run spherical neighborhood searchlightΒΆ

Matlab output: run_spherical_neighborhood_searchlight

%% Spherical searchlight
% this example implements a spherical searchlight using
% cosmo_spherical_neighborhood and performs crossvalidation
% with a nearest neigh classifier
%
% Note: for running searchlights it is recommended to use
%       cosmo_searchlight and cosmo_spherical_neighborhood
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #


%% Set up parameters
config=cosmo_config();
data_path=fullfile(config.tutorial_data_path,'ak6','s01');
output_path=fullfile(config.output_data_path);

fn=fullfile(data_path,'glm_T_stats_perrun.nii');
maskfn=fullfile(data_path,'brain_mask.nii');
radius=3;
targets=repmat(1:6,1,10);
chunks=floor(((1:60)-1)/6)+1;
classifier=@cosmo_classify_nn;
classifier_opt=struct();

%% load data and set sample attributes
ds=cosmo_fmri_dataset(fn, 'mask', maskfn, ...
                          'targets', targets, ...
                          'chunks', chunks);

fprintf('Input dataset:\n');
cosmo_disp(ds);

%% define centers of searchlight
nfeatures=size(ds.samples,2);
center_ids=1:nfeatures;

%% use voxel selection function
nbrhood=cosmo_spherical_neighborhood(ds, 'radius',radius);
center2neighbors=nbrhood.neighbors;
ncenters=numel(center2neighbors); % should be equal to 'nfeatures'

%% set up cross validation
% (here we use cosmo_oddeven_partitioner; cosmo_nfold_partitioner would be
% another possiblity, with the advantage of using a larger training set,
% but the disadvantage that it takes longer to run)
partitions=cosmo_oddeven_partitioner(ds.sa.chunks);

%% Allocate space for output
ncenters=numel(center_ids);
accs=zeros(1,ncenters);

%% Run the searchlight
% go over all features: in each iteration, slice the dataset to get the
% desired features using center2neighbors, then use cosmo_crossvalidate
% to get classification accuracies (it's its second output argument),
% and store the classiifcation accuracies.
%
% Note: it is generally easier (and faster) to use cosmo_searchlight
%       and cosmo_crossvalidation_measure; this example here is for
%       illustrative purposes mainly

% use cosmo_show_progress to show a pretty progress bar
prev_msg='';
clock_start=clock();
show_progress_every=1000;
for k=1:ncenters
    center_id=center_ids(k);
    sphere_feature_ids=center2neighbors{center_id};

    sphere_ds=cosmo_slice(ds, sphere_feature_ids, 2);

    % run cross validation
    [pred_cv,acc]=cosmo_crossvalidate(sphere_ds, classifier, ...
                                        partitions, classifier_opt);

    % for now, just store the accuracy (not the predictions)
    accs(center_id)=acc;


    % show progress every 1000 steps, and at the beginning and end.
    if k==1 || mod(k,show_progress_every)==0 || k==nfeatures
        mean_so_far=mean(accs(1:k));
        msg=sprintf('accuracy %.3f (%d features visited)', mean_so_far, k);
        prev_msg=cosmo_show_progress(clock_start,k/ncenters,msg,prev_msg);
    end
end

%% store the output
% this uses the neighborhood from spherical voxel selection,
% after removing the fields neighbors and origin
res_map=nbrhood;
res_map=rmfield(res_map,{'neighbors','origin'});
res_map.samples=accs;

% little sanity check
cosmo_check_dataset(ds);

fprintf('Output dataset:\n');
cosmo_disp(res_map);


output_fn=fullfile(output_path, 'spherical_neighborhood_searchlight.nii');
cosmo_map2fmri(res_map, output_fn);
fprintf('Output written to %s\n', output_fn);

%% Plot a few slices
cosmo_plot_slices(res_map);