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

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)

RRMS Relapsing-Remitting MS (n=5)SPMS Secondary Progressive MS (n=6)

PPMS Primary Progressive MS (n=5)

MS patients

Healthy controls

mean age/SD

47 ± 13 years

42 ± 13 years

gender F:M 2.3 : 1 1.6 : 1

EDSS 4.0 ± 2.0

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: 2mm3 isotropic covering whole brain, TA: ~15minSPGR: 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 resolutionMPRAGE pre/post Gd contrast at 1mm3

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

MSmcDESPOT:Findings and Figures

Jason Su

Present (almost) final results Judge figures, how to improve their

readability and presentation for publication Discussion of further analysis

Goals

MWF Comparison

Correlation Plots: Whole Brain

Correlation Plots: White Matter

Correlation Plots: NAWM

Correlation Plots: Lesion Load

Correlation Plots: PVF

Correlation Plots: PVF vs. DV

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

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

Statistical Testing: Rank Sum

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

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

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

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

Model Selection: Fitting EDSS

Model Selection

Model Selection: Diagnostics

Thoughts?

Correlation Plots: MWF in Brain

Correlation Plots: MWF in WM

Correlation Plots: MWF in NAWM

Correlation Plots: MWF in DAWM

Correlation Plots: MWF in Lesions