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