run anatomical dataset basicsΒΆ

Matlab output: run_anatomical_dataset_basics

%% 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');

%% 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);