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Brain network representations of placebo analgesia.

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1 match 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.

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  1. [1] § Materials and methods › Functional connectivity network mapping ↔ Code/FCNM.m, lines 43–168 · score 0.58 · FC maps, correlation, spheres, mask, radius, thresholded

Paper

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The authors' code

MATLAB · 221 lines · 9.4 KB · no license · 1 match

  1. %%% ---------------------------------------------------------------------------------------------------
  2. %%% Functional Connectivity Network Mapping (FCNM)
  3. %%% ---------------------------------------------------------------------------------------------------
  4. %%% This script manages data from Excel to combined masks, individual-level zFC,
  5. %%% group-level T-maps, and final group probability maps.
  6. %%% Developed by ChatGPT and Mofan, Anhui Medical University, December 28, 2022.
  7. %%% ---------------------------------------------------------------------------------------------------
  8. %%% Steps:
  9. %%% 1. Load and set paths.
  10. %%% 2. Establish directory structure and capture PDF file names for studies.
  11. %%% 3. Prepare Excel spreadsheets for later ROI generation.
  12. %%% 4. Generate ROI spheres, process BOLD files, and perform statistical analyses.
  13. %%% 5. Create and refine group probability maps.
  14. %%% ---------------------------------------------------------------------------------------------------
  16. %% Step 1: Add spm12 and ROI_ball_gen_combined.m to the path and clear all variables
  18. clear, clc
  19. support_path = 'F:\test\extra'; % Path for support files
  20. addpath(support_path);
  22. %% Step 2: Read the basic information and title of each independent study
  23. % Typically, we have downloaded all the PDFs of included studies, recommended to be named as "P + Number + Author + Year of Publication", e.g., P01Li2017
  24. % If not all PDFs are downloaded, create a placeholder PDF for missing studies as we only need to read each document's basic information and title
  26. path = 'F:\test';
  27. mkdir([path,'\Articles_Included']); % Create a new folder 'Articles_Included' to store all included PDF files of the studies
  28. Articles_path = [path, '\Articles_Included'];
  29. File = dir(fullfile(Articles_path, '*.pdf')); % Read all PDF files in 'Articles_Included', representing all studies. The file name without '.pdf' is obtained by (end-4)
  30. Filename = {File.name};
  33. %% Step 3: After reading the information, create an Excel spreadsheet for all articles' coordinates, which will be manually filled in later
  35. mkdir([path, filesep, 'Articles_Included_Excel']); % Create a new folder 'Articles_Included_Excel'
  36. cd([path, filesep, 'Articles_Included_Excel']); % Enter this folder
  38. for i = 1:length(Filename) % Create an Excel spreadsheet for each study, where coordinates will be manually entered into the spreadsheet later and saved. If there are multiple ROI results, the spreadsheet can be autofilled downwards
  39. filename = char(Filename(i));
  40. xlswrite([filename(1:end-4), '.xlsx'], cellstr([filename(1:end-4), '_ROI01']), 'sheet1', 'D1')
  41. end
  43. %% Step 4: Generate spheres for each study's ROI and merge spheres from the same study
  45. radius_Seeds_results = [path, '\4mm_Seeds_Results\']; % Note to adjust the radius size when generating spheres with different radii, here it is 4mm
  47. parfor i = 1:length(Filename)
  48. filename = char(Filename(i));
  49. article_folder = fullfile(radius_Seeds_results, filename(1:end-4));
  50. mkdir(article_folder)
  52. ROI_Seeds_path = fullfile(article_folder, 'ROI_Seeds');
  53. if ~exist(ROI_Seeds_path, 'dir')
  54. mkdir(ROI_Seeds_path)
  55. end
  57. onesample_path = fullfile(article_folder, ['onesample_', filename(1:end-4)]);
  58. if ~exist(onesample_path, 'dir')
  59. mkdir(onesample_path)
  60. end
  62. zFC_path = fullfile(article_folder, ['zFC_', filename(1:end-4)]);
  63. if ~exist(zFC_path, 'dir')
  64. mkdir(zFC_path)
  65. end
  67. % Proceed to generate and combine ROI spheres
  68. [coord, ROIs] = xlsread([path, filesep, 'Articles_Included_Excel', filesep, filename(1:end-4), '.xlsx']);
  69. output_path = ROI_Seeds_path;
  70. combined_mask = ROI_ball_gen_combined(coord, ROIs, [support_path, filesep, 'BNA_mask_3m.nii'], 4, output_path);
  73. % Loop through each participant's directory to process BOLD files
  74. Data_dir = 'F:\test\data\AMUD'; % Specify the path where the 4D NIfTI time series data are stored
  75. participant_folders = dir(fullfile(Data_dir, '*'));
  76. participant_folders = participant_folders(~ismember({participant_folders.name}, {'.', '..'}));
  78. Mask_BNA = spm_read_vols(spm_vol(fullfile(support_path, 'BNA_mask_3m.nii')));
  79. Mask_Brain = spm_read_vols(spm_vol(fullfile(support_path, 'BrainMask_05_61x73x61.img')));
  81. for p = 1:length(participant_folders)
  83. boldFiles = dir(fullfile(Data_dir, participant_folders(p).name, '*.nii'));
  84. boldFile = fullfile(boldFiles.folder, boldFiles.name);
  85. bold_vol = spm_vol(boldFile);
  86. boldData = spm_read_vols(bold_vol);
  87. boldData = boldData .* Mask_Brain;
  88. boldData2D = reshape(boldData, [], size(boldData, 4));
  91. roiTimeSeries = mean(boldData2D(combined_mask > 0, :), 1);
  92. maskIndex = Mask_Brain > 0;
  93. boldDataMasked = boldData2D(maskIndex(:), :);
  95. fcMap = corr(roiTimeSeries', boldDataMasked');
  96. zfcMap = zeros(size(boldData, 1), size(boldData, 2), size(boldData, 3));
  97. zfcMap(maskIndex) = atanh(fcMap);
  100. zFC_filename = fullfile(zFC_path, ['zFC_', participant_folders(p).name, '.nii']);
  101. zFC_vol = bold_vol(1);
  102. zFC_vol.fname = zFC_filename;
  103. spm_write_vol(zFC_vol, zfcMap);
  105. end
  107. zFC_files = spm_select('FPList', zFC_path, '^zFC_.*\.nii$');
  109. if isempty(zFC_files)
  110. warning('No zFC files found in %s.', zFC_path);
  111. continue;
  112. end
  114. % Setup the statistical design for one-sample t-tests in SPM
  115. matlabbatch = [];
  116. matlabbatch{1}.spm.stats.factorial_design.dir = {onesample_path};
  117. matlabbatch{1}.spm.stats.factorial_design.des.t1.scans = cellstr(zFC_files);
  118. matlabbatch{1}.spm.stats.factorial_design.cov = struct('c', {}, 'cname', {}, 'iCFI', {}, 'iCC', {});
  119. matlabbatch{1}.spm.stats.factorial_design.masking.tm.tm_none = 1;
  120. matlabbatch{1}.spm.stats.factorial_design.masking.im = 1;
  121. matlabbatch{1}.spm.stats.factorial_design.masking.em = {fullfile(support_path, 'BNA_mask_3m.nii,1')};
  122. matlabbatch{1}.spm.stats.factorial_design.globalc.g_omit = 1;
  123. matlabbatch{1}.spm.stats.factorial_design.globalm.gmsca.gmsca_no = 1;
  124. matlabbatch{1}.spm.stats.factorial_design.globalm.glonorm = 1;
  126. % Model estimation
  127. matlabbatch{2}.spm.stats.fmri_est.spmmat = {fullfile(onesample_path, 'SPM.mat')};
  129. % Contrast specification
  130. matlabbatch{3}.spm.stats.con.spmmat = {fullfile(onesample_path, 'SPM.mat')};
  131. matlabbatch{3}.spm.stats.con.consess{1}.tcon.name = 'One Sample T-test';
  132. matlabbatch{3}.spm.stats.con.consess{1}.tcon.weights = 1;
  133. matlabbatch{3}.spm.stats.con.consess{1}.tcon.sessrep = 'none';
  134. matlabbatch{3}.spm.stats.con.delete = 0;
  136. % Run the batch job
  137. spm_jobman('run', matlabbatch);
  140. % Load SPM T-map file
  141. V = spm_vol(fullfile(onesample_path, 'spmT_0001.nii'));
  142. [X, XYZ] = spm_read_vols(V);
  144. % Compute p-values for positive T statistics
  145. P_uncorrected = 1 - spm_Tcdf(X(X > 0), 655); % Adjust the second parameter based on your sample size
  146. P_sorted = sort(P_uncorrected);
  147. P_corrected = spm_P_FDR(P_sorted);
  149. % Reorder corrected P-values
  150. [~, sort_idx] = sort(P_uncorrected, 'ascend');
  151. [~, unsort_idx] = sort(sort_idx, 'ascend');
  152. P_corrected = P_corrected(unsort_idx);
  154. % Apply FDR threshold
  155. thresholded_map = zeros(size(X));
  156. positive_voxel_indices = find(X > 0);
  157. thresholded_map(positive_voxel_indices(P_corrected < 0.05)) = X(positive_voxel_indices(P_corrected < 0.05));
  159. % Save the corrected T-map
  160. V.fname = fullfile(onesample_path, 'spmT_0001_positive_FDR.nii');
  161. spm_write_vol(V, thresholded_map);
  163. % Create a new mask image for voxels above threshold
  164. mask = thresholded_map > 0;
  165. V.fname = fullfile(onesample_path, 'spmT_0001_positive_FDR_mask.nii');
  166. spm_write_vol(V, mask);
  168. end
  170. %% Step 5: Create and refine group probability maps.
  172. output_folder_avg = fullfile(radius_Seeds_results, 'Average_probability_map');
  173. mkdir(output_folder_avg);
  175. % Initialize the accumulation matrix
  176. sum_matrix = [];
  178. % Retrieve paths to all 'onesample_*' subfolders
  179. onesample_folders = dir(fullfile(radius_Seeds_results, '*', 'onesample_*'));
  180. num_folders = length(onesample_folders);
  182. % Iterate through each 'onesample_*' folder and accumulate the contents of each mask file
  183. for i = 1:num_folders
  184. parent_folder_name = onesample_folders(i).folder;
  185. onesample_folder_name = onesample_folders(i).name;
  186. mask_file = dir(fullfile(parent_folder_name, onesample_folder_name, '*_positive_FDR_mask.nii'));
  188. if ~isempty(mask_file)
  189. file_path = fullfile(parent_folder_name, onesample_folder_name, mask_file.name);
  190. finalVol = spm_vol(file_path);
  191. [Y, ~] = spm_read_vols(finalVol);
  193. if isempty(sum_matrix)
  194. sum_matrix = zeros(size(Y));
  195. end
  197. sum_matrix = sum_matrix + Y;
  198. end
  199. end
  201. % Calculate the average
  202. avg_matrix = sum_matrix / num_folders;
  204. % Retain values greater than or equal to 0.6
  205. avg_matrix(avg_matrix < 0.6) = 0;
  207. % Remove clusters smaller than 30 voxels
  208. % Use the bwlabeln function to label connected regions
  209. [L, num] = bwlabeln(avg_matrix, 6);
  211. % Iterate through each connected region and remove those smaller than 30 voxels
  212. for j = 1:num
  213. if sum(L(:) == j) < 30
  214. avg_matrix(L == j) = 0;
  215. end
  216. end
  218. % Save the final averaged matrix to a new nii file
  219. final_mask_filename = fullfile(output_folder_avg, 'average_mask_50_30.nii');
  220. finalVol.fname = final_mask_filename;
  221. spm_write_vol(finalVol, avg_matrix);

FCNM.m, no license · at the source

Overview

Authors: Xiaohan Zhang1,2,3, Xuetian Sun1,2,3, Weisheng Huang1,2,3, Kaijie An1,2,3, Xufeng Zhao1,2,3, Dan Zhang1,2,3, Wenwei Zhang1,2,3, Yongqiang Yu1,2,3, Yinfeng Qian1,2,3, Jiajia Zhu1,2,3
ORCID iDs: Jiajia Zhu
  1. Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
  2. Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China
  3. Anhui Provincial Key Laboratory for Brain Bank Construction and Resource Utilization, Hefei, China
Institutions: First Affiliated Hospital of Anhui Medical University (China)
Journal: Psychological medicine, volume 56, article e131
Dates: received 5 November 2025; accepted 8 December 2025; published online 5 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1017/s0033291726103924 · PMID 42083871 · PMCID PMC13161808 · OpenAlex W7160264354
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), pain (population)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging
Keywords: brain network, functional connectivity, neuroimaging, pain, placebo analgesia
MeSH: Analgesia*, Brain*, Nerve Net*, Pain*, Placebo Effect*, Adult, Brain Mapping, Connectome, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)
Topic: Pain Management and Placebo Effect (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82471952); Anhui Provincial Natural Science Foundation (2308085MH277); Scientific Research Key Project of Anhui Province Universities (2022AH051135); Scientific Research Foundation of Anhui Medical University (2022xkj143)
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

Background: Prior neuroimaging studies and meta-analyses investigating brain correlates of placebo analgesia (PA) have yielded neuroanatomically heterogeneous findings, which may be reconciled from a connectomics perspective. The objective of this study was to examine network localization of brain functional alterations related to PA.

Methods: We initially identified PA-induced brain activation alterations (hyper-activation and hypo-activation separately) during experimental pain from 29 published studies with 674 individuals. By combining these implicated dysfunctional brain regions with large-scale discovery (N = 1113) and validation (N = 1093) resting-state functional magnetic resonance imaging datasets, we then employed a novel functional connectivity network mapping approach to construct PA hyper-activation and hypo-activation networks, respectively.

Results: The PA hyper-activation network manifested as a pattern of circumscribed brain regions mainly involving the limbic, default, and frontoparietal networks. By contrast, the PA hypo-activation network comprised a broadly distributed set of brain regions primarily implicating the ventral attention, somatomotor, and subcortical networks.

Conclusions: Our findings regarding the brain network representations of PA may contribute to a deeper understanding of its action mechanisms and provide a neural framework that may inform future clinical translation.

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 1 match between paragraphs and lines of code.

OSF 3vxrj

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (1)
Size: 3 files, 1 script
Software Heritage: not checked
Found in: “Data availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (1 file), SPM (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/3vxrj/

The paper's code and data availability statement is in the Data section.

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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;
  • 1 script, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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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 data and analysis codes used in the preparation of this article are publicly available at https://osf.io/3vxrj/.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 5 keywords, 12 MeSH terms, 4 funders, 99 references.

Cite

This paper

Zhang, X., Sun, X., Huang, W., An, K., Zhao, X., Zhang, D., Zhang, W., Yu, Y., Qian, Y., & Zhu, J. (2026). Brain network representations of placebo analgesia. Psychological medicine, 56, e131. https://doi.org/10.1017/s0033291726103924

BibTeX

@article{zhang2026brain,
author = {Zhang, Xiaohan and Sun, Xuetian and Huang, Weisheng and An, Kaijie and Zhao, Xufeng and Zhang, Dan and Zhang, Wenwei and Yu, Yongqiang and Qian, Yinfeng and Zhu, Jiajia},
title = {{Brain network representations of placebo analgesia}},
journal = {Psychological medicine},
year = {2026},
month = may,
volume = {56},
pages = {e131},
publisher = {Cambridge University Press},
issn = {0033-2917},
doi = {10.1017/s0033291726103924},
url = {https://doi.org/10.1017/s0033291726103924},
pmid = {42083871},
pmcid = {PMC13161808}
}

RIS

TY - JOUR
AU - Zhang, Xiaohan
AU - Sun, Xuetian
AU - Huang, Weisheng
AU - An, Kaijie
AU - Zhao, Xufeng
AU - Zhang, Dan
AU - Zhang, Wenwei
AU - Yu, Yongqiang
AU - Qian, Yinfeng
AU - Zhu, Jiajia
TI - Brain network representations of placebo analgesia
T2 - Psychological medicine
J2 - Psychol Med
PY - 2026
DA - 2026/05/05
VL - 56
SP - e131
SN - 0033-2917
PB - Cambridge University Press
DO - 10.1017/s0033291726103924
UR - https://doi.org/10.1017/s0033291726103924
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Brain network representations of placebo analgesia",
"container-title": "Psychological medicine",
"author": [
{
"family": "Zhang",
"given": "Xiaohan"
},
{
"family": "Sun",
"given": "Xuetian"
},
{
"family": "Huang",
"given": "Weisheng"
},
{
"family": "An",
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{
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"given": "Yinfeng"
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"given": "Jiajia"
}
],
"container-title-short": "Psychol Med",
"volume": "56",
"page": "e131",
"DOI": "10.1017/s0033291726103924",
"PMID": "42083871",
"PMCID": "PMC13161808",
"ISSN": "0033-2917",
"publisher": "Cambridge University Press",
"URL": "https://doi.org/10.1017/s0033291726103924",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
5
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]
}
}

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