Crossvalidation searchlight

Contents

Define data

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

% In this exercise, output (NIFTI files) will be written to this directory
output_data_path=config.output_data_path;

data_fn=fullfile(data_path,'glm_T_stats_perrun.nii');
mask_fn=fullfile(data_path,'brain_mask.nii');
ds=cosmo_fmri_dataset(data_fn,'mask',mask_fn,...
                        'targets',repmat(1:6,1,10),...
                        'chunks',floor(((1:60)-1)/6)+1);

% remove constant features (due to liberal masking)
ds=cosmo_remove_useless_data(ds);

Neighborhood definition

% Use a searchlight with a more-or-less constant number of voxels,
% both near the edges of the brain and in the center of the brain.
nvoxels_per_searchlight=100;

% Define a spherical neighborhood with approximately
% 100 voxels around each voxel using cosmo_spherical_neighborhood,
% and assign the result to a variable named 'nbrhood'
% >@@>
nbrhood=cosmo_spherical_neighborhood(ds,'count',nvoxels_per_searchlight);
% <@@<

+00:00:06 [####################] -00:00:00  mean size 99.8

Define the measure as

define the measure as cosmo_crossvalidation_measure >@@>

measure=@cosmo_crossvalidation_measure;
% <@@<

% Define measure arguments.
% Ideally we would like to use nfold_partitioner (and you probably
% want to do this for a publication-quality), but this takes quite long.
% Instead, we take a short-cut here and use an
% odd-even partitioning scheme that has only one fold (test on odd chunks,
% test on even) using cosmo_oddeven_partitioner.

measure_args=struct();

% As the 'partitions' argument, use an odd-even partitioner
% >@@>
measure_args.partitions=cosmo_oddeven_partitioner(ds,'half');
% <@@<


% Use cosmo_classify_lda as classifier argument for the measure
% >@@>
measure_args.classifier=@cosmo_classify_lda;
% <@@<

Run searchlight

% Using the neighborhood, the measure, and the measure's arguments, run the
% searchlight using cosmo_searchlight. Assign the result to a variable
% named 'ds_cfy'

% >@@>
ds_cfy=cosmo_searchlight(ds,nbrhood,measure,measure_args);
% <@@<

+00:01:57 [####################] -00:00:00  

Visualize and store the results in a NIFTI file

% Visualize the results using cosmo_plot_slices
cosmo_plot_slices(ds_cfy);

% Set output filename
output_fn=fullfile(output_data_path,...
            sprintf('lda_odd-even_accuracy_d%.0fvx.nii',nvoxels_per_searchlight));

% Write output to a NIFTI file using cosmo_map2fmri
% >@@>
cosmo_map2fmri(ds_cfy, output_fn);
% <@@<

Published with MATLAB® R2016b