1 / 27

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

moriah
Download Presentation

Introduction: The lesion-centered view on MS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related