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

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 kids into two groups

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

Hypothetical Shift (Arcuate)
Hypothetical Shift (Occipital)

TBSS Analysis

Results

TBSS Mean Skeleton

Mean FA Skeleton from 52 kids

Statistics

Occipital Results
Arcuate Results

Trouble Shooting

Missing data at top of brain

Conclusions

Here is where you say what your results mean.

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.