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Functional connectivity density alterations in obsessive-compulsive disorder are associated with neurotransmitter and genetic profiles.

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  1. [1] § Methods and materials › Voxel-wise FCD analyses ↔ JuSpace_v2/compute_DomainGauges.m, lines 1–59 · score 0.55 · Pearson correlation coefficient, SPM, linear, Atlas, maps

Paper

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The authors' code

MATLAB · 315 lines · 11 KB · no license · 1 match

  1. function [Results] = compute_DomainGauges(list1,list2,files_PET,atlas, options,image_save)
  2. % [res,p_all,stats,data, D1,D2,data_PET] = compute_DomainGauges(list1,list2,files_PET,atlas, options, image_save)
  3. % Inputs:
  4. % list1, list2, files_PET are cellarrays of strings containing the filepath
  5. % atlas cellstr of filepath to the atlas to use for DomainGauges, i.e.
  6. % /atlas/m_labels_Neuromorphometrics.nii
  7. % options: a numeric array, i.e. [1 1]
  8. % first index indicates the computing option
  9. % option(1) = 1 --> es between
  10. % option(1) = 2 --> es within
  11. % option(1) = 3 --> mean list 1
  12. % option(1) = 4 --> list 1 each
  13. % option(1) = 5 --> ind z-score list 1 to list 2
  14. % option(1) = 6 --> pair-wise difference list 1 to list 2
  15. % options(1) = 7 --> leave one out from list 1
  16. % options(1) = 8 --> list 1 each compares against null distribution of
  17. % correlation coefficients
  18. % second index indicates the analysis option
  19. % option(2) = 1 --> % Spearman correlation
  20. % option(2) = 2 --> % Pearson correlation
  21. % option(2) = 3 --> % multiple linear regresion
  22. % if option(1) < 4 --> image_save: filepath (char) to save the produced image
  23. % Outputs:
  24. % [res,p_all,stats,data, D1,D2,data_PET]
  25. % res --> Fisher's z transformed correlation / multiple linear regression coefficient matrix, rows correspond to
  26. % input files, columns to PET maps
  27. % p_all --> p-values from one-sample t-test testing if the Fisher's z
  28. % transformed correlations /regression coefficients are different from zero
  29. % stats --> Summary statistics
  30. % stats.CorrOrig --> not Fisher's z transformed individual correlation
  31. % coefficients
  32. % stats.p_ind --> p-values for correlations (CorrOrig) of individual files with PET
  33. % maps
  34. % stats.res_ind --> Individual Fisher's z-transformed correlation
  35. % coefficients / regression coefficients
  36. % stats.ci95 --> 95% confidence interval of the one sample t-test for
  37. % the group test if the correlation coefficient distribution is different
  38. % from 0 (see also the p_all output)
  39. % D1 --> data matrix from list 1
  40. % D2 --> data matrix for list 2 (if not empty)
  41. % data_PET --> PET data matrix
  42. % Resh --> Reshaped results matrix [file_index PET_map Correlation_result p-value file_path]
  45. atlas_hdr = spm_vol(atlas);
  46. atlas1 = spm_read_vols(atlas_hdr);
  47. [a,b,c] = unique(atlas1(:));
  48. a = a(a~=0);
  49. atlas_vals = a(~isnan(a));
  51. D1 = mean_time_course(list1,atlas, atlas_vals);
  53. if ~isemptycell(list2)
  54. D2 = mean_time_course(list2,atlas, atlas_vals);
  55. else
  56. D2 = [];
  57. end
  59. data_PET = mean_time_course(files_PET,atlas,atlas_vals, 1);
  61. if options(4)==1 % adjust for structural correlation
  62. if isdeployed
  63. [~, ~] = system('path');
  64. path_base = pwd;
  65. path_T1 = fullfile(path_base,'mask','TPM.nii,1');
  66. else
  67. path_T1 = fullfile(fileparts(which('JuSpace')),'mask','TPM.nii,1');
  68. end
  69. T1 = mean_time_course({path_T1},atlas, atlas_vals);
  70. else
  71. T1 = '';
  72. end
  74. switch options(1)
  75. % opt_comp = 1 --> es between
  76. % opt_comp = 2 --> es within
  77. % opt_comp = 3 --> mean list 1
  78. % opt_comp = 4 --> list 1 each
  79. % opt_comp = 5 --> ind z-score list 1 to list 2
  80. % opt_comp = 6 --> pair-wise difference list 1 to list 2
  81. % opt_comp = 7 --> ind z-scores from list 1
  82. % opt_comp = 8 --> list 1 each compares against null distribution of
  83. % correlation coefficients
  84. case 1 % Cohen's d between groups
  85. m_D1 = mean(D1);
  86. std_D1 = std(D1);
  87. m_D2 = mean(D2);
  88. std_D2 = std(D2);
  89. data = (m_D1-m_D2)./sqrt((std_D1.^2+std_D2.^2)./2);
  90. case 2 % Cohen's d within group change
  91. delta_d = D1-D2;
  92. data = mean(delta_d)./std(delta_d);
  93. case 3 % mean list 1
  94. if size(D1,1)==1
  95. data = D1;
  96. else
  97. data = mean(D1);
  98. end
  99. case 4 % list 1 with PET data
  100. data = D1;
  101. case 5 % compute z-score list 1 relative to list 2
  102. m_D2 = mean(D2);
  103. std_D2 = std(D2);
  104. data = (D1 - repmat(m_D2,size(D1,1),1))./repmat(std_D2,size(D1,1),1);
  105. case 6 % pair-wise differences list 1 - list 2
  106. data = D1 - D2;
  107. case 7 % leave one out
  108. for i = 1:size(D1,1)
  109. data_i = D1(i,:);
  110. data_z = D1([1:i-1 i+1:end],:);
  111. data(i,:) = (data_i-mean(data_z))./std(data_z);
  112. end
  113. case 8
  114. data = D1;
  115. end
  117. % if options(2) == 3 % 1 = Spearman, 2 = Pearson, 3 = multiple linear regression
  118. % [res,p_all,stats] = correlateModalities(data,data_PET, options(2));
  119. % else
  120. if options(4)==1
  121. [res,stats] = correlateModalities(data,data_PET,options,T1);
  122. else
  123. [res,stats] = correlateModalities(data,data_PET,options);
  124. end
  125. % end
  126. if options(2) < 3
  127. [Resh] = reshape_res(options,res, files_PET,list1, stats.corrOrig);
  128. else
  129. [Resh] = reshape_res(options,res, files_PET,list1);
  130. end
  131. a = 1;
  132. if options(1)<4
  133. Y = zeros(size(atlas1));
  134. for i = 1:length(atlas_vals)
  135. Y(atlas1(:)==atlas_vals(i)) = data(i);
  136. end
  137. atlas_hdr.fname = image_save;
  138. atlas_hdr.pinfo(1) = 0.001;
  139. atlas_hdr.dt = [16 0];
  140. spm_write_vol(atlas_hdr,Y);
  141. end
  144. Results.res = res;
  145. Results.stats = stats;
  146. Results.data = data;
  147. Results.data_set1 = D1;
  148. Results.data_set2 = D2;
  149. Results.data_PET = data_PET;
  150. Results.T1 = T1;
  151. Results.Resh = Resh;
  153. end
  155. function [res,stats] = correlateModalities(data,data_PET,opts,T1)
  158. for i = 1:size(data,1)
  159. switch opts(2)
  160. case 1 % Spearman correlation
  161. for j = 1:size(data_PET,1)
  162. if opts(4)==1
  163. data_ij = removenan_my([data',data_PET',T1']);
  164. [r] = partialcorr(data_ij(:,1:size(data',2)),data_ij(:,size(data',2)+1:size(data',2)+size(data_PET',2)),data_ij(:,size(data',2)+size(data_PET',2)+1:end),'type','Spearman');
  165. else
  166. data_ij = removenan_my([data',data_PET']);
  167. [r] = corr(data_ij(:,1:size(data',2)),data_ij(:,size(data',2)+1:size(data',2)+size(data_PET',2)),'type','Spearman');
  168. end
  169. corrOrig = r;
  171. end
  172. res_ind = fishers_r_to_z(corrOrig);
  173. stats.corrOrig = corrOrig;
  174. stats.res_ind = res_ind;
  177. case 2 % Pearson correlation
  178. for j = 1:size(data_PET,1)
  179. if opts(4)==1
  180. data_ij = removenan_my([data',data_PET',T1']);
  181. [r,p] = partialcorr(data_ij(:,1:size(data',2)),data_ij(:,size(data',2)+1:size(data',2)+size(data_PET',2)),data_ij(:,size(data',2)+size(data_PET',2)+1:end),'type','Pearson');
  182. else
  183. data_ij = removenan_my([data',data_PET']);
  184. [r,p] = corr(data_ij(:,1:size(data',2)),data_ij(:,size(data',2)+1:size(data',2)+size(data_PET',2)),'type','Pearson');
  185. end
  186. corrOrig = r;
  187. p_ind = p;
  188. end
  189. res_ind = fishers_r_to_z(corrOrig);
  190. stats.corrOrig = corrOrig;
  191. stats.p_ind = p_ind;
  192. stats.res_ind = res_ind;
  194. case 3 % multiple linear regresion
  196. y = data(i,:)';
  197. if opts(4)==1
  198. X = [data_PET' T1'];
  199. ind_PET = 2:size(X,2);
  200. else
  201. X = [data_PET'];
  202. ind_PET = 2:size(X,2)+1;
  203. end
  205. dd = removenan_my([y X]);
  206. y = dd(:,1);
  207. y = zscore(y);
  209. X = dd(:,2:end);
  210. X = zscore(X);
  212. % X = zscore(X); % zscore_update
  213. Stats = regstats(y,X);
  214. res_ind(i,:) = Stats.beta(ind_PET)';
  215. stats.res_ind(i,:) = Stats.beta(ind_PET)';
  216. stats.ind(i).p_ftest_full = Stats.fstat.pval;
  217. stats.ind(i).f_ftest_full = Stats.fstat.f;
  218. stats.ind(i).t_all = Stats.tstat.t;
  219. stats.p_ind(i,:) = Stats.tstat.pval(ind_PET)';
  220. stats.rsquare(i,1) = Stats.rsquare;
  221. end
  222. end
  224. % opt_comp = 1 --> es between
  225. % opt_comp = 2 --> es within
  226. % opt_comp = 3 --> mean list 1
  227. % opt_comp = 4 --> list 1 each
  228. % opt_comp = 5 --> ind z-score list 1 to list 2
  229. % opt_comp = 6 --> pair-wise difference list 1 to list 2
  230. % opt_comp = 7 --> ind z-scores from list 1
  231. % opt_comp = 8 --> list 1 each compares against null distribution of
  232. % correlation coefficients
  233. switch opts(1)
  234. case {1,2,3,4}
  235. res = res_ind;
  236. case {5,6,7,8}
  237. if size(res_ind,1)>1
  238. res = mean(res_ind);
  239. [h,p_all,ci] = ttest(res_ind);
  240. stats.ci95 = ci;
  241. end
  242. end
  244. end
  246. function [Resh] = reshape_res(options,res, files_PET,list1, corrOrig)
  249. for i = 1:length(files_PET);
  250. [path,name] = fileparts(files_PET{i});
  251. tt = regexp(name,'_','Split');
  252. Rec_list{i}=tt{1};
  253. end
  254. %
  255. % switch options(1)
  256. % case 1
  257. % ff = 'Effect Size';
  258. % case 2
  259. % ff = 'Effect Size (within subject)';
  260. % case 3
  261. % ff = 'Mean list';
  262. % case 4
  263. % ff = 'List 1 image';
  264. % case 5
  265. % ff = 'Z-score file';
  266. % case 6
  267. % ff = 'Delta';
  268. % case 7
  269. % ff = 'Z-score loo';
  270. % case 8
  271. % ff = 'List 1 all against null';
  272. %
  273. % end
  275. switch options(2)
  276. case 1
  277. if options(1)<4
  278. % Resh = {ff 'PET Map' 'Fisher''s z (Spearman rho)' 'p-val (parametric)' 'File'};
  279. Resh = {'PET Map' 'Fisher''s z ' 'Spearman rho' 'File Modality' 'PET or cell map'};
  280. else
  281. Resh = { 'PET Map' 'Mean Fisher''s z' 'Median Spearman rho' 'File' 'PET or cell map'};
  282. end
  283. case 2
  284. if options(1)<4
  285. Resh = {'PET Map' 'Fisher''s z' 'Pearson r' 'File' 'PET or cell map'};
  286. else
  287. Resh = {'PET Map' 'Mean Fisher''s z' 'Median Pearson r)' 'File' 'PET or cell map'};
  288. end
  289. otherwise
  290. Resh = {'PET Map' 'Standardized Beta' 'File' 'PET or cell map'};
  291. end
  293. if exist('corrOrig','var')
  294. if size(corrOrig,1)>1 && size(res,1)==1
  295. corrOrig = median(corrOrig);
  296. end
  297. end
  299. for i = 1:size(res,1)
  300. for j = 1:size(res,2)
  301. if options(2)<3
  302. Resh{end+1,1} = Rec_list{j};
  303. Resh{end,2} = res(i,j);
  304. Resh{end,3} = corrOrig(i,j);
  305. Resh{end,4} = list1{i};
  306. Resh{end,5} = files_PET{j};
  307. else
  308. Resh{end+1,1} = Rec_list{j};
  309. Resh{end,2} = res(i,j);
  310. Resh{end,3} = list1{i};
  311. Resh{end,4} = files_PET{j};
  312. end
  313. end
  314. end
  315. end

compute_DomainGauges.m at commit 99c08a8, no license · at the source

Overview

Authors: Ke Chen1,2,3, Yueling Liu1,2,3, Yian Xiao1,2,3, Fuyao Cao1,2,3, Siya Zhu1,2,3, Woxin Pan1,2,3, Weihang Weng1,2,3, Yinchao Hong1,2,3, Qiang Hua2,3,4, Ke Wan1,2,3, Jiarui Ye1,2,3, Zhisen Li1,2,3, Shuoqi Xiang1,2,3, Fengqiong Yu1,2,3, Kai Wang1,2,3,4, Gongjun Ji1,2,3, Chunyan Zhu1,2,3,5
ORCID iDs: Chunyan Zhu
  1. School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China
  2. Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, China
  3. Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
  4. Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
  5. Department of Psychology and Sleep Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
Institutions: Anhui Medical University (China); First Affiliated Hospital of Anhui Medical University (China); Second Affiliated Hospital of Anhui Medical University (China)
Journal: Translational psychiatry, volume 16, issue 1, article 332
Dates: received 8 December 2025; accepted 30 April 2026; published online 12 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-04073-8 · PMID 42120356 · PMCID PMC13338170 · OpenAlex W7160921916
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Connectivity, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: Neuroscience, Genetics
MeSH: Brain*, Nerve Net*, Neurotransmitter Agents*, Obsessive-Compulsive Disorder*, Adult, Brain Mapping, Case-Control Studies, Female, Humans, Magnetic Resonance Imaging, Male, Support Vector Machine, Transcriptome, Young Adult (* major topic)
Topic: Obsessive-Compulsive Spectrum Disorders (Clinical Psychology, Psychology), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (32271134, 82371507, 81971689); Ministry of Education of the People's Republic of China (Ministry of Education of China) (25YJC190024)
Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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juryxy/juspace

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 99c08a889b292d1f28a89aa12df25663243f16c8, 8 June 2026
Languages: MATLAB (19), Shell (1)
Size: 182 files, 20 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (6 files), Statistics and Machine Learning Toolbox (4 files), fdr_bh (Benjamini-Hochberg FDR) (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
21 files
At the source: github.com/juryxy/JuSpace/tree/JuSpace_v1.5/JuSpace_v1.5/PETatlas

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

Tracing map

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Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41398-026-04073-8.

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, issue, pages, dates, 17 authors, 2 keywords, 14 MeSH terms, 2 funders, 79 references.

Cite

This paper

Chen, K., Liu, Y., Xiao, Y., Cao, F., Zhu, S., Pan, W., Weng, W., Hong, Y., Hua, Q., Wan, K., Ye, J., Li, Z., Xiang, S., Yu, F., Wang, K., Ji, G., & Zhu, C. (2026). Functional connectivity density alterations in obsessive-compulsive disorder are associated with neurotransmitter and genetic profiles. Translational psychiatry, 16(1), 332. https://doi.org/10.1038/s41398-026-04073-8

BibTeX

@article{chen2026functional,
author = {Chen, Ke and Liu, Yueling and Xiao, Yian and Cao, Fuyao and Zhu, Siya and Pan, Woxin and Weng, Weihang and Hong, Yinchao and Hua, Qiang and Wan, Ke and Ye, Jiarui and Li, Zhisen and Xiang, Shuoqi and Yu, Fengqiong and Wang, Kai and Ji, Gongjun and Zhu, Chunyan},
title = {{Functional connectivity density alterations in obsessive-compulsive disorder are associated with neurotransmitter and genetic profiles}},
journal = {Translational psychiatry},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {332},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04073-8},
url = {https://doi.org/10.1038/s41398-026-04073-8},
pmid = {42120356},
pmcid = {PMC13338170}
}

RIS

TY - JOUR
AU - Chen, Ke
AU - Liu, Yueling
AU - Xiao, Yian
AU - Cao, Fuyao
AU - Zhu, Siya
AU - Pan, Woxin
AU - Weng, Weihang
AU - Hong, Yinchao
AU - Hua, Qiang
AU - Wan, Ke
AU - Ye, Jiarui
AU - Li, Zhisen
AU - Xiang, Shuoqi
AU - Yu, Fengqiong
AU - Wang, Kai
AU - Ji, Gongjun
AU - Zhu, Chunyan
TI - Functional connectivity density alterations in obsessive-compulsive disorder are associated with neurotransmitter and genetic profiles
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/05/12
VL - 16
IS - 1
SP - 332
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04073-8
UR - https://doi.org/10.1038/s41398-026-04073-8
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Functional connectivity density alterations in obsessive-compulsive disorder are associated with neurotransmitter and genetic profiles",
"container-title": "Translational psychiatry",
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"family": "Chen",
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"family": "Hua",
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"given": "Ke"
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"PMID": "42120356",
"PMCID": "PMC13338170",
"ISSN": "2158-3188",
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"issued": {
"date-parts": [
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