run_anatomical_dataset

Anatomical dataset basics
Show the dataset contents using cosmo_disp. Where can you see the
There are a lot of zero values in the dataset. Find all non-zero values
Using cosmo_plot_slices, show a saggital view of the dataset
ds.fa.i contains, for each feature (voxel), an index for the
Advanced exercise: set all voxels around a center voxel at

Anatomical dataset basics

In this example, load a brain as a CoSMoMVPA datset and visualize it in matlab

For CoSMoMVPA's copyright information and license terms, #
see the COPYING file distributed with CoSMoMVPA. #

% Set the path.
config=cosmo_config();
data_path=fullfile(config.tutorial_data_path,'ak6','s01');

% Set filename
fn=fullfile(data_path, 'brain.nii');

% Load with cosmo_fmri_dataset and assign to a variable 'ds'
% >@@>
ds=cosmo_fmri_dataset(fn);
% <@@<

Show the dataset contents using cosmo_disp. Where can you see the

number of voxels in the three spatial dimensions? >@@>

cosmo_disp(ds)

% the number of voxels in the spatial dimensions are present in
% ds.a.vol.dim, and are also indicated by the sizes of ds.a.fdim.values
% <@@<

% Show a histogram of all values in the dataset. For bonus points add axis
% labels.
% Hint: all data is present in ds.samples
% >@@>
hist(ds.samples,100)
xlabel('intensity');
ylabel('count');
% <@@<

.a                                                                     
  .vol                                                                 
    .mat                                                               
      [      0         0         1     -84.9                           
        -0.938         0         0       124                           
             0     0.938         0     -65.8                           
             0         0         0         1 ]                         
    .xform                                                             
      'scanner_anat'                                                   
    .dim                                                               
      [ 256       256       160 ]                                      
  .fdim                                                                
    .labels                                                            
      { 'i'                                                            
        'j'                                                            
        'k' }                                                          
    .values                                                            
      { [ 1         2         3  ...  254       255       256 ]@1x256  
        [ 1         2         3  ...  254       255       256 ]@1x256  
        [ 1         2         3  ...  158       159       160 ]@1x160 }
.sa                                                                    
  struct (empty)                                                       
.samples                                                               
  [ 0         0         0  ...  0         0         0 ]@1x10485760     
.fa                                                                    
  .i                                                                   
    [ 1         2         3  ...  254       255       256 ]@1x10485760 
  .j                                                                   
    [ 1         1         1  ...  256       256       256 ]@1x10485760 
  .k                                                                   
    [ 1         1         1  ...  160       160       160 ]@1x10485760

There are a lot of zero values in the dataset. Find all non-zero values

in ds.samples, and plot a histogram of those. What do the two bumps represent? >@@>

nonzero_mask=ds.samples~=0;
nonzero_samples=ds.samples(ds.samples~=0);
hist(nonzero_samples,100);
xlabel('intensity');
ylabel('count');
% <@@<

Using cosmo_plot_slices, show a saggital view of the dataset

>@@>

cosmo_plot_slices(ds);
% <@@<

% Make a copy of the dataset
ds_copy=ds;

ds.fa.i contains, for each feature (voxel), an index for the

anterior-posterior position of that voxel. Set all voxels that have a value of less than 100 for ds.fa.i to zero, and plot the results >@@>

ds_copy.samples(ds.fa.i<100)=0;
cosmo_plot_slices(ds_copy)
% <@@<

% Store the results in a nifti file for visualization using cosmo_map2fmri
output_path=config.output_data_path;
fn_out=fullfile(output_path,'anatomical_dataset_posterior.nii');
% >@@>
cosmo_map2fmri(ds_copy,fn_out);
% <@@<

Advanced exercise: set all voxels around a center voxel at

i=150,j=100,k=50 within a 40-voxel radius to zero, and display the result

center_ijk=[150; 100; 50];
radius=40;
% >@@>
ds_copy2=ds;
all_ijk=[ds_copy2.fa.i; ...
         ds_copy2.fa.j; ...
         ds_copy2.fa.k];

% compute difference dimension-wise
delta=bsxfun(@minus,center_ijk,all_ijk);

% Pythogoras
squared_distance_from_center=sum(delta.^2,1);

% define mask
mask=squared_distance_from_center<=radius^2;

% set voxels to zero
ds_copy2.samples(:,mask)=0;

% show result
cosmo_plot_slices(ds_copy2);
% <@@<