3D Sensor SPMs

3D topography x time sensor SPMs can help you identify the latency and location of effects in sensor space. For example, here's a word-pseudoword effect (N=19) on magnetometers and gradiometers (RMS):

attachment:WdNw3DSensorSpm.jpg

Creating 3D Sensor SPMs

1. Select an averaged file (m*.mat), split it into separate files for magnetometers, gradiometers, and gradiometer-magnitude (m*-mags.mat, m*-grds.mat, and m*-grms.mat, respectively).

clear S
S.D='mae_mydata.mat';
spm_eeg_splitFIF_grms(S);

2. Write image volumes.

% Select options (see help for spm_eeg_convertmat2ana3D):
clear S
S.interpolate_bad = 0;
S.n = 32;
S.pizsize = 3;
% Select trial types:
S.trialtypes = [1 2];
% Mags:
S.Fname = 'mae_mydata-mags.mat';
spm_eeg_convertmat2ana3D(S);
% Grad magnitude:
S.Fname = 'mae_mydata-grms.mat';
spm_eeg_convertmat2ana3D(S);

3. Then compute an ANOVA over subjects (use GUI to batch, or a script if you have one).

Viewing 3D Sensor SPMs

To view the results, navigate to the directory that contains the SPM.mat for your ANOVA. In an SPM window, click 'Results' (1), select SPM.mat (2), select contrast Unwhitened EOI (3), and select options: (4) don't mask, whatever title, choose your p-value and extent thresholds (I usually start with .001 and 0). Click 'whole brain' (5) to view whole-volume results. The coordinates on the far right (6) show x (mm), y (mm), and time (ms). Ignore the glass brain images (7).

To view the cloud-in-a-box images, click 'overlays'->'sections', (8) and select mask.img (9). Lovely (10). If you want to write out the image volume, click 'save' and give it a name. You can then view it using 'Display' or 'Check Reg'.

If you want to correct for extent, rather than clicking 'Results', click 'Toolbox'->'ns'. As above, select SPM.mat, Unwhitened EOI, options: don't mask, whatever title, no p-value adjustment, threshold {p} .001 (or your choice), extent threshold 0. The leftmost column ('cluster-level p-corrected') shows p-values corrected for cluster extent (11). Note that this information was absent when you clicked 'Results'.

If you want to view or write out an image thresholded for cluster extent, determine what cluster size ('cluster-level k-voxel') needed to achieve your desired p-value ('cluster-level p-corrected'; in the example, 214 gets p=0.052 (12)). Then follow the above 'Toolbox'->'ns' directions, but this time enter the k-voxel extent threshold when prompted (eg., 215 (13)). Now the results are thresholded for both height and extent (14). View and save as above.