2009 Christine McLeavey & Jessica Tsang

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Testing the Ability of TBSS to Detect Tract Variations Between Groups

TBSS is a popular program [1] for exploring anatomical connectivity in the brain by analyzing anisotropic diffusion of water in white matter tracts. In this project, we create an artificial difference between the diffusion patterns of two otherwise similar groups, and determine TBSS's ability to find this difference.

Background

Tract-Based Spatial Statistics

TBSS provides a popular alternative to Voxel-Based Morphometry (VBM) analysis. It uses the following algorithm:

1. Align multiple FA images using a voxelwise nonlinear registration. TBSS attempts to strike a middle ground between low-dimensional warping (keeping brain structure, but losing alignment) and high-dimensional warping (good alignment, but extreme warping of original images). Alignment is best when a single subject's FA image is chosen to be the target. TBSS exhaustively tests using every individual subject as a target for all the others, and then chooses the best result.
2. Create mean FA skeleton. TBSS averages the transformed FA images to create a mean FA image. Tract skeleton generation then aims to create a single line through the center of the tract. It does this by sorting through voxels perpendicular to the direction of the tract, and choosing the one with the highest FA as the center of the tract. This introduces the possibility that a strong neighbouring tract will mistakenly be included as the tract of interest, hence our considering the Arcuate Tract to be more difficult than the Occipital Tract.
3. Project individual subjects' FA onto group skeleton. For each individual subject, TBSS looks in the perpendicular tract direction and chooses the voxels with the maximum FA value, assigning this to the skeleton voxel. This helps to fix any misalignment from step 1.
4. Run statistics to test for statistically significant FA differences between groups. Correct for multiple-comparisons, recognizing that by pure chance, statistically significant differences will be found if many voxels are compared.

Tracts of Interest

We consider the Arcuate tract to be the more difficult one for TBSS, as there are several other tracts in the neighbourhood that can potentially confuse tract assignment.

Occipital Tract

Occipital Tract

Occipital Tract

Arcuate Tract

Arcuate Tract

Methods

Assignment of subjects into two groups

This study began with data from 55 subjects, ages 7 to 12. 3 subjects were not included in the analysis, as they were lacking Arcuate Tract data. We assigned the remaining 52 into two groups, prioritizing:
1. gender
2. age
3. IQ
as items to match. We performed T-tests on the age, IQ, ADHD score, and mean FA distributions of the two groups, to demonstrate no statistically significant separation between the two.

Details of the group assignment are available in: File:Groups.pdf

Cleaning Occipital Tract Data

Occipital tract data was opened using MrDiffusion, and then tracts pushed into CINCH for easier viewing.
Tracts were cleaned according to the following principles (and records kept describing the cleaning for each individual brain):
1. Thin tracts clearly not connected to a main tract were removed.
2. Tracts that did not begin and end in the right plane were removed.
3. Tracts that crossed the midline more than once were removed.
4. Tracts that looped into other quadrants of the brain were removed.

Figures show a representative case. Tracts in red represent the final "cleaned" version. Those in blue indicate tracts that would be hand-removed, following the above algorithm.

Occipital Cleaning (top)

Occipital Cleaning (side)

Mean FA Histograms

Mean FA (Arcuate Tract)

Mean FA (Occipital Tract)

Shift FA in Group 1

We determine from the mean FA histograms that in order to create a ~1.5 standard deviation shift in Group 1, we would need to lower the FA by 0.85%. The following figures demonstrate the goal outcome of a script written to produce this FA shift.

Hypothetical Shift (Arcuate)

Hypothetical Shift (Occipital)

In order to do this correctly, we need to weight the FA shifting for each voxel by the fiber density of that voxel.

Fiber density (Occipital)

We use the formula:
newFA = originalFA * [1-WeightedEffectSize]
WeightedEffectSize = .085*FiberDensity

TBSS Analysis

We now enter the two groups (Adjusted Group 1 and Unadjusted Group 2) into TBSS and then run statistics on the outcomes to determine points of significant difference identified between the two. The results are shown in the next section.

Results

TBSS Mean Skeleton

Mean FA Skeleton from 52 kids

Statistics

Occipital Results

Arcuate Results

Trouble Shooting

After some searching, we determined that the top part of the brain is missing in the TBSS results because in one subject, data from the top part of the brain was also missing. TBSS would then throw out a section when it found it did not have data for every subject. We might exclude this subject from the study, however the tracts of interest are not near the missing data, and we elected to keep the subject in the study.

Missing data at top of brain

Conclusions

If these results are to be trusted, they would create a significant caveat for TBSS users. However, it must be emphasized that these are preliminary studies, and at this point it is quite likely that the problem lies in the algorithm used to shift the mean FA scores, or in some other part of our methodology.

Future Work

1. Shift Group 1 Mean FA scores by a larger amount.
2. Examine Mean FA histograms for Group 1 post-shift, to ensure they match predicted values.
3. Repeat this experiment using other tracts, or using shifts in only part of a given tract.
4. Run similar tests using VBM and compare to results with TBSS.

References

[1] Smith, Steven M. et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 31 (2006) 1487-1505.

Appendix I - Code and Data

Code

Matlab Code for Reducing FA

Data

zip file with my data

Appendix II - Work partition

Christine is new to fMRI work and did much of the brunt work - she divided the kids into two groups and showed the groups to be well matched statistically. She did the cleaning of the occipital tracts for each of the 55 kids and wrote the wiki page. Jessica provided the brains and experience and devised the way to reduce the FA in the tracts of one group, ran the results through TBSS, ran statistics on those results, and generally taught Christine the ropes.