Ashley

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Background

Studies of cognitive reappraisal have demonstrated that reinterpreting a stimulus can alter emotional responding, yet few studies have examined the durable effects associated with reinterpretation-based emotion regulation strategies. Evidence for the enduring effects of emotion regulation may be found in clinical studies that use cognitive restructuring techniques in cognitive behavioral therapy (CBT) to alleviate anxiety. These techniques are based on cognitive theories of anxiety that suggest these disorders arise from biased cognitions, and therefore changing a person’s thoughts will elicit durable changes in an individual’s emotional responses. Despite the considerable success of CBT for anxiety disorders, durable effects associated with emotion regulation have only recently been examined in the context of a laboratory paradigm. A recent study determined that cognitive restructuring, a technique used in CBT and similar to cognitive reappraisal, can attenuate conditioned fear responses and these effects can last up to 24 hours. The purpose of this study was to examine the neural mechanisms underlying cognitive restructuring to assess whether they overlap with traditional reappraisal techniques and mechanisms of extinction.
In session 1 participants were scanned in a functional magnetic resonance imaging (fMRI) scanner while they were conditioned using images of snakes or spiders that were occasionally paired with a mild shock to the wrist. We also obtained subjective fear reports and electrodermal activity (EDA). After conditioning, half of the participants were randomly assigned to cognitive restructuring (CR) training aimed at decreasing their emotional response to both the shock and the conditioned stimuli, while the other half received no such training. All participants returned 24 hours later to repeat the conditioning session in the scanner. Compared to control participants, CR participants demonstrated a reduction in fear and EDA across sessions. These findings suggest that cognitive restructuring has durable effects on fear responding.

Methods

Subjects

Subjects were 5 healthy volunteers.

MR acquisition

Data were obtained on a GE scanner. Et cetera.

MR Analysis

The MR data was analyzed using mrVista software tools.

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. Et cetera.

PRF model fits

PRF models were fit with a 2-gaussian model.

MNI space

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.

Et cetera.

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 - Resources and related work

References

Software

Appendix I - Code and Data

Code

File:CodeFile.zip

Data

zip file with my data

Appendix II - Work partition (if a group project)

Brian and Bob gave the lectures. Jon mucked around on the wiki.