FEAT Extras Practical

This practical contains an overview of some advanced analysis methods available in FEAT. It leads you through some of the more advanced usage and concepts in both single-session and higher-level FEAT analyses. Feel free to do the latter two sections in a different order if you are particularly interested in any of them.

Custom Waveforms

It is possible to specify EVs in FEAT using Custom Waveforms. Here, a simulated dataset (with reduced FOV) has been generated with some event-related conditions to model.

cd ~/fsl_course_data/fmri_extras/custom_waveforms

Open FEAT (Feat & [or Feat_gui & on Mac]) and follow the instructions below.

Select the data artdata.nii.gz (you may get a warning about pre-set options, you can ignore it). This dataset has already been preprocessed, so change Full analysis to Statistics .
Go to Full model setup (under the Stats tab) and set 3 EVs (there are three different things going on in the data with their own timings). We will convolve all of these EVs with a gamma function.
Condition 1 is a boxcar (square wave) design so set EV1 to be Off=20 On=20 Phase=10.
Condition 2 is a jittered event-related design with inter-stimulus-interval (ISI) > 20s and mean(ISI)=25s; so set EV2's Basic shape to custom (3 column format); select the filename as jittered_isi_custom_file.dat. In the terminal have a quick look at the custom file with the less command. The 3 columns are explained in the FEAT user guide.
Condition 3 is randomised event-related with ISI>3s and mean(ISI)=6s; set EV3's shape to custom (3 column format); select the filename as rand_isi_custom_file.dat.
Set 3 contrasts [1 0 0] [0 1 0] and [0 0 1] and an F-test [1 1 1] - that is, select all three t-contrasts to be part of the F-test. Press Done. Press Go and wait for exciting results! On the FEAT report page look at the time series plots of data vs model.
If you have time, have a detailed look at the timeseries plots in the Post-stats section of the webpage report. For example, click on the zstat1 timeseries plot, to see more information about the fitting related to contrast 1. Find the Peristimulus plot for EV1; this shows the data points for all repeats of the condition described in EV1, collapsed down to one "average" version of that stimulus. In this case this means all repeats of the basic block-design on-off shape. Now go back and click on the zstat2 timeseries plot, to see more information about the fitting related to contrast 2. Find the Peristimulus plot for EV3; in this case the plot is showing all repeats of the brief event.
Now start fsleyes, load in the filtered_func_data image in the FEAT output directory, and select View -> Layouts -> FEAT mode. You should be able to see the data timeseries, and the model fit for the current voxel. If you now open the time series control panel (Settings -> Time series 2 -> Time series control), expand the Time series settings for artdata: filtered_func_data section, and check the Plot COPE1 fit box, you will see the data, full model fit, and the COPE1 fit, for the current voxel.

HRF Basis Functions

This section shows you how basis functions can be setup and used in FEAT. The dataset we will use is a jittered single-event experiment with 200 time points. The stimulus is heat applied for 3 seconds with an average inter-stimulus interval of 70 secs. We will only analyze one slice to allow for quick processing.

cd ~/fsl_course_data/fmri_extras/flobs
Feat &

Press Select 4D data and select filtdata.nii.gz. This dataset has already been preprocessed, so change Full analysis to Statistics.
Set the High pass filter cutoff to 50sec.
Setup one EV using the Custom (3 column format) file pain_paradigm.txt. Note that this is the underlying experimental stimulus and is assumed to also correspond to the neuronal response to the stimulus. This needs to be convolved with our assumed HRF.

To start with we will analyze the dataset assuming a fixed Gamma HRF (no basis functions) and then compare the results with a set of the optimal linear basis functions.

Hence, for the EV leave the Convolution as Gamma, BUT turn OFF Add temporal derivative as we want to show the results for when a simple, fixed HRF is assumed.
Take a look at the contrasts - the default OC1 contrast of [1] is fine. You need to also make an F-contrast consisting only of ev1 so that we can compare it to what we will do later.
Press Done and view the design.
Take a look at the Post-stats tab - the defaults are fine.
Press Go and wait for the results. On the FEAT report page look at the time series plots of data vs model.

We will now process the same data using FMRIB's Linear Optimal Basis Set (FLOBS) and compare the results. Kill and restart Feat, and then follow the instructions below.

Press Load and select the design.fsf file in the filtdata.feat created from the fixed Gamma HRF analysis we have just performed. This loads up the design we just used - saving us the effort of having to setup many of the same options again.
The only bit we need to change is in Full model setup in the Stats tab. We need to change the Convolution option to Optimal/custom basis functions.
We can now specify the FMRIB's Linear Optimal Basis Set (FLOBS) filename we wish to use. By default the FLOBS provided in $FSLDIR/etc/default_flobs.flobs is used - so leave that selected. You can generate your own customised FLOBS by selecting Utils > Make_flobs on the main FEAT setup GUI (if you have time later then come back and try generating your own basis functions with this GUI and feed that into FEAT).
Now go to the Contrasts and F-tests tab. Change the number of F-tests back to zero - when using basis functions, an F-test will be automatically set up for you, so you do not need to manually add one.
Now, we have two ways of setting up contrasts with basis functions. This is via consideration of the Original EVs or the Real EVs.
The default is to work with the Original EVs. Original EVs correspond to the true underlying effects/conditions in the experiment. Hence, to investigate if the effect is significant, a simple OC1 contrast [1] suffices.
Now press View Design and the actual design matrix to be used is displayed. This shows one EV for each of the basis functions convolved with the experimental stimulus. The single Original EV is automatically expanded into the Real EVs (in this case 3 EVs) that need to be actually used in the design matrix when modelling with basis functions. Note that the OC1 [1] contrast will be actually carried out by the F1 f contrast. Why is this the correct way to do the test?
Return to Contrasts and F-tests and change Original EVs to Real EVs. The single Original EV is again automatically expanded into the Real EVs actually used in the design matrix, but now you have control of the actual contrast settings used.
Again press View Design and the design should be exactly the same as it was before.
Press Done and then press Go and wait for the results. Take a look at the FEAT report. In particular, look at the peristimulus (PST) plots by clicking on the model fit plots. The peristimulus plots show the fits to a single epoch of stimulation. The data actually acquired at each repeat of the stimulation is plotted as a scatter of points. The full model fit on the peristimulus plot indicates the HRF formed by fitting the basis set to the data.
Compare this FEAT report with the FEAT report from the single HRF Gamma run previously. Are there any differences?
If you get time at the end of this session try running a range of different basis sets, e.g. Sinusoidal, FIR. In particular, have a go at making your own customised basis set (FLOBS) using Utils > Make_flobs on the main FEAT setup GUI.

The End.

FEAT Extras Practical

Contents:

Custom Waveforms

HRF Basis Functions