Max

Back to Psych 204 Projects 2009

Background

fMRI-Adaptation (fMRI-A) refers to the phenomenon that, as a region of cortex is activated by a stimulus repeatedly, it becomes less responsive overall to successive presentations of the stimulus. While the exact mechanisms of adaptation vary from region to region, fMRI-A in visual cortex has been studied in depth (see Grill-Spector et al., 2006, Weiner et al., 2010). In order study fMRI-A, stimuli must often be presented in rapid succession using event-related designs. These designs allow experimenters to study temporally shorter processes, but they come with several methodological complications, specifically in data analysis techniques.

The shorter inter-stimulus intervals involved in event-related designs lead to signal overlap across different trial types and may induce dependencies and nonlinearities between study events and/or conditions. Because of these issues, standard General Linear Model (GLM) analyses may be less precise and appropriate than usual. Choices as to how to analyze timecourse data in event-related designs can have important consequences for significance detection, data visualization, and relative activations to different types of stimuli. We hope to explore these differences in the current study.

Region of Interest (ROI) GLM analyses provide crucial information about regional selectivity and activation, yet they oversimplify data by averaging activation across an entire region down to a single timecourse. Multi-voxel patterns can be used to further investigate and characterize the distribution of activity within a region by cross-correlating activation on a voxel-by-voxel basis between trial types. In ROI analyses, different voxels may drive the same signal change within an ROI. Using MVPA, distributed patterns of activation may be correlated and one is able to test effects that a GLM would not be able to capture.

Methods

MR acquisition

Data were obtained on a 3-Tesla GE scanner. 12 slices were acquired at a resolution of 1.5 x 1.5 x 3.0 mm. TR was 1,000 ms and TE was 30 ms. Slices were acquired using a two-shot T2*-sensitive spiral acquisition sequence. The flip angle was 77 degrees and the field of view was 192 mm.

MR Analysis

The MR data was analyzed using mrVista software tools.

Pre-processing

Data were pre-processed (motion corrected, slice time corrected, etc.) by Weiner and colleagues.

HRFs vs. Deconvolution

File:ROI GLM Boynton

Results

HRFs vs. Deconvolution

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 - Resources and related work

References

Software

Appendix I - Code and Data

Code

File:CodeFile.zip

Data

zip file with my data