MSmcDESPOT: Follow-Ups
November 1, 2010
Where We Are
• Baseline cross-section conclusions:– DVF is sensitive to early stages of MS where other
measures are not– DVF correlates with EDSS (R^2 = 0.37 in NAWM)– The addition of a quantitative measure significantly
improves EDSS prediction compared to volumetric atrophy measures alone
• 1yr follow-up scans– 23/26 patients scanned– 5/26 normals scanned and more incoming
Patient Overview
Patient Overview
Patient Overview
Patient Overview
Patient Overview
Correlation Plots
Correlation Plots
Discussion
• Our correlations for DV and DVF were consistent between the baseline and follow up
• Change in DV has a large scale– Could mean we’re quite sensitive to changes in the brain– Need to quantify how much DV varies due to repeatability
error• DV increased for almost every patient, only P020 had
a drop (not shown because no EDSS data)• Currently, we unexpectedly observe a negative
correlation between change in EDSS and change in DV
Normals
• N004, N008, N012• First glimpse at the repeatability of the DV
measure and mcDESPOT-derived MWF maps• We would like to see little change between the
baseline and follow up MWF mapsMean MWF Baseline Std. Dev. Mean MWF 1yr Std. Dev.
N004 0.103 0.0955 0.0952 0.0896
N008 0.125 0.0982 0.0921 0.0882
N012 0.0939 0.0935 0.0834 0.0877
Correlations
Correlations
Correlations
Histograms
Histograms
Histograms
DVBaseline DV (mm^3)
1yr DV (mm^3)
N004 165 3072
N008 0 1636
N012 467 1158
P022 1217 1802
• Using the old baseline mean and std. dev. MWF maps, computed DV for the new normal scans
• Disconcertingly large increase in DV
• Why?– Biased using the baseline
mean derived from normals to get their baseline DV
– Follow-up scan quality?
Baseline SPGR_fa13 Images
1yr SPGR_fa13 Images
Discussion
• I would argue that the reduction in quality in the follow-up scans is comparable between the normals and P022
• Are there ways to deal with the bias?– Cross-validation
• Try many random subsets of the normal population to generate mean and std. dev., choose the map pair that minimizes total DV among all normals
– Ensemble methods• Use all the map pairs and for each, generate a DV mask, then a
voxel is considered demyelinated only if a majority of the DV masks have it as demyelinated
MSmcDESPOT: Looking at Maps
October 29, 2010
Motivation
• Thus far we’ve been studying DV and DVF, which collapses all of our data into a single metric for each patient
• One of the key advantages of mcDESPOT is that it acquires whole brain maps
• We should start looking at our data as whole brain maps– Perhaps different subtypes of MS are associated
with different spatial distributions of MWF
Baseline: Mean MWF Normals
Baseline: Mean MWF CIS
Baseline: Mean MWF RRMS
Baseline: Mean MWF SPMS
Baseline: Mean MWF PPMS
Discussion
• There’s clearly a drop in overall MWF as we progress from CIS to RR to SP to PP
• Can’t really discern any favoring for locations of low MWF other than around the ventricles– DV maps would probably show this better than
anything, should generate a probabilistic DV map
Baseline: Std. Dev. MWF Normals
Baseline: Std. Dev. MWF CIS
Baseline: Std. Dev. MWF RRMS
Baseline: Std. Dev. MWF SPMS
Baseline: Std. Dev. MWF PPMS
Discussion• In normals, MWF has a much lower standard deviation in WM
areas• RR patients seem to have an overall lower standard deviation than
CIS– One interpretation might be that CIS patients are only starting to lose
myelin so there is a lot of variability among them• PP is by far the worst, the variance of MWF among the subjects
seems to be the same throughout the brain– This means that the amount and location of myelin lost among PP
patients varies wildly• Of course standard deviation is a group based measure, not sure
about the direct clinical application for one patient• The 1yr cross-section maps looks like the baseline
Difference Maps
• For each subject, the difference map was computed as MWF_1yr – MWF_baseline– Then the mean difference between patients was
computed for each subtype as well as the standard deviation of the differences
• The following maps may be hard to look at, they are highly non-traditional and probably it’s the first time anyone has ever seen such images
Difference: Mean CIS
Difference: Mean RRMS
Difference: Mean SPMS
Difference: Mean SPMS
Discussion
• There is a clear different between CIS and RR, with RR patients having much larger drops in MWF
• Actually, I feel like RR patients have the most actively changing MWF among all the subtypes looking at these images– Consistent with early stages being the most
active? Have to check the ages of our RR patients.
Ratio Maps
• For each subject, the ratio map was computed as MWF_1yr/MWF_baseline– Then the mean ratio between patients was computed for
each subtype as well as the standard deviation of the ratios• These maps are ugly, it is tough to tell what’s going on
– Ignore the white fringing around the brain, caused by regions of low MWF
– Inside the brain, they would indicate places where lesions with low MWF are• Maybe even they show lesions that have remyelinated a little as
(not as small MWF)/(really small MWF) = big number
Ratio: Mean CIS
Ratio: Mean RRMS
Ratio: Mean SPMS
Ratio: Mean PPMS
Discussion
• Hard to decipher these– CIS seems the most uniform, so the percent
change in MWF is perhaps low, which may not be clear based on just the mean difference maps
Thoughts
• This is more data than someone can humanly process, need to identify key regions
• Unsupervised exploratory data mining techniques could be worth pursuing, since our outcomes of EDSS and ΔEDSS are problematic– Goal here is to find patterns in the data rather
than trying to predict an outcome