Searchlight for representational similarity analysis

Using cosmo_searchlight, run cross-validation with nearest neighbor classifier

Contents

Define data

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

data_path = fullfile(study_path, 's01');
data_fn = fullfile(data_path, 'glm_T_stats_perrun.nii');
mask_fn = fullfile(data_path, 'brain_mask.nii');
targets = repmat(1:6, 1, 10)';
ds = cosmo_fmri_dataset(data_fn, ...
                        'mask', mask_fn, ...
                        'targets', targets);

% compute average for each unique target, so that the dataset has 6
% samples - one for each target
ds = cosmo_fx(ds, @(x)mean(x, 1), 'targets', 1);

models_path = fullfile(study_path, 'models');
load(fullfile(models_path, 'behav_sim.mat'));
load(fullfile(models_path, 'v1_model.mat'));

Set measure

Set the 'measure' and 'measure_args' to use the @cosmo_target_dsm_corr_measure measure and set its parameters to so that the target_dsm is based on behav_sim.mat

% >@@>
measure = @cosmo_target_dsm_corr_measure;
measure_args = struct();
measure_args.target_dsm = behav;
% <@@<

% Enable centering the data
measure_args.center_data = true;

Run searchlight

use spherical neighborhood of 100 voxels

voxel_count = 100;
% define a neighborhood using cosmo_spherical_neighborhood
% >@@>
nbrhood = cosmo_spherical_neighborhood(ds, 'count', voxel_count);
% <@@<

% Run the searchlight
% >@@>
results = cosmo_searchlight(ds, nbrhood, measure, measure_args);
% <@@<

% Save the results to disc using the following command:
output_path = config.output_data_path;
cosmo_map2fmri(results, ...
               fullfile(output_path, 'rsm_searchlight_behav.nii'));

+00:00:01 [####################] -00:00:00  mean size 99.8
+00:00:02 [####################] -00:00:00  

Make a histogram of correlations

hist(results.samples, 47);

Show some slices

cosmo_plot_slices(results);

Advanced exercise: regresion-based RSA

% Using @cosmo_target_dsm_corr_measure, investigate the relative
% contributions of the v1-model and behavioural similarity matrix.
%
% Thus, set the 'measure' and 'measure_args' to use the
% @cosmo_target_dsm_corr_measure measure and set its parameters
% so that the 'glm_dsm' option uses the 'behav' and 'v1_model' targets
% >@@>
measure = @cosmo_target_dsm_corr_measure;
measure_args = struct();
measure_args.glm_dsm = {behav, v1_model};

% <@@<
% Enable centering the data
measure_args.center_data = true;

Run searchlight

use spherical neighborhood of 100 voxels

voxel_count = 100;
% define a neighborhood using cosmo_spherical_neighborhood
% >@@>
nbrhood = cosmo_spherical_neighborhood(ds, 'count', voxel_count);
% <@@<

% Run the searchlight
% >@@>
glm_dsm_results = cosmo_searchlight(ds, nbrhood, measure, measure_args);
% <@@<

% Save the results to disc using the following command:
output_path = config.output_data_path;
cosmo_map2fmri(glm_dsm_results, ...
               fullfile(output_path, 'rsm_searchlight_glm_behav-v1.nii'));

+00:00:01 [####################] -00:00:00  mean size 99.8
+00:00:03 [####################] -00:00:00  

Show behavioural searchlight map

figure();
cosmo_plot_slices(cosmo_slice(glm_dsm_results, 1));

Show V1 searchlight map

figure();
cosmo_plot_slices(cosmo_slice(glm_dsm_results, 2));

Published with MATLAB® R2024a