1. Introduction: The lesion-centered view on MS __________________________________________________________________ RRI/TUD/StanU – HH Kitzler 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

2. Material – MS Patients & Healthy Controls __________________________________________________________________ RRI/TUD/StanU – HH Kitzler Case-controlled study design 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 low-risk CIS(n=5) high-risk CIS(n=5) RRMSRelapsing-Remitting MS (n=5) SPMSSecondary Progressive MS (n=6) PPMSPrimary Progressive MS (n=5) MS patients Healthy controls mean age/SD 47 ± 13 years 42 ± 13 years gender F:M 2.3 : 11.6 : 1 EDSS4.0 ± 2.0

3. Methods - mcDESPOT __________________________________________________________________ RRI/TUD/StanU – HH Kitzler Non-linear co-registration to MNI standard brain space (2mm2 MNI152 T1 template) * Deoni SC, Rutt BK, et al. MRM. 60:1372-1387, 2008. Multi-component Driven Equilibrium Single Pulse Observation of T1/T2 (mcDESPOT)* MR Data Acquisition* 1.5T (GE Signa HDx), 8-ch.RF mcDESPOT: 2mm 3 isotropic 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 mm 2 in-plane and 3mm slice resolution MPRAGE pre/post Gd contrast at 1mm 3

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

5. Jason Su

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

14.  Testing at p < 0.05 level is typical  Patients vs. Normals ◦ DV brain : p << 0.0001 ◦ PVF: p = 0.01  Low-Risk CIS vs. Normals ◦ DV brain : p = 0.0006 ◦ PVF: p = 0.37 X  High-Risk CIS vs. Normals ◦ DV brain : p = 0.0006 ◦ PVF: p = 0.81 X  CIS vs. Normals ◦ DV brain : p << 0.0001 ◦ PVF: p = 0.68 X

15.  RRMS vs. Normals ◦ DV brain : p = 0.0005 ◦ PVF: p = 0.76 X  SPMS vs. Normals ◦ DV brain : p = 0.0002 ◦ PVF: p = 0.0006  PPMS vs. Normals ◦ DV brain : p = 0.0005 ◦ PVF: p = 0.0005  RRMS vs. SPMS ◦ DV brain : p = 0.052 X ◦ DV nawm : 0.03 ◦ PVF: p = 0.004

16.  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.  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.  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.  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