fmri_regress_average

 generates an averaged fmri regressor from a given set of responses
 
   [names,vals] = fmri_regress_average(pinfo,minfo,sess)
 
 called by fmri_generate_regress
 
 note: expects the same number of responses for all variables in a given
 set
 
 REQUIRES
   minfo.stim_average.var_sets - cell array of cell array of strings
       containing cues to be averaged together
   minfo.stim_average.set_names - cell array of strings that are names for
       each of the sets defined above
 
 RETURNS
   names = cell array of six regressor names
   vals = volume X regressor matrix containing motion regressors
 
 FB 2010.02.25

0001 function [names,vals] = fmri_regress_average(pinfo,minfo,sess)
0002 
0003 % generates an averaged fmri regressor from a given set of responses
0004 %
0005 %   [names,vals] = fmri_regress_average(pinfo,minfo,sess)
0006 %
0007 % called by fmri_generate_regress
0008 %
0009 % note: expects the same number of responses for all variables in a given
0010 % set
0011 %
0012 % REQUIRES
0013 %   minfo.stim_average.var_sets - cell array of cell array of strings
0014 %       containing cues to be averaged together
0015 %   minfo.stim_average.set_names - cell array of strings that are names for
0016 %       each of the sets defined above
0017 %
0018 % RETURNS
0019 %   names = cell array of six regressor names
0020 %   vals = volume X regressor matrix containing motion regressors
0021 %
0022 % FB 2010.02.25
0023 
0024 % init output vars
0025 names = minfo.stim_average.set_names;
0026 nsets = length(names);
0027 vals = zeros(pinfo.scanner.TR,nsets);
0028 stim_avg_vars = minfo.stim_average.var_sets;
0029 nsav = length(stim_avg_vars);
0030 if nsets ~= nsav, error('different number of sets and names'); end
0031 
0032 % init vars
0033 ccm = parse_fh(minfo.cond_cue_map); % function handle for condition to cue mappings
0034 
0035 % Now get the vector of stimulus onset times to which these response
0036 % parameters refer
0037 sfilt.include.all = minfo.response_filter;
0038 sinfo = ensemble_filter(pinfo,sfilt);
0039 pc = set_var_col_const(pinfo.vars);
0040 onsets = sinfo.data{pc.RUN_REL_TIME}/1000;
0041 
0042 % Durations
0043 if isfield(minfo,'music_dur')
0044   % Durations are constant
0045   durs = ones(size(onsets))*minfo.music_dur;
0046 elseif isfield(minfo,'music_dur_db') && minfo.music_dur_db
0047   sids = sinfo.data{pc.EVENT_CODE};
0048   durs = fmri_stim_duration(pinfo,minfo,sids);
0049 end
0050 
0051 % iterate over stim avg sets, get average resp_params, calculate vals
0052 vals = zeros(pinfo.scanner.actual_nvol,nsav);
0053 for j=1:nsav
0054   vset = stim_avg_vars{j};
0055   resp_params = [];
0056   for k=1:length(vset)
0057     cue = ccm(vset{k}); % get cue for this regressor
0058     lrp = extract_resp_params_v2(cue,pinfo,minfo,sess);
0059     if isempty(lrp), continue; end
0060     resp_params = [resp_params lrp];
0061   end % for k=1:length(vset
0062   avg_rp = mean(resp_params,2);
0063   vals(:,j) = fmri_convolve_regress(onsets,durs,avg_rp,pinfo.scanner.TR,...
0064       pinfo.scanner.dt,pinfo.scanner.actual_nvol);
0065 end % for j=1:length(stim_avg_vars