAIR at 0.86 mm2 in-plane and 3mm slice resolution • MPRAGE pre/post Gd contrast at 1mm3 RRI/TUD/StanU – HH Kitzler

  4. Postprocessing – Compartment-specific demyelination __________________________________________________________________ Z-score based WM Tissue Segmentation Conventional MR-Data + whole-brain isotropic MWF maps  MNI standard space • Probabilistic WM map WM compartments MWF map Demyelination map Compartment-specific demyelination map RRI/TUD/StanU – HH Kitzler

  5. MSmcDESPOT:Findings and Figures Jason Su

  6. Goals • Present (almost) final results • Judge figures, how to improve their readability and presentation for publication • Discussion of further analysis

  7. MWF Comparison

  8. Correlation Plots: Whole Brain

  9. Correlation Plots: White Matter

  10. Correlation Plots: NAWM

  11. Correlation Plots: Lesion Load

  12. Correlation Plots: PVF

  13. Correlation Plots: PVF vs. DV

  14. Statistical Testing: Rank Sum • Testing at p < 0.05 level is typical • Patients vs. Normals • DVbrain: p << 0.0001 • PVF: p = 0.01 • Low-Risk CIS vs. Normals • DVbrain: p = 0.0006 • PVF: p = 0.37 X • High-Risk CIS vs. Normals • DVbrain: p = 0.0006 • PVF: p = 0.81 X • CIS vs. Normals • DVbrain: p << 0.0001 • PVF: p = 0.68 X

  15. Statistical Testing: Rank Sum • RRMS vs. Normals • DVbrain: p = 0.0005 • PVF: p = 0.76 X • SPMS vs. Normals • DVbrain: p = 0.0002 • PVF: p = 0.0006 • PPMS vs. Normals • DVbrain: p = 0.0005 • PVF: p = 0.0005 • RRMS vs. SPMS • DVbrain: p = 0.052 X • DVnawm: 0.03 • PVF: p = 0.004

  16. Multiple Linear Regression • Y = X*a • a = pinv(X)*Y, LS solution, pinv(X) = inv(X’X)X’ • X is a matrix with columns of predictors • The outcome is linear in a predictor after accounting for all the others • Same assumptions from simple lin. reg. • Inde. normal-dist. residuals, constant variance • Adding even random noise to X improves R^2 • Adjusted R^2, instead of sum of square error, use mean square error: favors simpler models

  17. Model Selection • As suggested by Adjusted R^2, what we really want is a parsimonious model • One that predicts the outcome well with only a few predictors • This is a combinatorially hard problem • Models are evaluated with a criterion • Adjusted R^2 • Mallow’s Cp – estimated predictive power of model • Akaike information criterion (AIC) – related to Cp • Bayesian information criterion (BIC) • Cross validation with MSE

  18. Search Strategy • If the model is small enough, can search all • In MSmcDESPOT this is probably feasible, our predictors are: age, PVF, log(DV), gender, PP, SP, RR, High-Risk CIS • 127 possibilities • Stepwise • This is a popular search method where the algorithm is giving a starting point then adds or removes predictors one at a time until there is no improvement in the criterion

  19. Model Selection: Fitting EDSS • Exhaustive search with Mallow’s Cp criterion • leaps() in R • Chooses a model with Age+SPMS+PPMS(Intercept) Age PPMS1 SPMS1 -0.97579 0.06416 3.07291 3.70352 • Consolation prize: models with DV rather than PVF generally had an improved Cp but still not the best • F-test of Age+PVF+DV and Age+PVF • Works on nested models, used in ANOVA • Tests if the coefficient for DV is non-zero, i.e. if it is a significantly better fit with DV • p = 0.004, DV should be included

  20. Model Selection

  21. Model Selection: Diagnostics

  22. Thoughts?

  23. Correlation Plots: MWF in Brain

  24. Correlation Plots: MWF in WM

  25. Correlation Plots: MWF in NAWM

  26. Correlation Plots: MWF in DAWM

  27. Correlation Plots: MWF in Lesions

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