Jim

Background

This quarter, we've talked a great deal about the importance of making deliberate choices at ever step of imaging analysis, and not trusting a 'black-box' pipeline. In this spirit, I decided to take a closer look at a typical preprocessing stream used in my lab. In doing so, I noticed variation in the preprocessing steps taken in my lab at even the earliest steps of preprocessing. Specifically, noticed some variation in the implementation of volume registration, and how decisions about the inputs to this volume registration can affect the motion parameters by this process. Accordingly, for my project, I decided to take a look at this variation. To do so I performed volume registration on previously collected imaging data from two different subjects, varying the chosen reference volume and the amount of data included in the imaging process.

Volume Registration

An important early step of fMRI image preprocessing is volume registration. During scanning, there is inevitably some variance in the position of a participant’s brain. Participants (despite even the best intentions) move during a scan, and the brain will also move in the skull thanks to pulsation. Volume registration is one way of (partially) accounting for this. During volume registration, each acquired volume (full set of slices acquired in a single TR) is aligned to a template volume. During this process movement is estimated at each TR by measuring the displacement of an image from the reference image in 6 different dimensions, displacement in the x, y and z planes, and translation in terms of roll, pitch and yaw. These estimates are commonly used later in data analysis as nuisance parameters in the GLM (generalized linear model).

Methods

Data Acquisition

Two female participants aged 40 and 50 were scanned as a part of an ongoing project studing intergenerational risk factors for depression. Scans were conducted on a 3T scanner at the Lucas Center. Scans were 314 Volumes long, with a TR of 2000 MS and acquired with a flip angle=70 degrees, TE = 40 msec, FOV 204 and a T2* weighted sprial in/out pulse sequence. Volumes were composed of 24 axial slices, 3.0 mm per slice, 3.75 x 3.75 mm in plane resolution.

Data Analysis

Data analysis was conducted using AFNI (Analysis of Functional NeuroImaging), a data analysis program developed by Bob Cox. To test factors affecting the motion parameters, volume registration was conducted varying two inputs of interest. The first was the input data; during fMRI acquisition, the MR scanner typically takes several TRs to reach equilibrium. While the data acquired during the first few TRs is typically discarded prior to image analysis; some preprocessing scripts trim this initial TRs later in preprocessing. Accordingly, I conducted volume registration on both a full data set that included these initial TRs, and a trimmed data set that included only the ‘settled’ TRs.

Additionally, I also varied the volume chosen as the template volume for registration. For each data set (‘trimmed’ and ‘settled’), I conducted volume registration using either the initial (0), 90th or 300th TR of each data set as the template volume. These data analyses were performed for each subject. The 90th and initial volumes were chosen because they were both options chosen in preprocessing scripts for our lab, the later TR was chosen as it seemed interesting. These analyses were conducted on both participants’ data. The outcome measures for these analyses were the maximum displacement a volume as provided by 3dVolReg in afni, as well as the variance, means, and correlation (across analysis choice) of the 6 motion parameters produced by volume registration. I hypothesized that Volume registration conducted on the full data set with the initial TR as the reference volume would produce the worst motion parameters (highest variance, greatest motion displacement), but wanted to investigate whether including the initial TRs would distort motion parameters even when a later (90,300) TR was chosen as the reference volume.

Background

A point of emphasis this quarter has been understanding all the steps necessary for neuroimaging analysis, and

This quarter, we've talked a great deal about the importance of making deliberate choices at ever step of imaging analysis, and not trusting a 'black-box' pipeline. In this spirit, I decided to take a closer look at a typical preprocessing stream used in my lab.

You can use subsections if you like. Below is an example of a retinotopic map. Or, to be precise, below will be an example of a retinotopic map once the image is uploaded. 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

MNI space

MNI is an abbreviation for Montreal Neurological Institute.

Methods

Measuring retinotopic maps

Retinotopic maps were obtained in 5 subjects using Population Receptive Field mapping methods Dumoulin and Wandell (2008). These data were collected for another research project in the Wandell lab. We re-analyzed the data for this project, as described below.

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.