2009 Christine McLeavey & Jessica Tsang

Back to Psych 204 Projects 2009

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

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.

Occipital Cleaning (top)

Occipital Cleaning (side)

Results - What you found

Retinotopic models in native space

Some text. Some analysis. Some figures.

Retinotopic models in individual subjects transformed into MNI space

Some text. Some analysis. Some figures.

Retinotopic models in group-averaged data on the MNI template brain

Some text. Some analysis. Some figures. Maybe some equations.

Equations

If you want to use equations, you can use the same formats that are use on wikipedia.
See wikimedia help on formulas for help.
This example of equation use is copied and pasted from wikipedia's article on the DFT.

The sequence of N complex numbers x₀, ..., x_N−1 is transformed into the sequence of N complex numbers X₀, ..., X_N−1 by the DFT according to the formula:

$X_k=\sum _{n=0}^{N-1}{x}_n{e}^{-\frac{2\pi i}{N}kn}k=0,\dots ,N-1$

where i is the imaginary unit and $e^{\frac{2\pi i}{N}}$ is a primitive N'th root of unity. (This expression can also be written in terms of a DFT matrix; when scaled appropriately it becomes a unitary matrix and the X_k can thus be viewed as coefficients of x in an orthonormal basis.)

The transform is sometimes denoted by the symbol $ℱ$, as in $𝐗=ℱ\left\{𝐱\right\}$ or $ℱ\left(𝐱\right)$ or $ℱ𝐱$.

The inverse discrete Fourier transform (IDFT) is given by

$x_n=\frac{1}{N}\sum _{k=0}^{N-1}{X}_k{e}^{\frac{2\pi i}{N}kn}n=0,\dots ,N-1.$

Retinotopic models in group-averaged data projected back into native space

Some text. Some analysis. Some figures.

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.