Home > fmri > utils > display_template.m

display_template

PURPOSE ^

batch for slice_overlay.m

SYNOPSIS ^

This is a script file.

DESCRIPTION ^

 batch for slice_overlay.m

CROSS-REFERENCE INFORMATION ^

This function calls: This function is called by:

SOURCE CODE ^

0001 %
0002 % batch for slice_overlay.m
0003 %
0004 
0005 % 10/17/00 Petr Janata  Made compatible with Brett's 9/12/00 revision
0006 
0007 plot_types = {'group_stats', ... 
0008       'indiv_stats', ...
0009       'group_contrast_contour' ...
0010       }; 
0011 
0012 plot_idx = 1;
0013 
0014 things_to_plot = plot_types{plot_idx}
0015 
0016 ADD_MASK_CONTOUR = 1;
0017 DISPLAY_ON_INDIV_BRAIN = 1;
0018 
0019 PRINT_INDIV = 0;
0020 PRINT_TO_FILE = 0;
0021 
0022 clear global SO
0023 global SO
0024 
0025 rootpath = '/data1/ts1/';        % enter the path to where your subject
0026                                         % and group analysis directories lie
0027 spm_path = '/usr/local/matlab/matlab5/toolbox/spm/spm99/';
0028 
0029 % Load up a list of subject ids (this is used to access individual subject directories)
0030 sinfo = get_ts1_sinfo;
0031 subject_ids = cellstr(char(sinfo(:).id))
0032 nsub = length(subject_ids);
0033 proc_sub = 1:nsub;
0034 
0035 df_indiv = 426;                % Degrees of freedom in the individual
0036                                         % subject models
0037 df_group = nsub-1;
0038 
0039 threshold_p = 0.01        % Individual subjects
0040                                 %  2.34 = 0.01 (d.f. = 426)
0041                                 %  3.11 = 0.001
0042                 %  3.75 = 0.0001
0043                 %  4.31 = 0.00001 (d.f. = 426)
0044 
0045                 % Group
0046                 %  2.72 = 0.01 (d.f. = 11)
0047                 %  4.02 = 0.001 (d.f. = 11)
0048                 %  5.45 = 0.0001 (d.f. = 11)
0049                 %  7.10 = 0.00001 (d.f. = 11)
0050 
0051 max_cutoff_indiv = 10;                
0052             
0053 %
0054 % Set up slice information
0055 %
0056 
0057 transform_types = {'sagittal','coronal','axial'};
0058 transform_idx = [1];        % 1=sagittal, 2=coronal, 3=axial
0059 
0060 slice_ranges = [-65 65; -60 -75; -45 75];
0061 
0062 slice_skip = 5;
0063 
0064 %
0065 % Load colormaps that we might want to use.  Note that Matthew Brett's routines
0066 % also support several different default colormaps.
0067 %
0068 
0069 load hot2
0070 load hot3
0071 
0072 newhot1 = brighten(hot,0.5);
0073 newhot2 = brighten(hot2,0.5);
0074 newhot3 = brighten(hot3,0.5);
0075 
0076 switch things_to_plot
0077   case 'group_stats'
0078     nimg = 0;
0079             
0080     threshold = abs(tinv(threshold_p, df_group))
0081     color_range = [threshold 8];
0082     
0083     nimg = nimg+1;
0084     SO.img(nimg).vol = spm_vol(sprintf('%s/group/mean.img', rootpath));
0085     SO.img(nimg).type = 'truecolor';
0086     SO.img(nimg).prop = 1;
0087     SO.img(nimg).cmap = gray;
0088     
0089     SO.cbar = [2 3];
0090     
0091     SO.transform = transform_types{transform_idx};
0092     SO.slices = ...
0093     slice_ranges(transform_idx,1):sign(diff(slice_ranges(transform_idx,:)))*slice_skip:slice_ranges(transform_idx,2);
0094     
0095     nanat = nimg;
0096 
0097     SO.labels.format = '%+d';
0098     SO.figure = spm_figure('GetWin', 'Graphics'); % use SPM figure window
0099     set(SO.figure,'DefaultLineLineWidth',2.0)
0100 
0101     % Set up things for the MUSIC VS REST contrast
0102     curdir = sprintf('%s/group/music-rest', rootpath);
0103     
0104     nimg = nanat+1;
0105     SO.img(nimg).vol = spm_vol(sprintf('%s/%s', curdir, 'spmT_0002.img'));
0106     SO.img(nimg).type = 'split';
0107     SO.img(nimg).range = color_range;
0108     SO.img(nimg).prop = 1;
0109     SO.img(nimg).cmap = newhot1;
0110 
0111     nimg = nimg+1;
0112     SO.img(nimg).vol = spm_vol(sprintf('%s/group/music-rest/%s', rootpath, 'spmT_0003.img'));
0113     SO.img(nimg).type = 'split';
0114     SO.img(nimg).range = color_range;
0115     SO.img(nimg).prop = 1;
0116     SO.img(nimg).cmap = newhot3;
0117 
0118     if ADD_MASK_CONTOUR
0119       nimg = nimg+1;
0120       SO.img(nimg).vol = spm_vol(sprintf('%s/mask.img', curdir));
0121       SO.img(nimg).type = 'contour';
0122       SO.img(nimg).cmap = hot3;
0123       SO.img(nimg).contours = [0.5 0.5];
0124       SO.img(nimg).linespec = 'g';
0125     end
0126 
0127     slice_overlay            % This is the routine that does all the work
0128 
0129     if PRINT_TO_FILE
0130       set(gcf,'PaperPositionMode', 'auto')
0131       print('-dpsc','-painters','-noui', sprintf('%s/group/music_vs_rest.ps',rootpath))
0132     end
0133 
0134   case 'indiv_stats'
0135     for isub = proc_sub
0136       curdir = fullfile(rootpath, subject_ids{isub},'normalized');
0137 
0138       threshold = abs(tinv(threshold_p, df_indiv))
0139 
0140       nimg = 0;
0141       SO.cbar = [];
0142       
0143       % Set things up for the anatomical
0144       
0145       nimg = nimg+1;
0146       if DISPLAY_ON_INDIV_BRAIN
0147     hires_dir = fullfile(rootpath, subject_ids{isub},'hires');
0148     eval(['ls ' hires_dir '/n*.img;']); hires_img = ans(1:end-1);
0149     SO.img(nimg).vol = spm_vol(hires_img);
0150 
0151       else % Display individual data on top of canonical image
0152     SO.img(nimg).vol = spm_vol(fullfile(spm_path,'canonical/avg152T1.img'));
0153       end
0154       SO.img(nimg).type = 'truecolor';
0155       SO.img(nimg).prop = 1;
0156       SO.img(nimg).cmap = gray;
0157       SO.cbar(nimg) = 0;
0158      
0159       nimg = nimg+1;
0160       fname = sprintf('%s/spmT_0009.img', curdir)
0161       SO.img(nimg).vol = spm_vol(fname);
0162       SO.img(nimg).type = 'split';
0163       SO.img(nimg).cmap = hot;
0164       SO.img(nimg).range = [threshold max_cutoff_indiv];
0165       SO.cbar(nimg) = 1;
0166       
0167       SO.cbar = find(SO.cbar);
0168 
0169       SO.transform = transform_types{transform_idx};
0170       SO.slices = ...
0171       slice_ranges(transform_idx,1):sign(diff(slice_ranges(transform_idx,:)))*slice_skip:slice_ranges(transform_idx,2);
0172 
0173       SO.figure = spm_figure('GetWin', 'Graphics'); % use SPM figure window
0174 
0175       slice_overlay
0176 
0177       if PRINT_INDIV
0178     set(gcf,'PaperPositionMode', 'auto')
0179     print('-dpsc','-painters','-noui', sprintf('%s/%s_attend.ps',rootpath,subject_ids{isub}))
0180       end
0181     
0182     end
0183     
0184   case 'group_contrast_contour'
0185     nimg = 0;
0186     threshold = abs(tinv(threshold_p, df_group))
0187     color_range = [threshold 8];
0188     
0189     nimg = nimg+1;
0190     SO.img(nimg).vol = spm_vol(sprintf('%s/group/mean.img', rootpath));
0191     SO.img(nimg).type = 'truecolor';
0192     SO.img(nimg).prop = 1;
0193     SO.img(nimg).cmap = gray;
0194     
0195     SO.transform = transform_types{transform_idx};
0196     SO.slices = ...
0197     slice_ranges(transform_idx,1):sign(diff(slice_ranges(transform_idx,:)))*slice_skip:slice_ranges(transform_idx,2);
0198     
0199     nanat = nimg;
0200 
0201     SO.labels.format = '%+d';
0202     SO.figure = spm_figure('GetWin', 'Graphics'); % use SPM figure window
0203     set(SO.figure,'DefaultLineLineWidth',2.0)
0204     
0205     % Set up things for the CONDITION 1 vs CONDITION 2 overlay
0206     nimg = nanat+1;
0207     SO.img(nimg).vol = spm_vol(sprintf('%s/group/t1-listen/%s', rootpath, 'spmT_0002.img'));
0208     SO.img(nimg).type = 'contour';
0209     SO.img(nimg).contours = [threshold threshold];
0210     SO.img(nimg).prop = 0.4;
0211     SO.img(nimg).cmap = newhot1;
0212     SO.img(nimg).linespec = 'r';
0213     
0214     nimg = nimg+1;
0215     SO.img(nimg).vol = spm_vol(sprintf('%s/group/t2-listen/%s', rootpath, 'spmT_0002.img'));
0216     SO.img(nimg).type = 'contour';
0217     SO.img(nimg).contours = [threshold threshold];
0218     SO.img(nimg).prop = 0.4;
0219     SO.img(nimg).cmap = newhot3;
0220     SO.img(nimg).linespec = 'c';
0221 
0222     if ADD_MASK_CONTOUR
0223       nimg = nimg+1;
0224       SO.img(nimg).vol = spm_vol(sprintf('%s/group/t1-listen/mask.img', rootpath));
0225       SO.img(nimg).type = 'contour';
0226       SO.img(nimg).cmap = hot3;
0227       SO.img(nimg).contours = [0.5 0.5];
0228       SO.img(nimg).linespec = 'g';
0229     end
0230 
0231     slice_overlay        % This is the routine that does all the work
0232     
0233     if PRINT_TO_FILE
0234       set(gcf,'PaperPositionMode', 'auto')
0235       print('-dpsc','-painters','-noui', sprintf('%s/group/t1_vs_t2_contour.ps',rootpath))
0236     end
0237 end

Generated on Sat 24-Aug-2019 04:00:39 by m2html © 2003