Nicole

Back to Psych 204B Projects 2012

Background

Broad Context

- These data were collected as part of a larger study of transfer and the effectiveness of formula learning. - Materials: Polygon problem (IMAGES, etc.)

Trial Paradigm

- In each trial, participants saw the stimulus, computed the answer and responded using the trackball. - The study timing was self-paced as in (ZEFFIRO REFERNCE) with jittering the ISI between each trial

Study Design

- Subjects received training before the scanner and then completed 3 trial blocks

-- In the present investigation, we're only looking at 7 participants in condition 2 and only considering the 2 formula blocks (not the choice block and not considering the transfer questions outside of the scanner).

The present investigation: Subject Motion

(Example of subject motion graph(s).)

Methods

Subjects

Seven healthy volunteers participated in this study. Their mean age was 23.8 years old and four participants were male. These data were collected as part of a larger sample of 16 healthy volunteers (mean age 23.2 years old).

MR acquisition

Data were obtained on a 3T GE scanner at Stanford's Center for Cognitive and Neurobiological Imaging (CNI)

MR Analysis

The MR data was analyzed using SPM software tools. (PUT LINK TO SPM HERE!) Specifically, ART repair was used (LINK)

Pre-processing

All data were slice-time corrected, motion corrected, and repeated scans were averaged together to create a single average scan for each subject.

MNI space

MNI is an abbreviation for Montreal Neurological Institute.

After a pRF model was solved for each subject, the model was trasnformed into MNI template space. This was done by first aligning the high resolution t1-weighted anatomical scan from each subject to an MNI template. Since the pRF model was coregistered to the t1-anatomical scan, the same alignment matrix could then be applied to the pRF model.
Once each pRF model was aligned to MNI space, 4 model parameters - x, y, sigma, and r^2 - were averaged across each of the 6 subjects in each voxel.

Group Level Analyses

No Motion Correction

Some text. Some analysis. Some figures.

Using Motion Correction

Some text. Some analysis. Some figures.

Dropping A Subject With Too Much Motion

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

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

Conclusions

Here is where you say what your results mean.

References - Resources and related work

Software

Art Repair

For more information about the SPM plugin ArtRepair, see: Art Repair