MSmcDESPOT : Follow-Ups

1. MSmcDESPOT: Follow-Ups November 1, 2010

2. 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

3. Patient Overview

4. Patient Overview

5. Patient Overview

6. Patient Overview

7. Patient Overview

8. Correlation Plots

9. Correlation Plots

10. 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

11. 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 maps

12. Correlations

13. Correlations

14. Correlations

15. Histograms

16. Histograms

17. Histograms

18. DV • 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?

19. Baseline SPGR_fa13 Images

20. 1yr SPGR_fa13 Images

21. 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

22. MSmcDESPOT: Looking at Maps October 29, 2010

23. 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

24. Baseline: Mean MWF Normals

25. Baseline: Mean MWF CIS

26. Baseline: Mean MWF RRMS

27. Baseline: Mean MWF SPMS

28. Baseline: Mean MWF PPMS

29. 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

30. Baseline: Std. Dev. MWF Normals

31. Baseline: Std. Dev. MWF CIS

32. Baseline: Std. Dev. MWF RRMS

33. Baseline: Std. Dev. MWF SPMS

34. Baseline: Std. Dev. MWF PPMS

35. 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

36. 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

37. Difference: Mean CIS

38. Difference: Mean RRMS

39. Difference: Mean SPMS

40. Difference: Mean SPMS

41. 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.

42. 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

43. Ratio: Mean CIS

44. Ratio: Mean RRMS

45. Ratio: Mean SPMS

46. Ratio: Mean PPMS

47. 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

48. 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