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

1yr: Mean MWF CIS

1yr: Mean MWF RRMS

1yr: Mean MWF SPMS

1yr: Mean MWF PPMS

1yr: Std. Dev. MWF CIS

1yr : Std. Dev. MWF RRMS

1yr: Std. Dev. MWF SPMS

1yr: Std. Dev. MWF PPMS

Discussion

• The 1yr cross-section looks like the Baseline more or less

• Our previous observations still seem to hold

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.

Difference: Std. Dev. CIS

Difference: Std. Dev. RRMS

Difference: Std. Dev. SPMS

Difference: Std. Dev. PPMS

Discussion

• Here again the RR seems to have lower variance than CIS– The interpretation is different though, this means

that the variation of the change in MWF is low– Perhaps RR patients are losing similar amounts of

myelin in the same areas of the brain?• Need to somehow show both mean MWF difference

and its standard deviation together

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

Ratio: Std. Dev. CIS

Ratio: Std. Dev. RRMS

Ratio: Std. Dev. SPMS

Ratio: Std. Dev. SPMS

Discussion

• CIS and RR seem about the same here• Clearly there’s higher variability for the change

in MWF among progressive patients as was also visible in the standard deviation 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