MATLAB script for the first approach

   clear all
   format long g
   %set directory for the two subjects to be compared
   if ispc
   subjADir='Y:\New_Localizers\dr021108-1p5mm-3mm';
   subjBDir='Y:\New_Localizers\kw011708-1p5mm-3mm';
   else
   %     subjADir='/biac2/kgs/projects/New_Localizers/dr021108-1p5mm-3mm';
   % %     subjBDir='/biac2/kgs/projects/New_Localizers/kw011708-1p5mm-3mm';
   %     subjBDir='/biac2/kgs/projects/New_Localizers/kgs020408-1p5mm-3mm';
   %     
  home = '/biac2/kgs/projects/SameDifferent/'
   %    subjADir='/biac2/kgs/projects/Prosopagnosia/fMRIData/112909whFMRI';    
   %    subjBDir='/biac2/kgs/projects/Prosopagnosia/fMRIData/52309AF';    
   % subjADir = '/biac2/kgs/projects/Kids/fmri/localizer/adult_al_22yo_051108';
   % subjBDir ='/biac2/kgs/projects/Kids/fmri/localizer/adult_jc_27yo_052408';
   subjADir ='/biac2/kgs/projects/Kids/fmri/localizer/adult_kw_25yo_090308';

   subjBDir = '/biac2/kgs/projects/Kids/fmri/localizer/adult_dy_25yo_041908';

   end

   % %choose an ROI in subject A
   % roiA='lh_LOfaces_event_loc';
   % roiA='lh_PPA_event';

   % roiA='lPPA';
   % roiA='rSTSfaces';

   % if you want, get all the ROIs from subject A
   % cd(subjADir);
   % cd Inplane/ROIs

   %finds all the file names and chops off the .mat

   % rois=dir('*.mat');
   % for i=1:size(rois,1)
   % roisA(i) = cellstr(strrep(rois(i).name,'.mat',));
   % end 

   % golijeh kid data rois
   roisA={'lFFA_MBvAC_p3','rFFA_MBvAC_p3','lLO_ACvT_p3','rLO_ACvT_p3','lMT_p4_al', 'rMT_p4_al',...
   'lPPA_IOvAC_p3','rPPA_IOvAC_p3'};

   % ,'lSTS_MBvAC_p3', 'rSTS_MBvAC_p3'
   %load ROIs from subject B
   % roiB={'lFFA','lPPA','rPPA','pSTSfaces'};

   % get all the ROIs from subject B
   % cd(subjBDir);
   % cd Inplane/ROIs

   %finds all the file names and chops off the .mat

   % rois=dir('*.mat');
   % for i=1:size(rois,1)
   % roisB(i) = cellstr(strrep(rois(i).name,'.mat',));
   % end

   % golijeh kid data rois
   roisB={'lFFA_MBvAC_p3','rFFA_MBvAC_p3','lLO_ACvT_p3','rLO_ACvT_p3','lMT_p4_al','rMT_p4',...
   'lPPA_IOvAC_p3','rPPA_IOvAC_p3'};
   % ,'lpSTS_MBvACIO_p3', 'rSTS_MBvAC_p3'
   %want to get the motion corrected data type which is usually 3
   dt = 3; %'MotionComp_RefScan5';
   %then pick the scans from the motion corrected data that were (in this
   %case) taken from the event related adaptation experiment for
   %dr021108-1p5mm-3mm this is scans 1:8
   % for wald scan went
   %   mtloc,eccbias1,eccbias2,4xretinotopy,objloc1,objloc2
   % so to get our scans in order we have
   % scan = [8 9 1 2 3];

   % golijeh kid scans
   scan = [1 2 3 4];
   %find the betas over time for all the ROIs loaded. each voxel represented
   %as a vector with the B for each category/stimulus

   %go to path

   % .. for subject A brain'
   %go to subjects data directory
   cd(subjADir); 
   %initialize our hidden inplane set to the right data type,scan, and roi
   hiA = initHiddenInplane(dt, scan, roisA);
   %then we grab the multivoxel data from the ROI
   %this should give us mvA which is a struct with lots of useful stuff
   %in particular
   %mvA.trials has information about conditions and order of stimuli etc
   %mvA.coord has the coordinates for each voxel in the roi should be 3 x
   %numVoxels
   %mvA.tSeries has the time series for each voxel in % signal change
   %mvA.roi had roi info

   for i = 1:size(roisA,2)
       mvA{i} = mv_init(hiA, roisA{i}, scan, dt);
   %         mvA{i} = mv_applyGlm(mvA{i});
   end

   %then we apply our glm to each of the voxels in the roi
   %we will probably want to set these parameters ourselves so that we know
   %what happened
   %this gives us
   %mvA.glm has the betas, the design matrix and so on
   %for a block design there will be as many betas as there are conditions
   %for an event related design there will be timepoints x conditions x voxels
   %betas

   % scan=[1 2 3 4 5];
   % scan=[1 2];

   % kevin scans
   scan=[1 2 3 4];

   % .. for all ROIs in subj B
   %instead invoke hidden inplane with multiple rois.  then loop through the
   %rois when comparing to subjectA
   %go to subject b directory
   cd(subjBDir);
   dt=3;
   %load data with all rois
   hiB = initHiddenInplane(dt,scan,roisB);

   for i = 1:size(roisB,2);
       mvB{i} = mv_init(hiB, roisB{i}, scan, dt);
   %     mvB{i} = mv_applyGlm(mvB{i});
   end

   %find the correlation coefficient between a voxel in subject A and each
   %voxel in each ROI in subj B

   %get the number of voxels by getting the size and then the number of
   %columns

   %trim the size of the tSeries down (if necessary)
   % for i = 1:nROIsInB
       %     mvB{i}.tSeries(1249:1296,:) = [];
   % end
   
   %     as a figure, lets just make one plot comparing all the ROIS
   % size should be numROIs subjA x numROIs in subjB
   % each entry is the % of voxels in subjA ROI that were most highly
   % correlated with the ROI in subjB
   % make matrix

   ROImix = zeros(size(mvA,2),size(mvB,2));

   % for each ROI in subject A
   for m = 1:size(mvA,2)
   %     for each voxel in subject A
       for i = 1:size(mvA{m}.tSeries, 2) %number of voxels in ROI in A
   %         vector for the correlations
       corrs=[];
   %       for each ROI in subject B
             for j = 1:size(mvB,2) %number of ROI
   %           for each voxel in subject B
                 for k = 1:size(mvB{j}.tSeries, 2); %number of voxels in the ROI in B
                    %get correlation between the time series of the two voxels
                     r=corrcoef(mvA{m}.tSeries(:,i), mvB{j}.tSeries(:,k)); %records the correlations in a
   %               2D matrix with each ROI's set of correlations in a row
   % so corrs is j rois by k voxels, where k is the largest number of voxels
   % in any of the rois
                     corrs(j,k) = r(2);
             
             
             %for glm betas correlation
   %               r=corrcoef(mvA{m}.glm.betas(:,i),mvB{j}.glm.betas(:,k));
   %               corrs(j,k) = r;

             end
         end   
   %       then for each voxel in ROIA find the maximum correlation in Brain b
         maxCorr(i) = max(max(corrs)); % get the max correlation from all ROIs
   %       find the name of the ROI
         [row,col] = find(corrs == max(max(corrs))); % finds the row that the correlation was in and then records the name from that ROI
   %     if there is more than one ROI with the same max correlation pick the
   %     second....  probably doesn't happen much.
         if length(row) > 1
         row = row(2);
         end
     
         maxCorrLocnum(i)=row;
   %       get the name of the roi
         maxCorrLoc(i) = cellstr(mvB{row}.roi.name); %record from where that coef came from

   end

   % make a histogram showing how many times each roi in subject b was chosen
   % as a match
   % get names of chosen rois
    a=unique(maxCorrLoc);
   %  get count of each from maxCorrLoc
   roipicks=[];
   for i = 1:length(a)
    roipicks=[roipicks,size(find(strcmp(cellstr(maxCorrLoc),a(i))),2)];
   end

   % bar plot
   %     figure(gcf+1);
   %     bar(roipicks);
   %     set(gca,'XTickLabel',a);
   %     Title(mvA{m}.roi.name);

   %get a sense for the kind of correlations we are getting
   %     figure(gcf+1); hist(maxCorr);
   
   
   %     now for this ROI in A we want to get the %of times each ROI in B was
   %     picked
   %     for each ROI in subjB, find the number of times it was picked
   %     normalized by number of voxels in ROI from subjA
       for z = 1:size(mvB,2)
           ROImix(m,z) = size(find(maxCorrLocnum==z),2);%/size(mvA{m}.tSeries, 2);
       end
   
   end

   % normalize ROImix
   denom=sum(ROImix,2);
   rdenom = repmat(denom,1,size(ROImix,2));
   nROImix = ROImix./rdenom;
   % plot figure
   figure(gcf+1); imagesc(nROImix);
   set(gca,'YTick',1:size(roisA,2));
   set(gca,'XTick',1:size(roisB,2));
   set(gca,'YTickLabel',roisA);
   set(gca,'XTickLabel',roisB);
   %set title and axis labels
   colorbar;