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fMRI analysis

Only the main characteristics of the fMRI analysis are described below; for a more detailed demonstration of fMRI analysis, read previous tutorial chapters describing fMRI analyses.

Toggle the modality from EEG to fMRI, and change directory to the fMRI subdirectory (either in MATLAB, or via the “CD” option in the SPM “Utils” menu)

Preprocessing the fMRI data

Press the Batch button and then:

  • Select Spatial: Realign: Estimate & Reslice from the SPM menu, create two sessions, and select the 390 fM*.img EPI images within the corresponding Session1 / Session 2 subdirectories (you can use the filter ^fM.*). In the “Resliced images” option, choose “Mean Image Only”.

  • Add a Spatial: Coreg: Estimate module, and select the smri.img image in the sMRI directory for the “Reference Image” and select a “Dependency” for the “Source Image”, which is the Mean image produced by the previous Realign module. For the “Other Images”, select a “Dependency” which are the realigned images (two sessions) from Realign.

  • Add a Spatial: Segment module, and select the smri.img image as the “Data”.

  • Add a Spatial: Normalise: Write module, make a “New: Subject”, and for the “Parameter File”, select a “Dependency” of the “Deformation Field Subj-\(>\)MNI” (from the prior segmentation module). For the “Images to Write”, select a “Dependency” of the “Coreg: Estimate: Coregistered Images” (which will be all the coregistered EPI images) and “Segment: Bias Corr Images” (which will be the bias-corrected structural image). Also, change the “Voxel sizes” to [3 3 3], to save diskspace.

  • Add a Spatial: Smooth module, and for “Images to Smooth”, select a “Dependency” of “Normalise: Write: Normalised Images (Subj 1)”.

  • Save the batch file (e.g, as batch_fmri_preproc.mat, and then press the “Run” button.

These steps will take a while, and SPM will output some postscript files with the movement parameters and the coregistration results (see earlier chapters for further explanation). The result will be a series of 2 sets of 390 swf*.img files that will be the data for the following 1st-level fMRI timeseries analysis.

Statistical analysis of fMRI data

First make a new directory for the stats output, e.g, a Stats subdirectory within the fMRI directory.

Press the batch button and then:

  • Select “Stats: fMRI model specification” from the SPM module menu, and select the new Stats subdirectory as the “Directory”.

  • Select “Scans” for “Units of design”.

  • Enter 2 for the “Interscan interval” (i.e, a 2s TR).

  • Create a new session from the “Data & Design” menu. For “Scans”, select all the swf*.img files from the Session1 subdirectory (except the mean). Under “Multiple Conditions”, click “Select File”, and select the trials_ses1.mat file that is provided with these data. (This file just contains the onsets, durations and names of every event within each session.). For “Multiple regressors”, click “Select File”, and select the rp*.txt file that is also in the Session1 subdirectory (created during realignment).

  • Repeat the above steps for the second session.

  • Under “Basis Functions”, “Canonical HRF” add the “Time and Dispersion” derivatives.

  • Then add a “Stats: Model estimation” module, and for the “Select SPM.mat”, choose the “Dependency” of the SPM.mat file from the previous “fMRI model specification” module.

  • Add a “Stats: Contrast Manager” module, and for the “Select SPM.mat”, choose the “Dependency” of the SPM.mat file from the previous “Model Estimation module”.

  • Under “Contrast Sessions”, create a new F-contrast with a “Name” like faces vs scrambled (all BFs) and then enter [eye(3) -eye(3) zeros(3,6)]. This will produce a 3x12 matrix that picks out the three basis functions per condition (each as a separate row), summing across the two conditions (with zeros for the movement parameter regressors, which are of no interest). Then select “Replicate (average over sessions)”.

  • Under “Contrast Sessions”, create a new F-contrast with a “Name” like faces + scrambled vs Baseline (all BFs) and then enter the [eye(3) eye(3) zeros(3,6)]. Again, select “Replicate (average over sessions)”.

  • Save the batch file (e.g, as batch_fmri_stats.mat, and then press the “Run” button.

When this has finished, press Results and select the SPM.mat file that should have been created in the new Stats directory. The Contrast Manager window will appear, and you can select the “faces vs scrambled (all BFs)” contrast. Do not mask, keep the title, threshold at \(p<.05\) FWE corrected, use an extent threshold of 60 voxels, and you should get the MIP and table of values (once you have pressed “whole brain”) like that in Figure  1. This shows clusters in bilateral midfusiform (FFA), right occipital (OFA), right superior temporal gyrus/sulcus (STS), in addition to posterior cingulate and anterior medial prefrontal cortex. These clusters show a reliable difference in the evoked BOLD response to faces compared with scrambled faces that can be captured within the “signal space” spanned by the canonical HRF and its temporal and dispersion derivatives. Note that this F-contrast can include regions that show both increased and decreased amplitude of the fitted HRF for faces relative to scrambled faces (though if you plot the “faces vs scrambled” contrast estimates, you will see that the leftmost bar (canonical HRF) is positive for all the clusters, suggesting greater neural activity for faces than scrambled faces (also apparent if you plot the event-related responses)).

There is some agreement between these fMRI effects and the localisation of the differential ERP for faces vs scrambled faces in the EEG data (see earlier section). Note however that the fMRI BOLD response reflects the integrated neural activity over several seconds, so some of the BOLD differences could arise from neural differences outside the 0-600ms epoch localised in the EEG data (there are of course other reasons too for differences in the two localisations; see, eg, Henson et al, under revision).

SPM{F} for faces vs scrambled faces.

You can also press Results and select the “faces + scrambled vs Baseline (all BFs)” contrast. Using the same threshold of \(p<.05\) FWE corrected, you should see a large swathe of activity over most of the occipital, parietal and motor cortices, reflecting the general visuomotor requirements of the task (relative to interstimulus fixation). The more posterior ventral occipital/temporal BOLD responses are consistent with the MEG localisation of faces (or scrambled faces) versus baseline, though note that the more anterior ventral temporal activity in the MEG localisation is not present in the fMRI data, which suggests (but does not imply) that the MEG localisation may be erroneous.

These contrasts of fMRI data can now be used as spatial priors to aid the localisation of EEG and/or MEG data, as in the next section.