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
Set measure
Define neighborhood
Run the searchlight
Make a histogram of classification accuracies
Plot a map

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);
% <@@<

+00:00:03 [####################] -00:00:00  mean size 111.5

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']);

+00:02:36 [####################] -00:00:00

Make a histogram of classification accuracies

figure()
hist(results.samples,47)

Plot a map

figure();
cosmo_plot_slices(results);