introduction: the lesion-centered view on ms
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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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
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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
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MSmcDESPOT:Findings and Figures
Jason Su
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Present (almost) final results Judge figures, how to improve their
readability and presentation for publication Discussion of further analysis
Goals
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MWF Comparison
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Correlation Plots: Whole Brain
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Correlation Plots: White Matter
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Correlation Plots: NAWM
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Correlation Plots: Lesion Load
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Correlation Plots: PVF
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Correlation Plots: PVF vs. DV
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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
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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
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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
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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
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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
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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
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Model Selection
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Model Selection: Diagnostics
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Thoughts?
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Correlation Plots: MWF in Brain
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Correlation Plots: MWF in WM
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Correlation Plots: MWF in NAWM
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Correlation Plots: MWF in DAWM
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Correlation Plots: MWF in Lesions