Gray matter volume correlates of resilience: a coordinate-based meta-analysis.

Code ↔ Paper

2 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 2 matches

[1] § Methods › Statistical analysis ↔ Miscellaneous_Functions/read_database.m, lines 1 to 71 · score 0.61 · MNI space, Talairach coordinates, transformation, weighted, clusters, meta · Report a mismatch
[2] § Methods › Statistical analysis ↔ densityUtility3/Support_functions_density/meta_stochastic_activation_blobs.m, lines 1 to 110 · score 0.53 · Monte Carlo, weighted proportion, FWE, map, thresholds, Voxels · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

MATLAB · 268 lines · 9.5 KB · GPL-2.0 · 1 match

% script read_database
% reads a neuroimaging coordinate database with fields
% and transforms T88 coordinates to MNI coordinates
% When you set up your own database, several rules apply. Formatting the spreadsheet
% so it loads correctly can sometimes be the hardest part of running a
% meta-analysis.
%
% See https://canlabweb.colorado.edu/wiki/doku.php/help/meta/meta_analysis_mkda
%
% And https://canlabweb.colorado.edu/wiki/doku.php/help/meta/database
%
% Here are some rules for setting up the file:
% 1 The variable "dbname" in the workspace should specify the name of your coordinate database file
% 2 The database must be a text file, tab delimited
% 3 The 1st row of the database must contain the number of columns as its 1st and only entry
% 4 The 2nd row of database must contain variable names (text, no spaces or special characters)
% 5 The 3rd - nth rows of the database contains data
% 6 Talairach coordinates are indicated by T88 in CoordSys variable
% 7 coordinate fields should be called x, y, and z
% 8 Do NOT use Z, or any other field name in clusters structure
%
% The second row of your database should contain names for each variable
% you have coded. Some variables should be named with special keywords,
% because they are used in the meta-analysis code. Other variables can be named
% anything, as long as there are *no spaces or special characters* in the
% name (e.g., !@#$%^&*(){}[] ~`\/|<>,.?/;:"''+=).
% Anything that you could not name a variable in Matlab will also not work
% here.
% Here are the variable names with special meaning. They are case-sensitive:
%
% Subjects : Sample size of the study to which the coordinate belongs
% FixedRandom : Study used fixed or random effects.
% Values should be Fixed or Random.
% Fixed effects coordinates will be automatically
% downweighted
% SubjectiveWeights : A coordinate or contrast weighting vector based on FixedRandom
% and whatever else you want to weight by; e.g., study reporting threshold
% The default is to use FixedRandom only if available
% x, y, z : X, Y, and Z coordinates
% study : name of study
% Contrast : unique indices (e.g., 1:k) for each independent
% contrast. This is a required variable!
% All rows belonging to the same contrast should
% (almost) always have the same values for every
% variable other than x, y, and z.
% CoordSys : Values should be MNI or T88, for MNI space or Talairach space
% Talairach coordinates will be converted to MNI using
% Matthew Brett's transform
%
% Note: On MAC, seems to read stuff saved as Windows Text in Excel
%
% AN EXAMPLE FROM THE SADNESS DATABASE
% % 1 - save sheet as sheet name.txt, Windows format text
% e.g., sheet name is MastervNeutralOnly.txt
% clear variables
% clear
% define the name of the sheet to load
% dbname = 'MastervNeutralOnly.txt'
% read the database in, all columns
% read_database
% save a matlab .mat data file with these columns (variables) in it
% call it the same name as the sheet
% save MastervNeutralOnly
% to load the file again, just type load filename
% load MastervNeutralOnly
%
% If a variable dbsave is created with a text string, the DB structure will
% be saved in a .mat file with the name specified by the string.
clear a DBnames
global study
% not to be used in variable names
badstrings = '!@#$%^&*(){}[] ~`\/|<>,.?/;:"''+=';
% -----------------------------------------------------------------------------
% * load database and read in first column - first row of first col is num columns
% -----------------------------------------------------------------------------
if ~exist('dbname') == 1, dbname = input('Enter name of input file: ','s'); end
a = textread(dbname,'%s%*[^\n]','delimiter','\t');
numc = str2num(a{1});
str = repmat('%s',1,numc);
% -----------------------------------------------------------------------------
% * define output string
% -----------------------------------------------------------------------------
outstr = ['[a1'];
for i = 2:numc
outstr = [outstr ',a' num2str(i)];
end
outstr = [outstr ']'];
% -----------------------------------------------------------------------------
% * load full database as strings
% -----------------------------------------------------------------------------
eval([outstr ' = textread(dbname,str,''delimiter'',''\t'');'])
% -----------------------------------------------------------------------------
% * change strings to numbers, if possible
% -----------------------------------------------------------------------------
myl = length(a1) - 2;
for i = 1:numc
eval(['vec = a' num2str(i) '(3:end);'])
vecout = [];
for j = 1:myl
try
vecout(j,1) = str2num(vec{j});
catch
break
end
end
if isempty(vecout) | length(vecout) < length(vec), vecout = vec; end
eval(['ColumnNames{i} = a' num2str(i) '{2};'])
eval(['myname = a' num2str(i) '{2};'])
% get rid of problematic characters
myname = deblank(myname);
for j = 1:length(badstrings), myname(strfind(myname, badstrings(j))) = []; end
DBnames{i} = myname;
eval([myname ' = vecout;'])
eval(['clear a' num2str(i)])
end
% -----------------------------------------------------------------------------
% * check x, y, z
% -----------------------------------------------------------------------------
if ~(exist('x') == 1 && exist('y') == 1 && exist('z') == 1)
disp('Database must have columns named x, y, and z, which contain numeric coordinates.');
error('One of these is missing.')
elseif iscell(x) || iscell(y) || iscell(z)
disp('Database must have columns named x, y, and z, which contain numeric coordinates.');
disp('Finding errors:')
if iscell(x)
xx = cellfun(@str2num, x, 'UniformOutput', 0);
fprintf('x has %d bad values.\nRows:\n', sum(cellfun(@isempty, xx)));
find(cellfun(@isempty, xx))
end
if iscell(y)
yy = cellfun(@str2num, y, 'UniformOutput', 0);
fprintf('x has %d bad values.\nRows:\n', sum(cellfun(@isempty, yy)));
find(cellfun(@isempty, yy))
end
if iscell(z)
zz = cellfun(@str2num, z, 'UniformOutput', 0);
fprintf('z has %d bad values.\nRows:\n', sum(cellfun(@isempty, zz)));
find(cellfun(@isempty, zz))
z(find(cellfun(@isempty, zz)))
end
% for i = 1:length(x)
% try
% xx(i, 1) = str2num(x{i});
% yy(i, 1) = str2num(y{i});
% zz(i, 1) = str2num(z{i});
% catch
% fprintf('Row %3.0f : Bad value for x, y, or z. Values entered are : %s %s %s\n', i, x{i}, y{i}, z{i});
% end
% end
error('One of these seems to have non-numeric values in at least one row (check for blank spaces/empty row at the end!)')
end
clear xt yt zt
% -----------------------------------------------------------------------------
% * convert from talairach to MNI, if we can
% -----------------------------------------------------------------------------
if exist('CoordSys') == 1 & exist('x') == 1 & exist('y') == 1 & exist('z') == 1
fprintf(1,'Converting T88 coordinates to MNI (M. Brett transform): ')
numt = 0;
for i = 1:length(CoordSys)
if strcmp(CoordSys{i},'T88')
XYZt = tal2mni([x(i) y(i) z(i)]);
x(i) = XYZt(1); y(i) = XYZt(2); z(i) = XYZt(3);
numt = numt + 1;
end
end
fprintf(1,'%3.0f Transformed\n',numt)
fprintf(1,'Converting MNI coordinates to T88 for XYZtal array: ')
numt = 0;
for i = 1:length(CoordSys)
if strcmp(CoordSys{i},'MNI') | strcmp(CoordSys{i},'spm96') | strcmp(CoordSys{i},'spm99')
XYZt = mni2tal([x(i) y(i) z(i)]);
xt(i) = XYZt(1); yt(i) = XYZt(2); zt(i) = XYZt(3);
numt = numt + 1;
else
xt(i) = x(i); yt(i) = y(i); zt(i) = z(i);
end
end
fprintf(1,'%3.0f Transformed\n',numt)
XYZall = [x y z];
XYZtal = [xt' yt' zt'];
end
% -----------------------------------------------------------------------------
% * convert T to Z, if all elements are present
% -----------------------------------------------------------------------------
if exist('Tscore') == 1 & exist('Zscore') == 1 & exist('N') == 1
fprintf(1,'Converting Tscores to Zscores\n ')
for i = 1:length(Tscore),
if ~isnan(Tscore(i)) & isnan(Zscore(i))
Zscore(i) = spm_t2z(Tscore(i),N(i)-1);
end
end
end
clear a, clear i, clear j, clear myl, clear myname, clear vec, clear vecout, clear numc
clear str, clear XYZt, clear numt
%DBnames = whos;
if ~exist('xyz') == 1 && exist('x', 'var') && exist('y', 'var') && exist('z', 'var'), xyz = [x y z]; end
for i = 1:length(DBnames)
eval(['LEN = length(' DBnames{i} ');'])
if exist('xyz', 'var')
% Meta-database, has XYZ, include only if right size
if LEN == size(xyz,1)
eval(['DB.(DBnames{i}) = ' DBnames{i} ';'])
end
else
% Other database; include all in struct.
eval(['DB.(DBnames{i}) = ' DBnames{i} ';'])
end
end
% Save, if dbsave var is entered
if ~exist('dbsave', 'var')
% do not save; do nothing
else
if isempty(dbsave)
dbsave = input('Enter name of file to save (without .mat extension): ','s');
end
eval(['save ' dbsave])
end

read_database.m at commit c77c20b, under GPL-2.0 · at the source

Overview

Authors: Niki Hosseini-Kamkar¹, Nellie Kamkar², Rupa Kalahasthi¹, Jihane Ghorayeb¹, Aamna Razaan¹, Annapurna Nair¹, Andrew A. Nicholson^3,4,5, Maaham Maswood¹

ORCID iDs: Rupa Kalahasthi, Aamna Razaan

Rochester Institute of Technology, Dubai, United Arab Emirates
London Health Sciences Centre Research Institute, London, ON, Canada
Atlas Institute for Veterans and Families, Ottawa, ON, Canada
School of Psychology, University of Ottawa, Ottawa, ON, Canada
The Institute of Mental Health Research, Royal Ottawa Hospital, University of Ottawa, Ottawa, ON, Canada

Institutions: Rochester Institute of Technology (United States); London Health Sciences Centre Research Institute (Canada); University of Ottawa (Canada); Royal Ottawa Mental Health Centre (Canada); Institute of Mental Health Research (Canada)

Journal: Frontiers in psychology, volume 17, article 1691912

Dates: received 24 August 2025; accepted 27 February 2026; published online 24 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3389/fpsyg.2026.1691912 · PMID 41953303 · PMCID PMC13053268 · OpenAlex W7140223932

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: structural MRI / diffusion (modality)

Methods: Statistics, fMRI & imaging, Preprocessing

Keywords: resilience, meta-analysis, gray matter volume, MKDA, coordinate-based meta-analysis

Topic: Resilience and Mental Health (Clinical Psychology, Psychology), according to OpenAlex

Citations: not cited yet (Europe PMC); 62 references in the paper

Abstract

Introduction: Psychological resilience refers to the capacity to maintain or regain functioning in the face of adversity, yet its structural substrates remain poorly characterized.

Methods: We conducted a coordinate-based meta-analysis of whole-brain voxel-based morphometry studies to identify gray-matter markers of resilience. Eleven eligible studies (n = 2,993) were synthesized using MKDA.

Results: Across studies reporting increased gray matter volume, a cluster showed convergence on the medial frontal wall, extending from the superior and medial frontal gyrus into the dorsal cingulate, k = 11 studies, family-wise error–corrected at p < 0.05 (10,000 permutations).

Discussion: The findings implicate medial frontal–cingulate architecture as a convergent correlate of resilience, consistent with models of fronto-limbic regulatory control. Despite the modest study base and variability in definitions of resilience across studies, this meta-analysis identifies the medial frontal–cingulate hub as a reproducible structural marker of resilience, offering a concrete target for translational and interventional research.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

canlab/Canlab_MKDA_MetaAnalysis

License: GPL-2.0

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: c77c20b96516e22890b604427c555d361fca0da0, 25 January 2024

Languages: MATLAB (202)

Size: 240 files, 202 scripts

Software Heritage: not archived

Found in: the end of the paper

Holds: license file, tests

Not found: README, CITATION.cff, environment file, continuous integration, documentation

Tools: SPM (54 files), Statistics and Machine Learning Toolbox (27 files)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

203 files

Meta_NBC/@meta_dataset/meta_dataset.m, MATLAB, 179 lines
Meta_NBC/@meta_dataset/plot.m, MATLAB, 274 lines
Meta_NBC/@meta_dataset/setup_data.m, MATLAB, 137 lines
Meta_NBC/@meta_nbc/cv.m, MATLAB, 83 lines
Meta_NBC/@meta_nbc/get_error.m, MATLAB, 58 lines
Meta_NBC/@meta_nbc/meta_nbc.m, MATLAB, 98 lines
Meta_NBC/@meta_nbc/paramopt.m, MATLAB, 11 lines
Meta_NBC/@meta_nbc/plot_sens_spec.m, MATLAB, 35 lines
Meta_NBC/@meta_nbc/report.m, MATLAB, 50 lines
Meta_NBC/@meta_nbc/test.m, MATLAB, 160 lines
Meta_NBC/@meta_nbc/train.m, MATLAB, 117 lines
Meta_NBC/Meta_NBC.m, MATLAB, 99 lines
Meta_NBC/Meta_NBC_from_mkda.m, MATLAB, 56 lines
Meta_NBC/Meta_SVM_from_mkda.m, MATLAB, 611 lines
Meta_NBC/example_scripts/classify_NBC_new_paindata1.m, MATLAB, 85 lines
Meta_NBC/example_scripts/classify_NBC_new_paindata2.m, MATLAB, 70 lines
Miscellaneous_Functions/Meta_tables_of_variables.m, MATLAB, 276 lines
Miscellaneous_Functions/database_point_probe.m, MATLAB, 144 lines
Miscellaneous_Functions/dbcluster_point_table.m, MATLAB, 87 lines
Miscellaneous_Functions/meta_analysis_table.m, MATLAB, 78 lines
Miscellaneous_Functions/read_database.m, MATLAB, 268 lines, 1 match
Miscellaneous_Functions/read_database2.m, MATLAB, 165 lines
Miscellaneous_Functions/read_database2_indevelopment.m, MATLAB, 12 lines
additional_utilities_and_imgs/points_in_sphere.m, MATLAB, 123 lines
citation_plotter/citation_mca.m, MATLAB, 185 lines
citation_plotter/make_indicator.m, MATLAB, 20 lines
citation_plotter/title_keywords.m, MATLAB, 89 lines
citation_plotter/titlemarker.m, MATLAB, 42 lines
cluster_multivariate/bootstrap_mtx.m, MATLAB, 139 lines
cluster_multivariate/burt_profile.m, MATLAB, 47 lines
cluster_multivariate/cluster_chi2.m, MATLAB, 226 lines
cluster_multivariate/cluster_manova.m, MATLAB, 208 lines
cluster_multivariate/confusion_matrix.m, MATLAB, 117 lines
cluster_multivariate/database2clusters.m, MATLAB, 212 lines
cluster_multivariate/db_cluster_burt_table.m, MATLAB, 227 lines
cluster_multivariate/db_cluster_table.m, MATLAB, 49 lines
cluster_multivariate/dbcluster2indic.m, MATLAB, 280 lines
cluster_multivariate/dbcluster_contrast_table.m, MATLAB, 336 lines
cluster_multivariate/dbcluster_contrast_table2.m, MATLAB, 260 lines
cluster_multivariate/dbcluster_contrast_table_xyz.m, MATLAB, 264 lines
cluster_multivariate/dbcluster_glass_plot.m, MATLAB, 134 lines
cluster_multivariate/dbcluster_meta_meta.m, MATLAB, 411 lines
cluster_multivariate/fuzzy_conf_cluster.m, MATLAB, 125 lines
cluster_multivariate/indic2classify1.m, MATLAB, 199 lines
cluster_multivariate/indic2classify2.m, MATLAB, 84 lines
cluster_multivariate/indic2condf.m, MATLAB, 29 lines
cluster_multivariate/manova_examples.m, MATLAB, 51 lines
cluster_multivariate/maxcor_npm.m, MATLAB, 93 lines
cluster_multivariate/perc_barplot.m, MATLAB, 288 lines
cluster_multivariate/permute_mtx.m, MATLAB, 349 lines
cluster_multivariate/reclassify_clusters.m, MATLAB, 66 lines
cluster_multivariate/simple_knn.m, MATLAB, 72 lines
cluster_multivariate/stackedbar.m, MATLAB, 40 lines
cluster_multivariate/stackedbar2.m, MATLAB, 20 lines
cluster_multivariate/tor_knn.m, MATLAB, 108 lines
cluster_multivariate/tor_mca.m, MATLAB, 257 lines
cluster_multivariate/weighted_knn.m, MATLAB, 79 lines
cluster_overlap_monte_carlo/cluster_overlap_npm.m, MATLAB, 208 lines
cluster_overlap_monte_carlo/overlap.m, MATLAB, 42 lines
cluster_overlap_monte_carlo/overlap_closest.m, MATLAB, 98 lines
cluster_overlap_monte_carlo/overlap_table.m, MATLAB, 53 lines
confidence_volume/confidence_volume.m, MATLAB, 88 lines
confidence_volume/ellipsoidMask.m, MATLAB, 29 lines
confidence_volume/rotateByEigen.m, MATLAB, 67 lines
densityUtility/density2_calculations/Meta_Chisq_old.m, MATLAB, 61 lines
densityUtility/density2_calculations/Meta_Chisq_old2.m, MATLAB, 61 lines
densityUtility/density2_calculations/dbcontrast2density copy.m, MATLAB, 403 lines
densityUtility/density2_calculations/dbcontrast2density.m, MATLAB, 430 lines
densityUtility/density2_calculations/mask2density_gauss.m, MATLAB, 238 lines
densityUtility/density2_calculations/p_task_given_activation.m, MATLAB, 133 lines
densityUtility/density2_calculations/pa_overall_threshold.m, MATLAB, 50 lines
densityUtility/density2_calculations/threshold_pa_overall.m, MATLAB, 145 lines
densityUtility/density2_calculations/tor_spm_mean_ui.m, MATLAB, 79 lines
densityUtility/density2_calculations/whole_brain_ptask_givena.m, MATLAB, 248 lines
densityUtility/density_analysis_calculations/d_pdf.m, MATLAB, 44 lines
densityUtility/density_analysis_calculations/density.m, MATLAB, 338 lines
densityUtility/density_analysis_calculations/density2clusters.m, MATLAB, 48 lines
densityUtility/density_analysis_calculations/density_diff.m, MATLAB, 195 lines
densityUtility/density_analysis_calculations/density_diff_pdf.m, MATLAB, 267 lines
densityUtility/density_analysis_calculations/density_pdf.m, MATLAB, 186 lines
densityUtility/density_analysis_calculations/fast_max_density.m, MATLAB, 178 lines
densityUtility/density_analysis_calculations/get_i_dens_threshold.m, MATLAB, 21 lines
densityUtility/density_analysis_calculations/get_random_mask.m, MATLAB, 74 lines
densityUtility/density_analysis_calculations/i_density_diff.m, MATLAB, 244 lines
densityUtility/density_analysis_calculations/mask2density.m, MATLAB, 190 lines
densityUtility/density_analysis_calculations/tor_conv_sphere.m, MATLAB, 89 lines
densityUtility/density_choose_radius.m, MATLAB, 58 lines
densityUtility/density_plots_tables/density_image.m, MATLAB, 97 lines
densityUtility/density_plots_tables/density_results_table.m, MATLAB, 66 lines
densityUtility/density_plots_tables/plot_dens_results.m, MATLAB, 65 lines
densityUtility/density_plots_tables/task_indicator_table.m, MATLAB, 25 lines
densityUtility/i_density.m, MATLAB, 289 lines
densityUtility/recover_choose_radius.m, MATLAB, 61 lines
densityUtility/smooth_dens_and_mask.m, MATLAB, 41 lines
densityUtility3/Meta_Activation.m, MATLAB, 135 lines
densityUtility3/Meta_Activation_FWE.m, MATLAB, 1,022 lines
densityUtility3/Meta_Analysis.m, MATLAB, 6 lines
densityUtility3/Meta_Analysis_gui.m, MATLAB, 485 lines
densityUtility3/Meta_Chisq.m, MATLAB, 397 lines
densityUtility3/Meta_Chisq_new.m, MATLAB, 254 lines
densityUtility3/Meta_Logistic.m, MATLAB, 414 lines
densityUtility3/Meta_Logistic_Design.m, MATLAB, 405 lines
densityUtility3/Meta_Multivariate.m, MATLAB, 261 lines
densityUtility3/Meta_Parcel.m, MATLAB, 164 lines
densityUtility3/Meta_Parcel_chisq.m, MATLAB, 68 lines
densityUtility3/Meta_Parcel_results.m, MATLAB, 42 lines
densityUtility3/Meta_SOM.m, MATLAB, 126 lines
densityUtility3/Meta_Select_Contrasts.m, MATLAB, 229 lines
densityUtility3/Meta_Setup.m, MATLAB, 357 lines
densityUtility3/Meta_Specificity.m, MATLAB, 252 lines
densityUtility3/Meta_Specificity_ROI.m, MATLAB, 102 lines
densityUtility3/Meta_Study_Table.m, MATLAB, 174 lines
densityUtility3/Meta_cluster_tools.m, MATLAB, 494 lines
densityUtility3/Meta_parcel_princomp.m, MATLAB, 61 lines
densityUtility3/Meta_plot_points_on_slices.m, MATLAB, 119 lines
densityUtility3/Meta_tasktype_nmds.m, MATLAB, 26 lines
densityUtility3/Support_functions/Meta_Prune.m, MATLAB, 90 lines
densityUtility3/Support_functions/Meta_Task_Indicator.m, MATLAB, 175 lines
densityUtility3/Support_functions/Meta_interactive_table.m, MATLAB, 76 lines
densityUtility3/Support_functions/Meta_interactive_table_vox.m, MATLAB, 363 lines
densityUtility3/Support_functions/SOMResults_view_connections.m, MATLAB, 30 lines
densityUtility3/Support_functions/chi2test.m, MATLAB, 177 lines
densityUtility3/Support_functions/chi2test_massive.m, MATLAB, 199 lines
densityUtility3/Support_functions/compare_rsquare_noprint.m, MATLAB, 23 lines
densityUtility3/Support_functions/dens_fdr_thresh.m, MATLAB, 73 lines
densityUtility3/Support_functions/dist_to_surface.m, MATLAB, 26 lines
densityUtility3/Support_functions/fishers_exact.m, MATLAB, 65 lines
densityUtility3/Support_functions/get_contrast_indicator.m, MATLAB, 299 lines
densityUtility3/Support_functions/get_contrast_indicator_improved.m, MATLAB, 39 lines
densityUtility3/Support_functions/mask2density.m, MATLAB, 190 lines
densityUtility3/Support_functions/meta_SOMclusters.m, MATLAB, 813 lines
densityUtility3/Support_functions/meta_analyze_data.m, MATLAB, 226 lines
densityUtility3/Support_functions/meta_ascendancy.m, MATLAB, 173 lines
densityUtility3/Support_functions/meta_check_contrasts.m, MATLAB, 18 lines
densityUtility3/Support_functions/meta_chi2_matrix.m, MATLAB, 87 lines
densityUtility3/Support_functions/meta_count_contrasts.m, MATLAB, 34 lines
densityUtility3/Support_functions/meta_explore_field.m, MATLAB, 22 lines
densityUtility3/Support_functions/meta_get_contrast_numbers.m, MATLAB, 56 lines
densityUtility3/Support_functions/meta_simulate_nonparamchi2.m, MATLAB, 94 lines
densityUtility3/Support_functions/meta_study2contrast.m, MATLAB, 15 lines
densityUtility3/Support_functions/nonparam_chi2.m, MATLAB, 36 lines
densityUtility3/Support_functions/nonparam_specificity.m, MATLAB, 53 lines
densityUtility3/Support_functions/p0_monte_carlo.m, MATLAB, 59 lines
densityUtility3/Support_functions/p0_monte_carlo_FWE.m, MATLAB, 61 lines
densityUtility3/Support_functions/string2indicator.m, MATLAB, 48 lines
densityUtility3/Support_functions/weighted_sum_image.m, MATLAB, 53 lines
densityUtility3/Support_functions/xyz2density.m, MATLAB, 116 lines
densityUtility3/Support_functions_density/meta_apply_contrast.m, MATLAB, 54 lines
densityUtility3/Support_functions_density/meta_enter_contrasts.m, MATLAB, 36 lines
densityUtility3/Support_functions_density/meta_fast_sphere_conv.m, MATLAB, 22 lines
densityUtility3/Support_functions_density/meta_get_voxel_coords.m, MATLAB, 36 lines
densityUtility3/Support_functions_density/meta_prob_activation.m, MATLAB, 202 lines
densityUtility3/Support_functions_density/meta_read_image.m, MATLAB, 19 lines
densityUtility3/Support_functions_density/meta_read_mask.m, MATLAB, 25 lines
densityUtility3/Support_functions_density/meta_reconstruct_mask.m, MATLAB, 55 lines
densityUtility3/Support_functions_density/meta_stochastic_activation.m, MATLAB, 83 lines
densityUtility3/Support_functions_density/meta_stochastic_activation_blobs.m, MATLAB, 141 lines, 1 match
densityUtility3/Support_functions_density/meta_stochastic_mask.m, MATLAB, 17 lines
densityUtility3/Support_functions_density/meta_stochastic_mask_blobs.m, MATLAB, 79 lines
densityUtility3/plotting_functions/Meta_interactive_point_slice_plot.m, MATLAB, 114 lines
densityUtility3/plotting_functions/Meta_results_batch_script.m, MATLAB, 337 lines
densityUtility3/plotting_functions/addspheres.m, MATLAB, 205 lines
densityUtility3/plotting_functions/bar_interactive.m, MATLAB, 103 lines
densityUtility3/plotting_functions/bar_interactive_btnupfcn.m, MATLAB, 101 lines
densityUtility3/plotting_functions/cluster_barplot2.m, MATLAB, 117 lines
densityUtility3/plotting_functions/meta_plot_study_coords.m, MATLAB, 120 lines
densityUtility3/plotting_functions/plot_points_near_regions_on_surface.m, MATLAB, 53 lines
densityUtility3/plotting_functions/plot_points_on_brain.m, MATLAB, 296 lines
densityUtility3/plotting_functions/plot_points_on_medial_surface.m, MATLAB, 356 lines
densityUtility3/plotting_functions/plot_points_on_montage.m, MATLAB, 336 lines
densityUtility3/plotting_functions/plot_points_on_slice.m, MATLAB, 425 lines
densityUtility3/plotting_functions/plot_points_on_subcortex.m, MATLAB, 201 lines
densityUtility3/plotting_functions/plot_points_on_surface2.m, MATLAB, 450 lines
densityUtility3/pmap_threshold.m, MATLAB, 89 lines
documentation_and_help/mkda_example_setup_and_run.m, MATLAB, 54 lines
plotting_functions/average_nearby_xyz.m, MATLAB, 62 lines
plotting_functions/meta_add_spheres_in_rois.m, MATLAB, 130 lines
plotting_functions/plot_points_on_surface.m, MATLAB, 81 lines
plotting_functions/pt_given_a_plot.m, MATLAB, 124 lines
publish_meta_analysis_report.m, MATLAB, 90 lines
studyplotUtility/chi2_tests/binomcdf.m, MATLAB, 81 lines
studyplotUtility/chi2_tests/binompdf.m, MATLAB, 37 lines
studyplotUtility/chi2_tests/ch2prdf.m, MATLAB, 16 lines
studyplotUtility/chi2_tests/chsqcudf.m, MATLAB, 36 lines
studyplotUtility/chi2_tests/computechi2.m, MATLAB, 68 lines
studyplotUtility/chi2_tests/computeynchi2.m, MATLAB, 73 lines
studyplotUtility/chi2_tests/gammcdf.m, MATLAB, 56 lines
studyplotUtility/chi2_tests/gammpdf.m, MATLAB, 40 lines
studyplotUtility/chi2_tests/simplechi2.m, MATLAB, 62 lines
studyplotUtility/contingency_table.m, MATLAB, 126 lines
studyplotUtility/countemstudies.m, MATLAB, 95 lines
studyplotUtility/plot_all_surface.m, MATLAB, 178 lines
studyplotUtility/plotbarcounts.m, MATLAB, 124 lines
studyplotUtility/read_talairach_daemon.m, MATLAB, 70 lines
studyplotUtility/study_table.m, MATLAB, 93 lines
studyplotUtility/study_table2.m, MATLAB, 76 lines
studyplotUtility/studyplot.m, MATLAB, 1,217 lines
studyplotUtility/studyplot_surface_plot_support/brainsurface.m, MATLAB, 18 lines
studyplotUtility/studyplot_surface_plot_support/distancetosurface.m, MATLAB, 31 lines
studyplotUtility/studyplot_surface_plot_support/getblankbrain.m, MATLAB, 87 lines
studyplotUtility/studyplot_surface_plot_support/nearestVertex.m, MATLAB, 77 lines
studyplotUtility/studyplot_surface_plot_support/plotvert.m, MATLAB, 32 lines
LICENSE, License, 339 lines

At the source: github.com/canlab/Canlab_MKDA_MetaAnalysis

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
202 scripts, each with its path and the digest of its content;
2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 5 keywords, 62 references.

Cite

This paper

Hosseini-Kamkar, N., Kamkar, N., Kalahasthi, R., Ghorayeb, J., Razaan, A., Nair, A., Nicholson, A. A., & Maswood, M. (2026). Gray matter volume correlates of resilience: a coordinate-based meta-analysis. Frontiers in psychology, 17, 1691912. https://doi.org/10.3389/fpsyg.2026.1691912

BibTeX

@article{hosseinikamkar2026gray, author = {Hosseini-Kamkar, Niki and Kamkar, Nellie and Kalahasthi, Rupa and Ghorayeb, Jihane and Razaan, Aamna and Nair, Annapurna and Nicholson, Andrew A. and Maswood, Maaham}, title = {{Gray matter volume correlates of resilience: a coordinate-based meta-analysis}}, journal = {Frontiers in psychology}, year = {2026}, month = mar, volume = {17}, pages = {1691912}, publisher = {Frontiers Media SA}, issn = {1664-1078}, doi = {10.3389/fpsyg.2026.1691912}, url = {https://doi.org/10.3389/fpsyg.2026.1691912}, pmid = {41953303}, pmcid = {PMC13053268} }

RIS

TY - JOUR AU - Hosseini-Kamkar, Niki AU - Kamkar, Nellie AU - Kalahasthi, Rupa AU - Ghorayeb, Jihane AU - Razaan, Aamna AU - Nair, Annapurna AU - Nicholson, Andrew A. AU - Maswood, Maaham TI - Gray matter volume correlates of resilience: a coordinate-based meta-analysis T2 - Frontiers in psychology J2 - Front Psychol PY - 2026 DA - 2026/03/24 VL - 17 SP - 1691912 SN - 1664-1078 PB - Frontiers Media SA DO - 10.3389/fpsyg.2026.1691912 UR - https://doi.org/10.3389/fpsyg.2026.1691912 LA - en ER -

CSL-JSON

{ "id": "10.3389/fpsyg.2026.1691912", "type": "article-journal", "title": "Gray matter volume correlates of resilience: a coordinate-based meta-analysis", "container-title": "Frontiers in psychology", "author": [ { "family": "Hosseini-Kamkar", "given": "Niki" }, { "family": "Kamkar", "given": "Nellie" }, { "family": "Kalahasthi", "given": "Rupa" }, { "family": "Ghorayeb", "given": "Jihane" }, { "family": "Razaan", "given": "Aamna" }, { "family": "Nair", "given": "Annapurna" }, { "family": "Nicholson", "given": "Andrew A." }, { "family": "Maswood", "given": "Maaham" } ], "container-title-short": "Front Psychol", "volume": "17", "page": "1691912", "DOI": "10.3389/fpsyg.2026.1691912", "PMID": "41953303", "PMCID": "PMC13053268", "ISSN": "1664-1078", "publisher": "Frontiers Media SA", "URL": "https://doi.org/10.3389/fpsyg.2026.1691912", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 24 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown

<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.3389/fpsyg.2026.1691912.svg)](https://openscicode.org/badge/doi/10.3389/fpsyg.2026.1691912)
<!-- oscr-badge:end -->

reStructuredText

.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.3389/fpsyg.2026.1691912.svg
   :target: https://openscicode.org/badge/doi/10.3389/fpsyg.2026.1691912
   :alt: OSCR

.. oscr-badge:end

HTML

<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.3389/fpsyg.2026.1691912"><img src="https://openscicode.org/badge/doi/10.3389/fpsyg.2026.1691912.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

canlab/Canlab_MKDA_MetaAnalysis on GitHub (add a README there)

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.