%% 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
%%%% >>> Your code here <<< %%%%
% 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);