Contents

Dependencies:

% Matlab statistics toolbox
    % Matlab signal processing toolbox
    % Statistical Parametric Mapping (SPM) software https://www.fil.ion.ucl.ac.uk/spm/
    % For full functionality, the full suite of CANlab toolboxes is recommended. See here: Installing Tools

% test_dat = load_image_set('npsplus', 'noverbose');
%  image_obj = get_wh_image(test_dat, 1);

test_dat = load_image_set('emotionreg', 'noverbose');
 image_obj = mean(test_dat);


% If data file is not found:
% cd('/Users/tor/Google_Drive/CanlabDataRepository/Neuroimaging_Autolabeler')
% g =genpath(pwd); addpath(g); savepath

Run neurosynth similarity

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( image_obj, 'images_are_replicates', false, 'noverbose');

% Example for NPS:
%     testr_low      words_low      testr_high      words_high
%     _________    _____________    __________    _______________
%
%     -0.22531     'object'         0.27001       'pain'
%      -0.2179     'recognition'    0.26484       'stimulation'
%     -0.21431     'objects'        0.26199       'heat'
%     -0.21123     'visual'         0.25992       'noxious'
%     -0.20244     'reading'        0.25942       'painful'
%     -0.20204     'perceptual'     0.24892       'sensation'
%     -0.19627     'words'           0.2354       'nociceptive'
%     -0.19202     'read'           0.23043       'somatosensory'
%     -0.18744     'semantic'       0.22025       'temperature'
%     -0.18732     'memory'         0.20817       'sensory'

Input image 1
fullpath_was_empty
_____________________________________________________________________
    testr_low       words_low       testr_high     words_high 
    _________    _______________    __________    ____________

    -0.23326     'stimulation'        0.2495      'monitoring'
    -0.19523     'frequency'         0.24703      'control'   
    -0.17128     'noise'             0.24036      'memory'    
    -0.16888     'male'              0.23539      'working'   
    -0.15396     'adaptation'        0.22153      'demand'    
    -0.15345     'female'            0.22031      'demands'   
    -0.15209     'somatosensory'     0.21877      'executive' 
    -0.15023     'images'            0.21601      'correct'   
    -0.14991     'animal'            0.21213      'knowledge' 
    -0.13918     'sensory'           0.19795      'conflict'  

Aggregate results for plot

lowwords = [top_feature_tables{1}.words_low(:)]';
disp(lowwords)

highwords = [top_feature_tables{1}.words_high(:)]';
disp(highwords)

r_low = top_feature_tables{1}.testr_low;
r_high = top_feature_tables{1}.testr_high;

r_to_plot = [r_high; r_low];
textlabels = [ highwords lowwords];

  Columns 1 through 5

    'stimulation'    'frequency'    'noise'    'male'    'adaptation'

  Columns 6 through 10

    'female'    'somatosensory'    'images'    'animal'    'sensory'

  Columns 1 through 6

    'monitoring'    'control'    'memory'    'working'    'demand'    'demands'

  Columns 7 through 10

    'executive'    'correct'    'knowledge'    'conflict'

create_figure('wedge_plot');

%hh = tor_wedge_plot(r_to_plot, textlabels, 'outer_circle_radius', .3, 'colors', {[1 .7 0] [.4 0 .8]}, 'nofigure');

hh = tor_wedge_plot(r_to_plot, textlabels, 'outer_circle_radius', .3, 'colors', {[1 .7 0] [.4 0 .8]}, 'bicolor', 'nofigure');

Published with MATLAB® R2018a