Statistics in sensor space

A standardised way or running sensor-level statistics on your MEG data is implemented in SPM5:

http://imaging.mrc-cbu.cam.ac.uk/meg/SensorSpm

But nothing prevents you from finding your own way...

The following two scripts will help you

1. convert fiff-files to SPM images (if necessary)
2. run group statistics on MEG data in signal space in SPM 5

1. Converting Fiff-files to SPM images

This script has not been tested extensively, but it is based on the scripts at http://imaging.mrc-cbu.cam.ac.uk/meg/SensorSPM.

% Modified from http://imaging.mrc-cbu.cam.ac.uk/meg/SensorSPM
% takes a list of Fiff-files (cell array "fifflist{}") and converts MEG data to SPM image files,
% which can then be subjected to 2nd-level statistics, for example
% "rootdir" can contain the directory path common to all files
% the script will produce subdirectories with img-files in the directories of the input fiff-files
% so far only tested on one data set
% OH, March 2009

% you can specify a list of fiff-files here, to be converted into SPM format
fifflist = {'/fullpath/file4subj1.fif', ...
            '/fullpath/file4subj2.fif', ...
            '/fullpath/file4subj3.fif'};

nr_fiffs = length(fifflist);

% root directory common to all input fiff-files (not required if full pathnames specified in fifflist{})
root_dir = '';

% GET GOING...
clear S;
for ff = 1:nr_fiffs,    % for all Fiff-files...
% CONVERT FROM FIFF TO SPM FORMAT...
    S.Fchannels = fullfile(spm('dir'),'EEGtemplates','FIF306_setup.mat');
    S.veogchan  = [62];         % Channel 62 is (bipolar) VEOG, MAY NEED EDITING
    S.heogchan  = [61];         % Channel 61 is (bipolar) HEOG, MAY NEED EDITING
    S.veog_unipolar = 0;        % MAY NEED EDITING
    S.heog_unipolar = 0;        % MAY NEED EDITING
    S.conds = 1;                % conditions in fiff-file, MAY NEED EDITING
    S.grms = 1;    % so that the splitting outputs mags, grads and grad RMS (grms)
    [fiffpath,fiffname,fiffext,fiffversn] = fileparts(fifflist{ff});
    S.Fdata = fullfile(root_dir, fifflist{ff});   % Input Fiff-file before splitting
    fileSPMout = fullfile(root_dir, fiffpath, [fiffname '_SPM.mat']); % Output SPM file (if not specified, will be 'myexp.mat')
    S.Pout  = fileSPMout;   % output for conversion from Fiff to SPM format (before splitting)
    D0 = spm_eeg_rdata_FIF(S);  % convert fiff- to SPM-format

% SPLIT DATA INTO MAGS/GRADS/EEG...
    S.D = fileSPMout;       % structure for splitting
    D1 = spm_eeg_splitFIF(S);   % split SPM files

% WRITE TO IMAGE VOLUMES:
    % Select options (see help for spm_eeg_convertmat2ana3D):
    clear S
    S.interpolate_bad = 0;
    S.n = 32;
    S.pixsize = 3;
    % Select trial types:
    S.trialtypes = [1];
    img_outnames = '';
    % Mags:
    S.Fname = fullfile(root_dir, fiffpath, [fiffname '_SPM-mags.mat']);
    tmpname = spm_eeg_convertmat2ana3D(S);
    img_outnames(1,:) = fullfile(root_dir, fiffpath, [fiffname '_SPM-mags'], 'trialtype1', 'average.img');
    % Grad magnitude:
    S.Fname = fullfile(root_dir, fiffpath, [fiffname '_SPM-grds.mat']);
    tmpname = spm_eeg_convertmat2ana3D(S);
    img_outnames(2,:) = fullfile(root_dir, fiffpath, [fiffname '_SPM-grds'], 'trialtype1', 'average.img');
    % GradRMS magnitude:
    S.Fname = fullfile(root_dir, fiffpath, [fiffname '_SPM-grms.mat']);
    tmpname = spm_eeg_convertmat2ana3D(S);
    img_outnames(3,:) = fullfile(root_dir, fiffpath, [fiffname '_SPM-grms'], 'trialtype1', 'average.img');

% SMOOTH IMAGES
    P = img_outnames;
    SmoothKernel = [5 5 10]; % % Smoothness in x (mm), y (mm) and z (ms), MAY NEED EDITING
    for n=1:size(P,1);
               [pth,nam,ext] = fileparts(P(n,:));
               Pout{n}       = fullfile(pth,['s' nam ext]);
               spm_smooth(spm_vol(P(n,:)),Pout{n},SmoothKernel);
               Pin = strvcat(P(n,:),Pout{n});
               spm_imcalc_ui(Pin,Pout{n},'((i1+eps).*i2)./(i1+eps)',{[],[],'float32',0});
    end
    %The final imcalc step above is just to reinsert NaN's for voxels outside space-time volume into which data were smoothed
end;    %..ff

2. Sensor-level group analysis in SPM 5

% based on http://imaging.mrc-cbu.cam.ac.uk/meg/SensorSpm
% to be run in SPM 5 EEG
% runs group statistics on files in "imgfiles"
% output written into directory "outdir"
% OH, March 2009

addpath /imaging/local/meg_misc;    % for meg_batch_anova

% output directory
outdir = '/youroutputwillgohere/;

% Here: One group of subjects, 2 conditions
% You can also specify more groups of subjects, or test one condition against zero:
% More groups of subjects: Vary first cell index of imgfiles (imgfiles{1}{cc}...imgfiles{2}{cc}...)
% One-sample t-test against zero mean: just specify one file per line,
% e.g. imgfiles{1}{cc} = ['/thispath/subj1_con1.img']; cc=cc+1; etc.
if exist('imgfiles')~=1,
    cc = 1;
    imgfiles{1}{cc} = ['/thispath/subj1_con1.img'; 'thispath/subj1_con2.img']; cc=cc+1;
    imgfiles{1}{cc} = ['/thispath/subj2_con1.img'; 'thispath/subj2_con2.img']; cc=cc+1;
    imgfiles{1}{cc} = ['/thispath/subj3_con1.img'; 'thispath/subj3_con2.img'];
end;
nr_subjects = cc;

% root directory common to all input fiff-files (not required if full pathnames specified in imgfiles{})
root_dir = '';

% Attach root directory
clear Panova;
for i=1:nr_subjects, % create full image file names, including root directory etc.
    [m,n] = size(imgfiles{1}{i});
    for j=1:m,
        Panova{1}{i}(j,:) = fullfile( root_dir, imgfiles{1}{i}(j,:) );
    end;    %..j
end;    %..i

meg_batch_anova(Panova,imgset(ss).outdir);  % Run SPM stats