MRIcron Peristimulus Plots


SPM and FSL are powerful tools for analyzing fMRI data. However, the statistical maps most people generate with these tools can be difficult to interpret. Generating peristimulus plots can allow you to get a better idea of what your data actually looks like, and can help you determine if a region shows an increased amplitude of activity or a more sustained response to a stimuli. To generate peristimulus plots you will need:
  1. MRIcron.
  2. A 4D fMRI dataset (typically motion corrected and smoothed)
  3. A FSL format 3-column text file for each condition you wish to analyze.
  4. Optional: regions of interest for specific brain regions.
You can also analyze the tutorial sample dataset which includes NIfTI images in the file ''.

Basic Usage

Here are step-by-step instructions
  1. launching MRIcron. Then choose '4D traces' from the View menu.
  2. Press the 'Open Data' button.
  3. MRIcron will now display a timeline for your data. If you have loaded multiple regions, a separate line displays each ROI. If you have not selected any ROIs, you will be shown the currently selected voxel - use MRIcron's main window to select a voxel you want to view and then press the red refresh button in the timeline window to see the timeiline for this voxel. Note that if you have loaded any event onsets, each condition is shown as a unique color of vertical stripes - for example in the example left hand taps are shown as red bars and right taps are shown as green bars. Note with the example datasets that left taps are followed by increases in signal for the right ROI, while right taps are followed by increasing signal in the left hemisphere.
  4. Before generating peristimulus plots, make sure that the TR is accurately set. Our sample data has a TR of 3 seconds, and this is correctly reported in the image file, so MRIcron correctly reports a TR of 3 seconds. If your TR is incorrect, the events will not be correctly aligned with your images.
  5. Press the 'Plot' button to generate phase-locked peristimulus plot. You will want to check the settings for your peristimulus plot
    1. The bin width sets the resolution for plot - smaller bins are more precise but noiser. By default, the bin width is set to your TR, in our example 3 seconds.
    2. The pre-stimulus bins sets the number of baseline bins. In our example we are setting 4 bins (12 seconds).
    3. The number of post-stimulus bins plot signal changes after an event has been presented. Remember that fMRI signals are sluggish, and take 5-6 seconds to peak. For the example, set this to 14 (42 seconds).
    4. If you slice time corrected your data, check the appropriate box. Event times will be adjusted for the acquisition of the middle-slice in your volume (e.g. all of your onsets will be adjusted by 0.5 TR).
    5. The save peristimulus volume button allows you to save a separate 3D dataset for each time bin. This is an advanced feature we will discuss later.
  6. MRIcron generates a peristimulus plot. Different colors are used for the different conditions, while different line styles are used for the different regions of interest. For our example, note that the right hemisphere shows a response for left but not right taps, while the reverse is true for the left hemisphere. The peak amplitude is about 1% signal change. While this effect sounds small, note that we are averaging over a large number of voxels which in this case were selected baseed solely on anatomy, rather than post-hoc selecting the single most active voxel. Also note that the error bars are rather small.

Removing Regressors

The recent versions of MRIcron (since December, 2007) allow you to remove the influence of other conditions. This feature can remove expected but uninteresting variance in your data, resulting in a better signal to noise. The images below illustrate how this works. Consider an event related design where an individual occasionally taps her left hand, and at other times she taps her right hand. If we looked at the motor cortex of the left hemisphere (which predominantly controls the individual's right hand) our data might look like this:
The observed signal is actually a combination of brain activity due to movement of the right hand, somewhat weaker activity following movement of the left hand and some noise. Since we know when the individual moved their hand, and we know the approximate pattern of the hemodynamic response, we can use multiple linear regression to estimate relatively pure signals of left hand movement and right handmovement, as shown here:
This can help visualize our peristimulus plots. If we were to make a peristimulus plot of the raw data for the left hand movement (top panel of the image below) the observed data would have an unusual latency and a lot of noise, because this plot includes not only responses for the left hand but also noise from the brain activation following movement of the right hand. The lower plot shows the effect of removing the irrelevant regressors. Note that this reveals a much purer effect. By default, MRIcron will plot the observed data after regression (e.g. a mixture of the signal of interest plus noise). However, you can alternatively choose to see the 'modeled rather than observed data'. This will show you the convolved effect of interest, with the amplitude estimated by the multiple regression. This is useful for seeing if the observed data has roughly the same latency as predicted by the HRF. If this differs a little bit (<1 sec), you will want to include temporal derivatives in your model. If the latency is dramatically different, you should check to ensure that your onset file is correct and consider using a tool like FLOBS to analyze your data.

Advanced Usage