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MSmcDESPOT : Looking at Maps

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

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MSmcDESPOT : Looking at Maps

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  1. MSmcDESPOT: Looking at Maps October 29, 2010

  2. 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

  3. Baseline: Mean MWF Normals

  4. Baseline: Mean MWF CIS

  5. Baseline: Mean MWF RRMS

  6. Baseline: Mean MWF SPMS

  7. Baseline: Mean MWF PPMS

  8. 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

  9. Baseline: Std. Dev. MWF Normals

  10. Baseline: Std. Dev. MWF CIS

  11. Baseline: Std. Dev. MWF RRMS

  12. Baseline: Std. Dev. MWF SPMS

  13. Baseline: Std. Dev. MWF PPMS

  14. 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

  15. 1yr: Mean MWF CIS

  16. 1yr: Mean MWF RRMS

  17. 1yr: Mean MWF SPMS

  18. 1yr: Mean MWF PPMS

  19. 1yr: Std. Dev. MWF CIS

  20. 1yr : Std. Dev. MWF RRMS

  21. 1yr: Std. Dev. MWF SPMS

  22. 1yr: Std. Dev. MWF PPMS

  23. Discussion • The 1yr cross-section looks like the Baseline more or less • Our previous observations still seem to hold

  24. 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

  25. Difference: Mean CIS

  26. Difference: Mean RRMS

  27. Difference: Mean SPMS

  28. Difference: Mean SPMS

  29. 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.

  30. Difference: Std. Dev. CIS

  31. Difference: Std. Dev. RRMS

  32. Difference: Std. Dev. SPMS

  33. Difference: Std. Dev. PPMS

  34. 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

  35. 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

  36. Ratio: Mean CIS

  37. Ratio: Mean RRMS

  38. Ratio: Mean SPMS

  39. Ratio: Mean PPMS

  40. 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

  41. Ratio: Std. Dev. CIS

  42. Ratio: Std. Dev. RRMS

  43. Ratio: Std. Dev. SPMS

  44. Ratio: Std. Dev. SPMS

  45. 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

  46. 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

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