Brittany

The Relationship between fMRI adaptation and category selectivity

This project investigates the link between adaptation and areas of the cortex linked to face recognition and limb recognition using an event related design.

Background

fMRI Adaptation is a phenomena that takes place in higher order cortical areas in which the BOLD response signal decreases in response to repeated identical visual stimuli. Adaptation has been shown to occur in higher order visual areas but not early visual areas such as V1 [1]. This study investigates the relationship between object selectivity and fMRI adapation. More specifically, it will examine face selective areas as well as body part selective areas with regards to adaptation. Furthermore, it will look at the differences between adaptation within the ROIs of those two specific categories as well as the similarities in adaptation across various anatomical brain areas.

Methods

Pre-processed data was used from the lab of Kalanit Grill-Spector from the parts of an experiment outlined below [2].

Subjects

The data from the right hemisphere of one subject, a male in his late 20s, was analyzed.

MR acquisition

The subject participated in two experiments, an event related design followed by a block design localizer scan. The first, event related design, consisted of 8 runs of 156 trials lasting for 2 seconds each. Each trial consisted of the presentation of an image of either a face, limb, car, or house for 1000ms followed by a 1000ms blank. Within each run, only two of the images were repeated six times, the rest not repeated at all. None of the images were repeated across scans.

The second experiment was a block design used to identify category selective areas within the ventral stream. Blocks were 12 seconds long with a 750ms stimulus presentation period followed by a 250 ms blank period. Each run consisted of 32 blocks, 4 blocks for each category (faces, limbs, flowers, cars, guitars, houses, and scrambled) as well as four blank blocks.

MR Analysis

As mentioned above, the data I analyzed was pre-processed. That high resolution MR data was was then analyzed using mrVista software tools.

ROIs

Using the localizer data from the second experiment, I created ROIs specific for areas selective for faces and areas selective for limbs. I chose the 4 different face selective areas seen in figure 1.

To add an image, simply put text like this inside double brackets 'MyFile.jpg | My figure caption'. When you save this text and click on the link, the wiki will ask you for the figure. 

Below is another example of a reinotopic map in a different subject.
Figure 2

Once you upload the images, they look like this. Note that you can control many features of the images, like whether to show a thumbnail, and the display resolution.

Figure 3

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:

$X_{k}=\sum _{n=0}^{N-1}x_{n}e^{-{\frac {2\pi i}{N}}kn}\quad \quad k=0,\dots ,N-1$

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 ${\mathcal {F}}$, as in $\mathbf {X} ={\mathcal {F}}\left\{\mathbf {x} \right\}$ or ${\mathcal {F}}\left(\mathbf {x} \right)$ or ${\mathcal {F}}\mathbf {x}$.

The inverse discrete Fourier transform (IDFT) is given by

$x_{n}={\frac {1}{N}}\sum _{k=0}^{N-1}X_{k}e^{{\frac {2\pi i}{N}}kn}\quad \quad n=0,\dots ,N-1.$

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