OSCR

Disgust Propensity, Not Disgust Sensitivity, Shapes the Reactivity of a Subjective Disgust Circuit in Humans.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › First‐Level fMRI Analysis ↔ CanlabCore/@fmri_data/denoise_timeseries_pipeline.m, lines 1–104 · score 0.78 · high pass filter, Nuisance regressors, related regressors, implicit, realignment, cutoff
  2. [2] § Methods › First‐Level fMRI Analysis ↔ CanlabCore/Misc_utilities/movement_regressors.m, the whole file · a weak match · score 0.78 · realignment parameters, motion parameters, related regressors, timepoints, derivatives, SPM
  3. [3] § Methods › fMRI Data Preprocessing ↔ CanlabCore/@image_vector/outliers.m, lines 1–60 · score 0.72 · Mahalanobis distances, absolute deviations, nuisance regressor, outliers, intensity
  4. [4] § Methods › fMRI Data Preprocessing ↔ CanlabCore/@fmri_data/extract_measures_batch.m, lines 1–60 · score 0.52 · Mahalanobis distances, global, preprocess, nuisance, outliers, volumes
  5. [5] § Methods › fMRI Data Preprocessing ↔ CanlabCore/@image_vector/slice_movie.m, lines 1–60 · score 0.51 · magnetic field, distortions, slice, head, fMRI, voxel
  6. [6] § Methods › fMRI Data Preprocessing ↔ CanlabCore/@image_vector/outliers.m, lines 1–60 · score 0.51 · magnetic field, distortions, head, slice, mm, voxel

Paper

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The authors' code

MATLAB · 392 lines · 16 KB · GPL-2.0 · 2 matches

  1. function [est_outliers_uncorr, est_outliers_corr, outlier_tables] = outliers(dat, varargin)
  2. % Find outliers based on a combination of rmssd (DVARS), robust spatial
  3. % variance (MAD), Mahalanobis distance based on covariance and correlation
  4. % matrices, and images with >25% missing values
  5. %
  6. % :Usage:
  7. % ::
  8. %
  9. % [est_outliers_uncorr, est_outliers_corr, outlier_table] = outliers(dat, ['noverbose', 'notimeseries'])
  10. %
  11. % Images usually change slowly over time, and sudden changes in intensity can also often be a sign of bad things
  12. % -- head movement artifact or gradient misfires, interacting with the magnetic field to create distortion
  13. % across the brain.
  14. %
  15. % RMSSD (DVARS) tracks large changes across successive images, regardless of what the sign of the changes is or where they are.
  16. % In addition, images with unusually high spatial standard deviation across voxels may be outliers with image
  17. % intensity distortions in some areas of the image but not others (e.g., bottom half of brain vs. top half,
  18. % or odd vs. even slices).
  19. % This matrix can be added to your design matrix as a set of nuisance covariates of no interest.
  20. %
  21. % :Optional Inputs:
  22. %
  23. % **'madlim'**
  24. % Limit for median absolute deviation (MAD) for rmssd and spatial abs. deviation
  25. %
  26. % **'noverbose'**
  27. % Suppress verbose output
  28. %
  29. % **'noplot'**
  30. % Suppress plot output
  31. %
  32. % **'fullplot'**
  33. % A more detailed plot of each criterion
  34. %
  35. % **'notimeseries'**
  36. % Suppress time series-specific metrics -- use for 2nd-level contrasts or beta series
  37. %
  38. % **'fd'**
  39. % called framewise_displacement() to generate framewise_displacement
  40. % indicators. Requires movement matrix to be passed in as an argument.
  41. % CAUTION!! Rotations must be 1st 3 columns of mvmt_mtx, then translations
  42. %
  43. % **'fd_thresh'**
  44. % Followed by framewise displacement threshold in mm. Values exceeding this threshold
  45. % get flagged as outliers. Default=0.5mm'
  46. %
  47. %
  48. % :Outputs:
  49. %
  50. % **est_outliers_uncorr**
  51. % Logical vector of outliers at uncorrected thresholds
  52. %
  53. % **est_outliers_corr**
  54. % Logical vector of outliers at corrected thresholds (more conservative)
  55. %
  56. % **outlier_tables**
  57. % Tables of outliers under various criteria, and indicator matrices for
  58. % nuisance regressors
  59. %
  60. % outlier_indicator_table: Table of logical indicator vectors for each criterion measure (Matlab Table object)
  61. % score_table: Table of criterion scores (Matlab Table object)
  62. % summary_table: Table of outlier counts for each measure, and overall
  63. % est_outliers_uncorr Logical vector of outliers at uncorrected thresholds
  64. % est_outliers_corr Logical vector of outliers at corrected thresholds
  65. % outlier_regressor_matrix_uncorr: Indicator matrix for nuisance regressors, uncorrected
  66. % outlier_regressor_matrix_corr: Indicator matrix for nuisance regressors, uncorrected
  67. %
  68. % :Examples:
  69. % ::
  70. % % -------------------------------------------------------------------------
  71. % % A minimal example on person-level (non-time series) image data
  72. % obj = load_image_set('emotionreg');
  73. % [est_outliers_uncorr, est_outliers_corr, outlier_tables] = outliers(obj, 'notimeseries');
  74. %
  75. % % -------------------------------------------------------------------------
  76. % % Load a multi-study dataset, rescale it, and identify/plot outliers
  77. % % Use 'notimeseries' option because this is not a time series dataset
  78. %
  79. % obj = load_image_set('kragel18_alldata');
  80. % obj2 = rescale(obj, 'l2norm_images'); % normalize heterogeneous datasets
  81. % [est_outliers_uncorr, est_outliers_corr, outlier_tables] = outliers(obj2, 'notimeseries');
  82. %
  83. % % -------------------------------------------------------------------------
  84. %
  85. % % -------------------------------------------------------------------------
  86. % % Load a dataset from CANlab 2nd-level batch script output and assess outliers
  87. % % across condition images. Select subjects with no outliers in any
  88. % % condition
  89. %
  90. % [est_outliers_uncorr, est_outliers_corr, outlier_tables] = outliers(obj2, 'notimeseries');
  91. % load('data_objects.mat') % Load DATA_OBJ
  92. % load('image_names_and_setup.mat'); % Load DAT
  93. % obj = cat(DATA_OBJ{:});
  94. % [est_outliers_uncorr, est_outliers_corr, outlier_tables] = outliers(obj, 'notimeseries');
  95. %
  96. % % -------------------------------------------------------------------------
  98. % Programmers Notes:
  99. % ..
  100. % Created 11/6/2021 by Tor Wager, from a combination of other code
  101. % (default: based on estimated outliers at 3 standard deviations.)
  102. %
  103. % Updated 10/1/2024 by Michael Sun PhD to add fd argument for
  104. % framewise_displacement.
  105. % ..
  107. % -------------------------------------------------------------------------
  108. % DEFAULT ARGUMENT VALUES
  109. % -------------------------------------------------------------------------
  110. madlim = 3; % Also Z-score limit for global means
  111. dotimeseries = true; % Adds rmssd/dvars, time series-specific outliers
  112. doverbose = true;
  113. verbosestr = 'doverbose';
  114. doplot = true;
  115. dobriefplot = true;
  116. dofd = false;
  117. fd_thresh = 0.5;
  119. % -------------------------------------------------------------------------
  120. % OPTIONAL INPUTS
  121. % -------------------------------------------------------------------------
  123. % This is a compact way to assign multiple variables. The input argument
  124. % names and variable names must match, however:
  126. allowable_inputs = {'madlim' 'doverbose' 'dotimeseries' 'plot', 'fd', 'fd_thresh'};
  128. keyword_inputs = {'noverbose' 'notimeseries' 'noplot' 'fullplot'};
  130. % optional inputs with default values - each keyword entered will create a variable of the same name
  132. for i = 1:length(varargin)
  133. if ischar(varargin{i})
  134. switch varargin{i}
  136. case allowable_inputs
  138. eval([varargin{i} ' = varargin{i+1}; varargin{i+1} = [];']);
  139. if strcmp(varargin{i}, 'fd')
  140. dofd = true;
  142. elseif strcmp(varargin{i}, 'fd_thresh')
  143. fd_thresh = varargin{i+1};
  144. end
  146. case keyword_inputs
  147. % Skip, deal with these below
  149. otherwise, warning(['Unknown input string option:' varargin{i}]);
  150. end
  151. end
  152. end
  154. % 2nd pass: Keyword inputs. These supersede earlier inputs
  155. for i = 1:length(varargin)
  156. if ischar(varargin{i})
  157. switch varargin{i}
  159. case {'noverbose'}
  160. doverbose = false;
  162. case {'notimeseries'}
  163. dotimeseries = false;
  165. case {'noplot'}
  166. doplot = false;
  168. case 'fullplot'
  169. dobriefplot = false;
  171. end
  172. end
  173. end
  175. % -------------------------------------------------------------------------
  176. % MAIN FUNCTION
  177. % -------------------------------------------------------------------------
  179. % -------------------------------------------------------------------------
  180. % Global mean and variance-related
  181. % -------------------------------------------------------------------------
  183. if doverbose
  184. disp('______________________________________________________________')
  185. disp('Outlier analysis')
  186. disp('______________________________________________________________')
  188. fprintf('global mean | global mean to var | spatial MAD | ');
  189. else
  190. verbosestr = 'noverbose';
  191. end
  193. robustz = @(x) (x - mean(x)) ./ mad(x);
  195. gm = mean(dat.dat)';
  196. sm = mad(dat.dat)';
  198. globalmean = abs( robustz( gm ) );
  199. spatialmad = abs( robustz( sm ) );
  200. global_mean_to_var = abs( robustz( gm ./ sm ) );
  202. global_mean_outliers = globalmean > madlim; % absolute robust zscore of global mean > limit (default = 3)
  203. global_mean_to_variance_outliers = global_mean_to_var > madlim; % absolute robust zscore of global mean over Median Abs Deviation > limit
  204. spatialmad_outliers = spatialmad > madlim; %spatialmad > mean(spatialmad) + madlim * mad(spatialmad);
  207. % -------------------------------------------------------------------------
  208. % Time series-specific outliers
  209. % -------------------------------------------------------------------------
  211. if dotimeseries
  213. if doverbose
  214. fprintf('rmssd | ');
  215. end
  217. % Get RMSSD (DVARS)
  218. sdiffs = diff(dat.dat')';
  219. sdiffs = [mean(sdiffs, 2) sdiffs]; % keep in image order
  221. rmssd = ( mean(sdiffs .^ 2) ) .^ .5; % rmssd - root mean square successive diffs
  222. rmssd(1) = median(rmssd); % avoid first time point being very different and influencing distribution and plots.
  223. rmssd = rmssd';
  225. rmssd_outliers = rmssd > mean(rmssd) + madlim * mad(rmssd);
  227. else
  228. % Omit time series measures -- all false
  230. rmssd = NaN .* zeros(size(global_mean_outliers));
  231. rmssd_outliers = false(size(global_mean_outliers));
  233. end
  235. % -------------------------------------------------------------------------
  236. % Coverage
  237. % -------------------------------------------------------------------------
  239. if doverbose
  240. fprintf('Missing values | ');
  241. end
  243. desc = descriptives(dat, 'noverbose');
  244. missingvals = desc.images_missing_over_25percent';
  246. if doverbose
  247. fprintf('%3.0f images \n', sum(missingvals));
  248. fprintf('\n\n');
  249. end
  251. % -------------------------------------------------------------------------
  252. % Multivariate outliers
  253. % -------------------------------------------------------------------------
  255. % Get mahalanobis outliers
  256. [mahalcov, ~, ~, mahal_cov_outlier_uncorr, mahal_cov_outlier_corr] = mahal(dat, 'noplot', verbosestr);
  257. [mahalcorr, ~, ~, mahal_corr_outlier_uncorr, mahal_corr_outlier_corr] = mahal(dat, 'corr', 'noplot', verbosestr);
  259. if doverbose
  260. fprintf('Mahalanobis (cov and corr, q<0.05 corrected):\n');
  261. fprintf('%3.0f images \n', sum(mahal_cov_outlier_corr | mahal_corr_outlier_corr));
  262. end
  264. % -------------------------------------------------------------------------
  265. % Framewise Displacement
  266. % -------------------------------------------------------------------------
  268. if dofd
  269. % Get FD
  270. % CAUTION!! Rotations must be 1st 3 columns of mvmt_mtx, then translations
  271. [fwd, ~, est_outliers] = framewise_displacement(fd, 'thresh', fd_thresh);
  273. fd_out = est_outliers;
  275. if doverbose
  276. fprintf('Framewise Displacement (before and after >%0.2f mm correction):\n',fd_thresh);
  277. fprintf('%3.0f images \n', sum(fd_out));
  278. end
  279. end
  281. % -------------------------------------------------------------------------
  282. % Summarize
  283. % -------------------------------------------------------------------------
  285. if dofd
  286. est_outliers_uncorr = global_mean_outliers | global_mean_to_variance_outliers | rmssd_outliers | spatialmad_outliers | mahal_cov_outlier_uncorr | mahal_corr_outlier_uncorr | fd_out | missingvals;
  287. est_outliers_corr = global_mean_outliers | global_mean_to_variance_outliers | rmssd_outliers | spatialmad_outliers | mahal_cov_outlier_corr | mahal_corr_outlier_corr | fd_out | missingvals;
  289. % Make indicator table
  291. outlier_indicator_table = table(global_mean_outliers, global_mean_to_variance_outliers, missingvals, rmssd_outliers, spatialmad_outliers, mahal_cov_outlier_uncorr, mahal_cov_outlier_corr, mahal_corr_outlier_uncorr, mahal_corr_outlier_corr, fd_out, est_outliers_uncorr, est_outliers_corr, ...
  292. 'VariableNames', {'global_mean' 'global_mean_to_variance' 'missing_values', 'rmssd_dvars', 'spatial_variability', 'mahal_cov_uncor', 'mahal_cov_corrected', 'mahal_corr_uncor', 'mahal_corr_corrected', 'fd', 'Overall_uncorrected', 'Overall_corrected'});
  294. else
  295. est_outliers_uncorr = global_mean_outliers | global_mean_to_variance_outliers | rmssd_outliers | spatialmad_outliers | mahal_cov_outlier_uncorr | mahal_corr_outlier_uncorr | missingvals;
  296. est_outliers_corr = global_mean_outliers | global_mean_to_variance_outliers | rmssd_outliers | spatialmad_outliers | mahal_cov_outlier_corr | mahal_corr_outlier_corr | missingvals;
  298. % Make indicator table
  300. outlier_indicator_table = table(global_mean_outliers, global_mean_to_variance_outliers, missingvals, rmssd_outliers, spatialmad_outliers, mahal_cov_outlier_uncorr, mahal_cov_outlier_corr, mahal_corr_outlier_uncorr, mahal_corr_outlier_corr, est_outliers_uncorr, est_outliers_corr, ...
  301. 'VariableNames', {'global_mean' 'global_mean_to_variance' 'missing_values', 'rmssd_dvars', 'spatial_variability', 'mahal_cov_uncor', 'mahal_cov_corrected', 'mahal_corr_uncor', 'mahal_corr_corrected' 'Overall_uncorrected' 'Overall_corrected'});
  303. end
  305. outliercounts = sum(table2array(outlier_indicator_table))';
  307. summary_table = table(outliercounts, 100 * outliercounts ./ desc.n_images, 'VariableNames', {'Outlier_count' 'Percentage'}, 'RowNames', outlier_indicator_table.Properties.VariableNames);
  309. if doverbose, disp(summary_table); end
  311. % Make score Table
  313. score_table = table(globalmean, global_mean_to_var, spatialmad, rmssd, mahalcov, mahalcorr, ...
  314. 'VariableNames', {'globalmean' 'global_mean_to_var' 'spatialmad' 'rmssd_dvars' 'mahal_cov' 'mahal_corr'});
  316. outlier_tables = struct('outlier_indicator_table', outlier_indicator_table, 'score_table', score_table, 'summary_table', summary_table);
  318. outlier_tables.est_outliers_uncorr = est_outliers_uncorr;
  319. outlier_tables.est_outliers_corr = est_outliers_corr;
  321. % -------------------------------------------------------------------------
  322. % Make indicator matrices
  323. % -------------------------------------------------------------------------
  325. outlier_tables.outlier_regressor_matrix_uncorr = intercept_model(size(dat.dat, 2), find(est_outliers_uncorr));
  326. outlier_tables.outlier_regressor_matrix_uncorr = outlier_tables.outlier_regressor_matrix_uncorr(:, 2:end); % remove initial intercept column
  328. outlier_tables.outlier_regressor_matrix_corr = intercept_model(size(dat.dat, 2), find(est_outliers_corr));
  329. outlier_tables.outlier_regressor_matrix_corr = outlier_tables.outlier_regressor_matrix_corr(:, 2:end); % remove initial intercept column
  332. % -------------------------------------------------------------------------
  333. % Plot
  334. % -------------------------------------------------------------------------
  335. if doplot
  337. if dobriefplot
  338. hold on;
  339. scores = zscore(table2array(score_table));
  340. maxscore = nanmax(scores, [], 2);
  341. plot(scores, 'k.-'); %, 'MarkerSize', 4);
  343. plot(find(est_outliers_uncorr), maxscore(est_outliers_uncorr), '+', 'color', [1 .3 .3], 'MarkerSize', 4, 'LineWidth', 2, 'MarkerFaceColor', [.5 .25 0]);
  344. plot(find(est_outliers_corr), maxscore(est_outliers_corr), 'ro', 'MarkerSize', 6, 'LineWidth', 2, 'MarkerFaceColor', [1 .5 0]);
  346. xlabel('Case number');
  347. ylabel('Scaled outlier criterion scores');
  349. else % full plot
  350. create_figure('plot', 3, 2);
  351. plot(score_table.globalmean); plot(find(global_mean_outliers), score_table.globalmean(find(global_mean_outliers)), 'ro', 'MarkerFaceColor', 'r');
  352. title('Global mean')
  354. subplot(3, 2, 2)
  355. plot(global_mean_to_var); plot(find(global_mean_to_variance_outliers), global_mean_to_var(find(global_mean_to_variance_outliers)), 'ro', 'MarkerFaceColor', 'r');
  356. title('Global mean to var')
  358. subplot(3, 2, 3)
  359. x = spatialmad;
  360. wh = spatialmad_outliers;
  361. plot(x); plot(find(wh), x(find(wh)), 'ro', 'MarkerFaceColor', 'r');
  362. title('Spatialmad')
  364. subplot(3, 2, 4)
  365. x = mahalcov;
  366. wh = mahal_cov_outlier_corr;
  367. plot(x); plot(find(wh), x(find(wh)), 'ro', 'MarkerFaceColor', 'r');
  368. title('Mahal cov')
  370. subplot(3, 2, 5)
  371. x = rmssd;
  372. wh = rmssd_outliers;
  373. plot(x); plot(find(wh), x(find(wh)), 'ro', 'MarkerFaceColor', 'r');
  374. title('RMSSD')
  375. xlabel('Case number');
  378. subplot(3, 2, 6)
  379. x = mahalcorr;
  380. wh = mahal_corr_outlier_corr;
  381. plot(x); plot(find(wh), x(find(wh)), 'ro', 'MarkerFaceColor', 'r');
  382. title('Mahal corr')
  383. xlabel('Case number');
  385. end
  388. end
  390. end % main function

outliers.m at commit cfc8292, under GPL-2.0 · at the source

Overview

Authors: Xianyang Gan1,2, Zihao Zheng1,2, Ran Zhang3,4, Feng Zhou3,4, Ting Xu3,4, Nan Qiu1,2, Junjie Wang1,2, Heng Jiang1,2, Shan Gao5, Yu Wu6, Benjamin Klugah‐Brown1,2, Dezhong Yao1,2, Benjamin Becker6,7
ORCID iDs: Nan Qiu, Heng Jiang, Shan Gao, Benjamin Klugah‐Brown, Dezhong Yao, Benjamin Becker
  1. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
  2. China‐Cuba Belt and Road Joint Laboratory on Neurotechnology and Brain‐Apparatus Communication, University of Electronic Science and Technology of China, Chengdu, China
  3. Faculty of Psychology, Southwest University, Chongqing, China
  4. Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
  5. School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China
  6. MIND & AI Lab, Department of Psychology, The University of Hong Kong, Hong Kong, Special Administrative Region, China
  7. SRT AI, Society & Social Dynamics, Faculty of Social Sciences, The University of Hong Kong, Hong Kong, Special Administrative Region, China
Institutions: University of Electronic Science and Technology of China (China); Southwest University (China); University of Hong Kong (Hong Kong SAR China)
Journal: Human brain mapping, volume 47, issue 8, article e70577
Dates: received 2 September 2025; accepted 28 May 2026; published online 4 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70577 · PMID 42237663 · PMCID PMC13581101 · OpenAlex W4413883853
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.27.672493
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Connectivity, Statistics, fMRI & imaging
Keywords: disgust, emotion, fMRI, insula, propensity, sensitivity, striatum
MeSH: Cerebral Cortex*, Corpus Striatum*, Disgust*, Hippocampus*, Insular Cortex*, Nerve Net*, Personality*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Psychology of Moral and Emotional Judgment (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministry of Science and Technology of the People's Republic of China (2022ZD0208500); Hong Kong University Grants Council (17615525); National Natural Science Foundation of China (82271583); University of Hong Kong seed funding and start-up schemes (2407102536)
Citations: not cited yet (Europe PMC); 88 references in the paper

Abstract

Disgust constitutes an evolutionary adaptive defensive‐avoidance response, yet humans vary markedly in their dispositional tendency to experience disgust (disgust propensity) and in their negative appraisal of such experience (disgust sensitivity). Conceptual frameworks and neuroimaging studies suggest that these traits may differentially modulate neural responses to disgust‐eliciting stimuli; however, methodological constraints have left their precise roles unresolved. Our comparably large fMRI study (n = 142) therefore aimed to systematically determine how trait disgust modulates neural responses to carefully selected and validated disgust‐specific visual stimuli across varying levels of subjective disgust experience. The whole‐brain voxel‐wise regression analyses revealed a differential pattern of neural associations between the two disgust traits, with disgust propensity, but not disgust sensitivity, modulating disgust‐related neural activity in the anterior, middle, and posterior insula, as well as the caudate, putamen, thalamus, hippocampus, and parahippocampal gyrus. Mediation and network‐level analyses further supported this partly dissociable pattern by showing that disgust propensity shapes disgust experience via insula‐striatal‐hippocampal pathways. Together, these findings provide evidence for a differential association between disgust propensity and disgust sensitivity with disgust‐related neural responses and elucidate how trait disgust shapes subjective experiences. They further suggest that disgust propensity and the identified systems may represent promising targets for the regulation of disgust‐related pathology.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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canlab/CanlabCore

License: GPL-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cfc8292b67956e27341b0431296471c83e03360b, 26 September 2026
Languages: MATLAB (2438), C (91), C++ (66), Python (59), Java (47), C/C++ (44), Shell (10), R (2), JavaScript (2)
Size: 4,175 files, 2,759 scripts
Software Heritage: not archived
Found in: the text, “fMRI Data Preprocessing”
Holds: continuous integration
Not found: README, license file, CITATION.cff, environment file, tests, documentation
Tools: SPM (160 files), Statistics and Machine Learning Toolbox (151 files), Brain Connectivity Toolbox (22 files), Optimization Toolbox (8 files), FreeSurfer (6 files), GIfTI library for MATLAB (3 files), Signal Processing Toolbox (3 files), boundedline (2 files), FieldTrip (2 files), Image Processing Toolbox (2 files), pandas (2 files), UMAP (2 files), AFNI (1 file), cifti-matlab (1 file), DPABI (1 file), fdr_bh (Benjamini-Hochberg FDR) (1 file), FSL (1 file), GIFT (1 file), Parallel Computing Toolbox (1 file), Matplotlib (1 file), Numba (1 file), NumPy (1 file), Violinplot-Matlab (1 file), Connectome Workbench (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files
At the source: github.com/canlab/CanlabCore

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2,000 scripts, each with its path and the digest of its content;
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Data

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Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 7 keywords, 14 MeSH terms, 4 funders, 87 references.

Cite

This paper

Gan, X., Zheng, Z., Zhang, R., Zhou, F., Xu, T., Qiu, N., Wang, J., Jiang, H., Gao, S., Wu, Y., Klugah‐Brown, B., Yao, D., & Becker, B. (2026). Disgust Propensity, Not Disgust Sensitivity, Shapes the Reactivity of a Subjective Disgust Circuit in Humans. Human brain mapping, 47(8), e70577. https://doi.org/10.1002/hbm.70577

BibTeX

@article{gan2026disgust,
author = {Gan, Xianyang and Zheng, Zihao and Zhang, Ran and Zhou, Feng and Xu, Ting and Qiu, Nan and Wang, Junjie and Jiang, Heng and Gao, Shan and Wu, Yu and Klugah‐Brown, Benjamin and Yao, Dezhong and Becker, Benjamin},
title = {{Disgust Propensity, Not Disgust Sensitivity, Shapes the Reactivity of a Subjective Disgust Circuit in Humans}},
journal = {Human brain mapping},
year = {2026},
month = jun,
volume = {47},
number = {8},
pages = {e70577},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70577},
url = {https://doi.org/10.1002/hbm.70577},
pmid = {42237663},
pmcid = {PMC13581101}
}

RIS

TY - JOUR
AU - Gan, Xianyang
AU - Zheng, Zihao
AU - Zhang, Ran
AU - Zhou, Feng
AU - Xu, Ting
AU - Qiu, Nan
AU - Wang, Junjie
AU - Jiang, Heng
AU - Gao, Shan
AU - Wu, Yu
AU - Klugah‐Brown, Benjamin
AU - Yao, Dezhong
AU - Becker, Benjamin
TI - Disgust Propensity, Not Disgust Sensitivity, Shapes the Reactivity of a Subjective Disgust Circuit in Humans
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/06/01
VL - 47
IS - 8
SP - e70577
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70577
UR - https://doi.org/10.1002/hbm.70577
LA - en
ER -

CSL-JSON

{
"id": "10.1002/hbm.70577",
"type": "article-journal",
"title": "Disgust Propensity, Not Disgust Sensitivity, Shapes the Reactivity of a Subjective Disgust Circuit in Humans",
"container-title": "Human brain mapping",
"author": [
{
"family": "Gan",
"given": "Xianyang"
},
{
"family": "Zheng",
"given": "Zihao"
},
{
"family": "Zhang",
"given": "Ran"
},
{
"family": "Zhou",
"given": "Feng"
},
{
"family": "Xu",
"given": "Ting"
},
{
"family": "Qiu",
"given": "Nan"
},
{
"family": "Wang",
"given": "Junjie"
},
{
"family": "Jiang",
"given": "Heng"
},
{
"family": "Gao",
"given": "Shan"
},
{
"family": "Wu",
"given": "Yu"
},
{
"family": "Klugah‐Brown",
"given": "Benjamin"
},
{
"family": "Yao",
"given": "Dezhong"
},
{
"family": "Becker",
"given": "Benjamin"
}
],
"container-title-short": "Hum Brain Mapp",
"volume": "47",
"issue": "8",
"page": "e70577",
"DOI": "10.1002/hbm.70577",
"PMID": "42237663",
"PMCID": "PMC13581101",
"ISSN": "1065-9471",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/hbm.70577",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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