run measure searchlightΒΆ

Matlab output: run_measure_searchlight

%% Searchlight using a data measure
%
% Using cosmo_searchlight, run cross-validation with nearest neighbor
% classifier
%
% #   For CoSMoMVPA's copyright information and license terms,   #
% #   see the COPYING file distributed with CoSMoMVPA.           #

%% Define data
config=cosmo_config();
data_path=fullfile(config.tutorial_data_path,'ak6','s01');

targets=repmat(1:6,1,10);
chunks=floor(((1:60)-1)/6)+1;

ds = cosmo_fmri_dataset(fullfile(data_path,'glm_T_stats_perrun.nii'),...
                        'mask',fullfile(data_path, 'brain_mask.nii'), ...
                                'targets',targets,'chunks',chunks);

%% Set measure
% Use the cosmo_cross_validation_measure and set its parameters
% (classifier and partitions) in a measure_args struct.
measure = @cosmo_crossvalidation_measure;
measure_args = struct();
measure_args.classifier = @cosmo_classify_lda;
measure_args.partitions = cosmo_oddeven_partitioner(ds);

%% Define neighborhood
radius=3; % 3 voxels
% define a neighborhood using cosmo_spherical_neighborhood
nbrhood=cosmo_spherical_neighborhood(ds,'radius',radius);

% show a histogram of the number of voxels in each searchlight
count=cellfun(@numel,nbrhood.neighbors);
hist(count,100);
%%


%% Run the searchlight
% hint: use cosmo_searchlight with the measure, args and nbrhood

results = cosmo_searchlight(ds,nbrhood,measure,measure_args);

% the following command would store the results to disk:
% >> cosmo_map2fmri(results, [data_path 'measure_searchlight.nii']);

%% Make a histogram of classification accuracies
figure()
hist(results.samples,47)

%% Plot a map
figure();
cosmo_plot_slices(results);