Topographical Estimation of Visual Population Receptive Fields by fMRI

The overall goal of this procedure is to estimate accurate visual population receptive fields. This is accomplished by first preparing effective and efficient visual stimuli localized in the visual space to elicit a distinct and robust response. The second step is to image functional brain activity with high visual attention and minimal movements of eye and head.

The final step is to pre-process the acquired data. Ultimately, application of a proposed method to model FMRI signal response to visual stimulus yields the topography of visual population, receptive field, and its model. The main advantage of this method over existing method like direct fitting method is that our method that general require any prior PRF model chosen To prepare a stimulus protocol that is effective in eliciting a reliable retina topic.

Visual response first present bar stimulus drifting across the screen sequentially along eight directions in 45 degrees steps ensure that the motion is in synchrony with the scanner frame acquisition so that the bar moves a step. Once an FMRI frame starts and stays at the new location until the frame ends. To define a field of view in visual angle over which the stimulus is presented, present the moving or flickering checkerboard patterns within the bar to elicit strong visual responses.

Then input the specific parameters such that there are eight evenly spaced directions of motion, and the bars move by half the bar with per frame. Next, scan the brain of a subject in an MRI scanner using a typical echo planer imaging scan that has a 192 frame duration with 24 frames in each direction of motion. Repeat the scans four to eight times to increase signal to noise ratio.

Afterward, set the parameters for the EPI sequence. All procedures are performed using the MATLAB based software toolbox Vista lab that is available on the Vista software site prior to estimating the PRF properties. Several typical FMRI data pre-processing steps are needed, such as head motion correction and alignment of functional volumes to the anatomical scan.

Download the code files from the internet, extract the compressed files, and place them in a preferred location on the local computer. Then add a path to the folder in matlab. Next, set the stimulus parameters using the experiment by selecting Mr.Vista.

Then analysis, then retina topic model, followed by set parameters. Specify the parameters such as the stimulus images, the stimulus size, the canonical hemodynamic function, and the frame rate of the FMRI scanner prior to the PRF estimation. Prepare the initial parameter sets by setting the cross validation sets in TPRF.

Set paras M from the code files. Then divide the time series into at least two subsets that are long enough for the bar to sweep the entire stimulus space. Alternatively, without averaging scans, validate the scans by leaving out one scan for testing and using the remaining scans for training.

Next, input a course parameter set. Then set a fine scale range afterward, set a threshold for the explained variance, such as 0.2 for visually responsive voxels. This threshold is used as the reference for selection of visually responsive voxels.

Then input a set of thresholds for defining the PRF center region in the normalized topography in the file. Next, execute the file to calculate the PRF topography and fit A 2D and isotropic gian. After specifying all the parameters described in this protocol and running the code, obtain the final estimation results.

Here is a typical PRF topography. In the topography. Red color indicates the most responsive area, which shows the PRF center lying on the middle right horizontal meridian in the PRF topography bar patterns across the PRF center structure with low weights are sometimes observed.

They're easily eliminated in the thresholding step shown here. As a comparison between a previous method and the topography based PRF center model, the corresponding percent of explained variance is shown above each model. In this study, T models show higher explained variance in all the examples with more accurate PRF shape.

This figure shows the eccentricity and polar angle maps of the left hemisphere of a subject, and here is a relationship between PRF size and eccentricity. The PRF size increases with eccentricity in visual areas V one to three. After watching this video, you should have a good understanding of how to estimate visual population receptive fields in FMRI.