ISMRM 2011 E-Poster #4643 mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone J.Su1, H.H.Kitzler2, M.Zeineh1, S.C.Deoni3, C.Harper-Little2, A.Leung2, M.Kremenchutzky2, and B.K.Rutt1 1Stanford U, CA, USA, 2TU Dresden, SN, Germany, 2U of Western Ontario, ON, Canada, 3Brown U, RI, USA Title: I prefer the wording “Compared to Atrophy Measures Alone” over “Compared to Only Atrophy Measures” Are we allowed to change the title so that it’s different from the original abstract? Declaration of Conflict of Interest or Relationship I have no conflicts of interest to disclose with regard to the subject matter of this presentation.

mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Background Conventional MRI measures such as lesion load have been criticized with adding little new information on top of clinical scores for multiple sclerosis (MS) patients Measures that quantify the hidden burden of disease in white matter are urgently needed

mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Purpose To apply mcDESPOT, a whole-brain, myelin-selective, multi-component relaxometric imaging method, in a pilot MS study Assess if the method can explain differences in disease course and severity by uncovering the burden of disease in normal-appearing white matter (NAWM)

Study Demographic Data Healthy Controls All Patients CIS RRMS SPMS mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Study Demographic Data Healthy Controls All Patients CIS RRMS SPMS PPMS N 26 10 5 6 Mean age, yr (SD) 42 (13) 49 (12) 41 48 58 (7) 55 Male/Female ratio 10/16 7/19 3/7 0/5 0/6 4/1 Mean disease duration, yr — 14 2 (2) 15 (10) 28 (8) 20 Mean EDSS score 3.6 (2.4) 1.7 (0.9) 2.0 (1.7) 6.4 (1.1) 5.6

Scanning Methods 1.5T GE Signa HDx, 8-channel head RF coil mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Scanning Methods 1.5T GE Signa HDx, 8-channel head RF coil mcDESPOT: 2mm3 isotropic covering whole brain, about 15 min. 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}° 2D T2 FLAIR: 0.86 mm2 in-plane and 3mm slice resolution 3D T1 IR-SPGR: 1mm3 resolution with pre/post Gd contrast

The Technique

Processing Methods: MWF mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: MWF Linearly coregister and brain extract mcDESPOT SPGR and SSFP images with FSL1 Find myelin water fraction maps using the established mcDESPOT fitting algorithm2 Myelin Water Fraction 1FMRIB Software Library. 2Deoni et al., Magn Reson Med. 2008 Dec;60(6):1372-87

mcDESPOT Maps in Normal T1single T1slow T1fast MWF 0 – 2345ms 0 – 1172ms 0 – 555ms 0 – 0.234 0 – 328ms 0 – 123ms 0 – 9.26ms 0 – 137ms T2single T2slow T2fast Residence Time

Processing Methods: Demyelination mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: Demyelination Non-linearly register mcDESPOT MWF maps to MNI152 standard space Combine normals together to form mean and standard deviation MWF volumes For each subject, calculate a z-score ([x – μ]/σ) at every voxel to determine if it is significantly demyelinated, i.e. MWF < -4σ below the mean Demyelinated Voxels

Processing Methods: WM mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: WM Brain extract MPRAGE images Segment white and gray matter with SPM83 Filter tissue masks to reduce noise then manually edit by a trained neuroradiologist Calculate parenchymal volume fraction (PVF) as WM+GM divided by the brain mask volume FLAIR WM 3Statistical Parametric Mapping software package.

Processing Methods: Lesions & DAWM mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: Lesions & DAWM Non-linearly register T2-FLAIR images to MNI152 standard space Combine normals together to form mean and standard deviation volumes Segment lesions as those voxels with z-score > +4 and diffusely abnormal white matter > +2 Edit masks by a trained neurologist DAWM Lesions

Processing Methods: NAWM & DVF mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Processing Methods: NAWM & DVF Segment normal-appearing white matter (NAWM) as WM – DAWM – lesions Find demyelinated volume fraction (DVF) Sum the volume of demyelinated voxels in each tissue compartment and normalize by the compartment’s volume # demy. voxels in compartment * voxel volume / compartment volume Normal-Appearing White Matter

Segmentations and DV FLAIR WM NAWM DAWM Lesions MWF Demyelinated mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Segmentations and DV FLAIR WM NAWM DAWM Lesions After all that processing, we can visualize the end result in a very intuitive way. MWF Demyelinated Voxels DV in NAWM DV in DAWM DV in Lesions

mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Statistical Methods Use rank sum tests to compare patient groups to normals along different measures Perform an exhaustive search to find the best multiple linear regression model for EDSS using Mallows’ Cp4 criterion among 21 possible image-derived predictors: PVF log-DVF in whole brain, log-DVF in WM, log-DVF in NAWM, log-DVF in lesions log-DV in those four compartments mean MWF in those four compartments volumes of those four compartments (lesion volume = T2 lesion load) volume fractions of those four compartments with respect to the whole brain mask volume 4Mallows C. Some comments on Cp. Technometrics. 1973;15(4):661-75.

Results: Mean MWF in Compartments mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Results: Mean MWF in Compartments Dotted line shows mean MWF in WM for normals. Rank sum testing was done for each bar against this Testing was also done for RRMS vs. SPMS and CIS vs. RRMS, any significant differences are shown with a connecting bracket Significance levels: * p < 0.05 ** p < 0.01 *** p < 0.001.

Results: DVF in Compartments mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Results: DVF in Compartments Dotted line shows demyelinated volume fraction in WM for healthy controls With DVF, all patient subclasses were significantly different from healthy controls PVF, however, fails to distinguish CIS and RR patients from normals

Results: Correlations with EDSS mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Results: Correlations with EDSS Lesion load correlates poorly with EDSS PVF and DVF are stronger indicators of decline

Results: Multiple Linear Regression mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Results: Multiple Linear Regression The best linear model for EDSS contains PVF (p < 0.001), mean MWF in whole brain (p < 0.001), and WM volume fraction (p < 0.01) Whole-brain MWF and WM volume fraction significantly improve the prediction of EDSS over that produced by PVF alone Explains 76% of the variance in EDSS (R2 = 0.76, adjusted R2 = 0.73) compared to 56% with only PVF

Discussion & Conclusions mcDESPOT-Derived MWF Improves EDSS Prediction in MS Patients Compared to Atrophy Measures Alone ISMRM 2011 #4643 Discussion & Conclusions DVF is able to differentiate CIS and RRMS patients from normals, whereas other measures such as PVF and mean MWF cannot The invisible burden of disease may be more important than lesions in determining disability, since we observe a higher correlation of EDSS with DVF in NAWM than lesion load A combination of established atrophy measures with new mcDESPOT-derived MWF are more capable in accurately estimating disability than either quantity alone

ISMRM 2011 E-Poster #7224 Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT J.Su1, H.H.Kitzler2, M.Zeineh1, S.C.Deoni3, C.Harper-Little2, A.Leung2, M.Kremenchutzky2, and B.K.Rutt1 1Stanford U, CA, USA, 2TU Dresden, SN, Germany, 2U of Western Ontario, ON, Canada, 3Brown U, RI, USA Declaration of Conflict of Interest or Relationship I have no conflicts of interest to disclose with regard to the subject matter of this presentation.

Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Purpose To apply mcDESPOT, a whole-brain, myelin-selective, multi-component relaxometric imaging method, in a 1-year longitudinal pilot MS study Assess the ability of the method to sense different rates of demyelination for different MS courses and compare it to changes in EDSS

Study Demographic Data Healthy Controls All Patients CIS RRMS SPMS Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Study Demographic Data Healthy Controls All Patients CIS RRMS SPMS PPMS N at baseline 26 10 5 6 N at 1-year 4 23 9 Mean age at baseline, yr (SD) 42 (13) 49 (12) 41 48 58 (7) 55 Mean disease duration at baseline, yr — 14 2 (2) 15 (10) 28 (8) 20 Mean EDSS score at baseline 3.6 (2.4) 1.7 (0.9) 2.0 (1.7) 6.4 (1.1) 5.6 N with EDSS change 3 1

Processing Methods: 1-year & DVF Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Processing Methods: 1-year & DVF At 1-year, demyelinated voxels are based on z-scores with respect to the combined baseline and 1-year normal group Find demyelinated volume fraction (DVF) Sum the volume of demyelinated voxels and normalize by brain mask volume # demy. voxels in compartment * voxel volume / compartment volume

Results: Mean MWF in Whole Brain Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Results: Mean MWF in Whole Brain Dotted line shows mean MWF for normals. Rank sum testing was done for each bar against this value Testing was also done for RRMS vs. SPMS and CIS vs. RRMS, any significant differences are shown with a connecting bracket Significance levels: * p < 0.05 ** p < 0.01 *** p < 0.001.

Results: DVF Change Colors denote subject type Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Results: DVF Change Colors denote subject type Arrowheads indicate the direction of change and the DVF at 1-year Dashed lines show subjects who also had a change in EDSS PPMS SPMS RRMS CIS Normals

Results: DVF in Whole Brain Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Results: DVF in Whole Brain Dotted line shows mean demyelinated volume fraction change for normals Definite MS patients are losing significantly more myelin than normals Progressive patients have a greater rate of demyelination In previous bar charts, you used connecting brackets to show significant differences, but here you aren’t. Any reason for that? There was no significance difference between CIS vs RR and RR vs SP despite the apparent visual differential in the bars.

Discussion & Conclusions Sensitive Detection of Myelination Change in Multiple Sclerosis by mcDESPOT ISMRM 2011 #7224 Discussion & Conclusions The normal pool at 1-year is currently too small to show significance for the changes in mean MWF DVF, however, is sensitive enough to show statistically significant changes in brain myelination over the study period Progressive patients show greater disease decline that are not reflected in their EDSS disability score EDSS and DVF measure different aspects of the disease. Patients with changes in EDSS did not actually have the largest demyelination changes