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Connectome Gradient-Based Subtyping of Major Depressive Disorder Reveals Distinct Neurobiological and Transcriptomic Signatures.

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. [1] § 2. Experimental Procedures › 2.3. Functional Gradients Calculation ↔ matlab/@GradientMaps/GradientMaps.m, lines 1–50 · score 0.65 · diffusion embedding, BrainSpace, cosine, Procrustes, components, gradients
  2. [2] § 2. Experimental Procedures › 2.8. Correlation Analysis Between Functional Gradient Differences and Cell Types ↔ brainspace/null_models/variogram.py, lines 1523–1543 · score 0.55 · nearest neighbors, distance weighted, Brain, maps

Paper

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The authors' code

MATLAB · 317 lines · 14 KB · BSD-3-Clause · 1 match

  1. classdef GradientMaps
  2. % obj = BrainSpace(varargin)
  3. %
  4. % Class definition and constructor for BrainSpace. The following
  5. % name-value pairs are allowed as input.
  6. %
  7. % - kernel (default: normalized angle)
  8. % - 'p','pearson'
  9. % - 'sm','spearman'
  10. % - 'g','gaussian'
  11. % - 'na','normalized angle'
  12. % - 'cs','cosine similarity'
  13. % - '','none'
  14. % - a function handle
  15. % - approach (default: diffusion embedding)
  16. % - 'dm','diffusion embedding'
  17. % - 'le','laplacian eigenmap'
  18. % - 'pca','principal component analysis'
  19. % - a function handle
  20. % - alignment (default: none)
  21. % - 'none',''
  22. % - 'pa','procrustes analysis'
  23. % - 'ja','joint alignment'
  24. % - a function handle
  25. % - n_components (default: 10)
  26. % - Any natural number.
  27. % - random_state (default: nan)
  28. % - Any valid input for MATLAB's "rng" function or nan for no
  29. % initialization.
  30. % - verbose (default: false)
  31. % - Determines wheter non-warning/error messages will be displayed.
  32. %
  33. % For complete documentation, including descriptions of this object's
  34. % properties and methods please consult our <a
  35. % href="https://brainspace.readthedocs.io/en/latest/pages/matlab_doc/main_functionality/gradientmaps.html">ReadTheDocs</a>.
  36. %
  37. % See also: GRADIENTMAPS.FIT
  39. properties (SetAccess = private)
  40. method
  41. gradients
  42. aligned
  43. lambda
  44. end
  46. properties (Access = private)
  47. random_state
  48. end
  50. methods
  51. %% Constructor
  52. function obj = GradientMaps(varargin)
  53. % Parse input
  54. in_fun = @(x) isa(x,'char') || isa(x,'function_handle');
  55. p = inputParser;
  56. addParameter(p, 'kernel', 'normalized angle', in_fun);
  57. addParameter(p, 'approach', 'diffusion embedding', in_fun);
  58. addParameter(p, 'alignment', 'none', in_fun);
  59. addParameter(p, 'n_components', 10, @isnumeric);
  60. addParameter(p, 'random_state', nan);
  61. addParameter(p, 'verbose', false, @islogical);
  63. parse(p, varargin{:});
  64. R = p.Results;
  66. % Set the properties
  67. if R.verbose
  68. disp('Launching BrainSpace, the gradient connectivity toolbox.');
  69. disp('');
  70. end
  71. obj = obj.set( ...
  72. 'kernel', R.kernel, ...
  73. 'approach', R.approach, ...
  74. 'alignment', R.alignment, ...
  75. 'random_state', R.random_state, ...
  76. 'n_components', R.n_components, ...
  77. 'verbose', R.verbose);
  78. end
  79. end
  80. methods(Access = private)
  82. %% Private methods
  83. function obj = set(obj,varargin)
  84. % obj2 = SET(obj,varargin)
  85. %
  86. % Private function to set the properties of the BrainSpace
  87. % object.
  89. change_string = {};
  90. for ii = 1:2:numel(varargin)
  91. switch lower(varargin{ii})
  92. case 'kernel'
  93. if isa(varargin{ii+1},'function_handle')
  94. obj.method.kernel = varargin{ii+1};
  95. change_string{end+1} = ('Set the kernel to a custom function handle.');
  96. else
  97. switch lower(varargin{ii+1})
  98. case {'none',''}
  99. obj.method.kernel = 'None';
  100. case {'p','pearson'}
  101. obj.method.kernel = 'Pearson';
  102. case {'sm','spearman'}
  103. obj.method.kernel = 'Spearman';
  104. case {'g','gaussian'}
  105. obj.method.kernel = 'Gaussian';
  106. case {'cs','cosine','cosine similarity','cossim','cosine_similarity','cosinesimilarity'}
  107. obj.method.kernel = 'Cosine Similarity';
  108. case {'na','normalized angle','normalizedangle','normangle','normalized_angle'}
  109. obj.method.kernel = 'Normalized Angle';
  110. otherwise
  111. error('Unknown kernel. Valid kernels are: ''none'', ''pearson'', ''spearman'', ''Gaussian'', ''cosine similarity'', and ''normalized angle''');
  112. end
  113. change_string{end+1} = (['Set the kernel to: ' obj.method.kernel '.']);
  114. end
  116. case 'approach'
  117. if isa(varargin{ii+1},'function_handle')
  118. obj.method.approach = varargin{ii+1};
  119. change_string{end+1} = ('Set the approach to a custom function handle.');
  120. else
  121. switch lower(varargin{ii+1})
  122. case {'pca','principalcomponentanalysis','principal component analysis'}
  123. obj.method.approach = 'Principal Component Analysis';
  124. case {'dm','diffusion embedding','diffusionembedding','diffemb'}
  125. obj.method.approach = 'Diffusion Embedding';
  126. case {'le','laplacian eigenmap','laplacian eigenmaps','lapeig','laplacianeigenmaps','laplacianeigenmap'}
  127. obj.method.approach = 'Laplacian Eigenmap';
  128. otherwise
  129. error('Unknown approach. Valid approaches are: ''principal component analysis'', ''diffusion embedding'', and ''laplacian eigenmap''');
  130. end
  131. change_string{end+1} = (['Set the approach to: ' obj.method.approach '.']);
  132. end
  133. case 'alignment'
  134. if isa(varargin{ii+1},'function_handle')
  135. obj.method.kernel = varargin{ii+1};
  136. change_string{end+1} = ('Set the alignment to a custom function handle.');
  137. else
  138. switch lower(varargin{ii+1})
  139. case {'','none'}
  140. obj.method.alignment = 'None';
  141. case {'pa','procrustes','procrustes analysis','procrustesanalysis'}
  142. obj.method.alignment = 'Procrustes Analysis';
  143. case {'ja','joint','jointalignment'}
  144. obj.method.alignment = 'Joint Alignment';
  145. end
  146. change_string{end+1} = (['Set the alignment to: ' obj.method.alignment '.']);
  147. end
  149. case 'random_state'
  150. obj.random_state = varargin{ii+1};
  151. if ~isnan(varargin{ii+1})
  152. change_string{end+1} = ['Set the random state initialization to: ' num2str(varargin{ii+1}) '.'];
  153. else
  154. change_string{end+1} = ['No random state initialization set.'];
  155. end
  157. case 'n_components'
  158. obj.method.n_components = varargin{ii+1};
  159. change_string{end+1} = ['Set the number of requested components to: ' num2str(varargin{ii+1}) '.'];
  161. case 'verbose'
  162. obj.method.verbose = varargin{ii+1};
  163. change_string{end+1} = ['Verbose display was set to: ' mat2str(obj.method.verbose) '.'];
  165. otherwise
  166. error('Unknown property. Valid properties are: ''connectivitymatrix'', ''kernel'', ''approach'', and ''nullmodel''.');
  167. end
  168. end
  169. if obj.method.verbose
  170. for ii = 1:numel(change_string)
  171. disp(change_string{ii})
  172. end
  173. disp(' ')
  174. end
  175. end
  176. % -------------------------------------
  177. % -------------------------------------
  178. % -------------------------------------
  179. function kernel_data = kernels(obj,data,varargin)
  180. % Applies kernel to the data. Known kernels are "none", "Cosine
  181. % Similarity", and "Normalized Angle".
  182. p = inputParser;
  183. addParameter(p, 'sparsity', 90, @isnumeric);
  184. addParameter(p, 'tolerance', 1e-6, @isnumeric);
  185. addParameter(p, 'gamma', 1/size(data,1), @isnumeric);
  187. parse(p, varargin{:});
  188. kernel = obj.method.kernel;
  190. % Check zero vectors in input data.
  191. if any(all(data==0))
  192. error('Input data contains a zero vector. Gradients cannot be computed for these vectors.')
  193. end
  195. % Sparsify input data.
  196. if obj.method.verbose
  197. disp(['Running with sparsity parameter: ' num2str(p.Results.sparsity)]);
  198. end
  199. sparse_data = data;
  200. sparse_data(data < prctile(data,p.Results.sparsity)) = 0;
  202. % If a custom function, just run the custom function.
  203. if isa(kernel,'function_handle')
  204. kernel_data = kernel(data);
  205. return
  206. end
  208. switch kernel
  209. case 'None'
  210. if p.Results.sparsity ~= 0
  211. warning('Using a none kernel with a matrix sparsification will likely lead to an asymmetric matrix. Consider setting the sparsity parameter to 0.');
  212. end
  213. kernel_data = sparse_data;
  214. case {'Pearson','Spearman'}
  215. kernel_data = corr(sparse_data,'type',kernel);
  216. case 'Gaussian'
  217. if obj.method.verbose
  218. disp(['Running with gamma parameter: ' num2str(p.Results.gamma) '.']);
  219. end
  220. kernel_data = exp(-p.Results.gamma .* squareform(pdist(sparse_data').^2));
  221. case {'Cosine Similarity','Normalized Angle'}
  222. cosine_similarity = 1-squareform(pdist(sparse_data','cosine'));
  223. switch kernel
  224. case 'Cosine Similarity'
  225. kernel_data = cosine_similarity;
  226. case 'Normalized Angle'
  227. kernel_data = 1-acos(cosine_similarity)/pi;
  228. end
  229. otherwise
  230. error('Unknown kernel method');
  231. end
  233. % Check for negative numbers.
  234. if any(kernel_data(:) < 0)
  235. if obj.method.verbose
  236. disp('Found negative numbers in the kernel matrix. These will be set to zero.');
  237. end
  238. kernel_data(kernel_data < 0) = 0;
  239. end
  241. % Check for vectors of zeros.
  242. if any(all(kernel_data == 0))
  243. error(['After thresholding, a complete vector in the kernel ' ...
  244. 'matrix consists of zeros. Consider using a kernel that ' ...
  245. 'does not allow for negative numbers (e.g. normalized angle).']);
  246. end
  248. if ~issymmetric(kernel_data)
  249. if max(max(abs(kernel_data - kernel_data'))) < p.Results.tolerance
  250. kernel_data = tril(kernel_data) + tril(kernel_data,-1)';
  251. else
  252. error('Asymmetry in the affinity matrix is too large. Increase the tolerance. Alternatively, are you using a ''none'' kernel with a non-zero sparsity parameter? This may result in errors.');
  253. end
  254. end
  255. end
  256. % -------------------------------------
  257. % -------------------------------------
  258. % -------------------------------------
  259. function [embedding, lambda] = approaches(obj, data, varargin)
  260. % [embedding, result] = approaches(obj, data, varargin)
  261. %
  262. % Computes the embedded data. This function should not be
  263. % called directly; it should only be called from the
  264. % run_analysis method
  265. %% Check input arguments.
  267. p = inputParser;
  268. addParameter(p, 'alpha' , 0.5 , @isnumeric );
  269. addParameter(p, 'diffusion_time' , 0 , @isnumeric );
  271. % Parse the input
  272. parse(p, varargin{:});
  273. in = p.Results;
  275. % If a custom function, just run the custom function.
  276. if isa(obj.method.approach,'function_handle')
  277. embedding = obj.method.approach(data);
  278. return
  279. end
  281. if ~issymmetric(data)
  282. if max(max(abs(data - data'))) > eps % floating point issues.
  283. error('Affinity matrix is not symmetric.')
  284. else
  285. data = tril(data) + tril(data,-1)'; % Attempt to force symmetry
  286. end
  287. end
  289. if ~graph_is_connected(data)
  290. error('Affinity matrix is not a connected graph.');
  291. end
  293. %% Embedding.
  294. % Set the random state for reproducibility.
  295. if ~isnan(obj.random_state)
  296. rng(obj.random_state)
  297. end
  299. % Run manifold learning
  300. switch obj.method.approach
  301. case 'Principal Component Analysis'
  302. [~, embedding, ~, ~, lambda] = pca(data);
  303. embedding = embedding(:,1:obj.method.n_components);
  304. case 'Laplacian Eigenmap'
  305. [embedding, lambda] = laplacian_eigenmaps(data, obj.method.n_components);
  306. case 'Diffusion Embedding'
  307. if obj.method.verbose
  308. disp(['Running with alpha parameter: ' num2str(in.alpha)]);
  309. disp(['Running with diffusion time: ' num2str(in.diffusion_time)]);
  310. end
  311. [embedding, lambda] = diffusion_mapping(data, obj.method.n_components, in.alpha, in.diffusion_time);
  312. otherwise
  313. error('Unknown manifold technique.');
  314. end
  315. end
  316. end
  317. end

GradientMaps.m at commit 8730de8, under BSD-3-Clause · at the source

Overview

Authors: Xiaozheng Liu1,2, Zhongwei Guo3
ORCID iDs: Xiaozheng Liu, Zhongwei Guo
  1. The Second Affiliated Hospital and Yuying Children’s Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang, China, wmu.edu.cn
  2. Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou 325027, Zhejiang, China
  3. Tongde Hospital of Zhejiang Province, Hangzhou 310012, Zhejiang, China, zjtongde.com
Institutions: Wenzhou Medical University (China); Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University (China); Tongde Hospital of Zhejiang Province (China)
Journal: Depression and anxiety, volume 2026, issue 1, article 8792696
Dates: received 8 July 2026; accepted 17 August 2026; published online 2 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1155/da/8792696 · PMID 42688891 · PMCID PMC13536004 · OpenAlex W7204992840
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), fMRI (modality), human (organism), depression (population)
Methods: Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: functional gradients, functional magnetic resonance imaging, major depressive disorder, subtypes
MeSH: Connectome*, Major Depressive Disorder*, Transcriptome*, Brain, Middle Aged, Nerve Net (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Science and Technology Department of Zhejiang Province (2020KY182, 2018KY031, 2024KY873)
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Background/Objective: Major depressive disorder (MDD) exhibits significant heterogeneity, and identifying these distinct biological subtypes aids clinical intervention. In this study, we employed functional gradient and Hydra clustering methods to distinguish subtypes of depression.

Method: Imaging data were derived from a large‐scale, multicenter MDD imaging database, comprising 1067 patients with MDD and 907 healthy controls (HCs). Based on the principal functional gradient, the Hydra method was employed to distinguish subtypes of MDD. We employed the Mann–Whitney U‐test to compare functional gradient abnormalities across different subtypes of MDD while simultaneously conducting imaging transcriptomics analysis.

Results: Using principal functional gradients, we identified two subtypes of MDD. Compared with HCs, subtype 1 exhibited abnormal functional gradient values in the dorsal attention network (DAN), ventral attention network (VAN), limbic network (LIB), frontoparietal network (FTP), and default mode network (DMN); subtype 2 exhibited abnormal functional gradient values in the visual network (VIS), somatosensory network (SMT), DAN, VAN, and DMN. Transcriptomics revealed that subtype 1 functional gradient abnormalities were significantly correlated with theory mind, 5‐hydroxytryptamine (5‐HT) 1B; subtype 2 functional gradient abnormalities were significantly correlated with motor function, dopamine D2 receptor, and norepinephrine transporter (NET).

Conclusions: Our research deepens the understanding of the biological diversity of MDD, providing a biologically grounded framework for stratifying patients, which may inform the future design of hypothesis‐driven clinical trials targeting specific cognitive or sensorimotor circuits.

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

Repositories

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

evarol/HYDRA

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 25c15b2c37cc78105be4afc171f3a75338338057, 21 November 2018
Languages: MATLAB (2)
Size: 10 files, 2 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Optimization Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
5 files
At the source: github.com/evarol/HYDRA

MICA-MNI/BrainSpace

License: BSD-3-Clause
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 8730de88ae32c4f88eeaf16ef2a6e53c5c32dc34, 5 May 2026
Languages: Python (77), MATLAB (40), Jupyter (4)
Size: 373 files, 121 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, CITATION.cff, environment (Dockerfile, requirements.txt, setup.cfg, setup.py, docs/requirements.txt), tests, continuous integration, documentation, 4 notebooks
Tools: NumPy (52 files), BrainSpace (32 files), SciPy (18 files), scikit-learn (13 files), Matplotlib (9 files), NiBabel (3 files), Nilearn (3 files), GIfTI library for MATLAB (1 file), Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
123 files
At the source: github.com/MICA-MNI/BrainSpace

Jfortin1/ComBatHarmonization

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 91f8bf3045381776c79358d2772e6ace135e21ce, 27 July 2021
Languages: MATLAB (8), R (6), Python (2)
Size: 102 files, 16 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: neuroCombat (5 files), NumPy (2 files), pandas (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
17 files
At the source: github.com/Jfortin1/ComBatHarmonization

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

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 139 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

Data of the REST‐meta‐MDD Project are available at: https://rfmri.org/REST-meta-MDD. Software packages used in this manuscript include Hydra (https://github.com/evarol/HYDRA), Functional gradients (https://github.com/MICA-MNI/BrainSpace), and ComBat harmonization (https://github.com/Jfortin1/ComBatHarmonization).

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 3, 28 September 2026

  • Issue: n/a → 1

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 4 keywords, 6 MeSH terms, 1 funder, 53 references.

Cite

This paper

Liu, X., & Guo, Z. (2026). Connectome Gradient-Based Subtyping of Major Depressive Disorder Reveals Distinct Neurobiological and Transcriptomic Signatures. Depression and anxiety, 2026(1), 8792696. https://doi.org/10.1155/da/8792696

BibTeX

@article{liu2026connectome,
author = {Liu, Xiaozheng and Guo, Zhongwei},
title = {{Connectome Gradient-Based Subtyping of Major Depressive Disorder Reveals Distinct Neurobiological and Transcriptomic Signatures}},
journal = {Depression and anxiety},
year = {2026},
month = sep,
volume = {2026},
number = {1},
pages = {8792696},
publisher = {Wiley},
issn = {1091-4269},
doi = {10.1155/da/8792696},
url = {https://doi.org/10.1155/da/8792696},
pmid = {42688891},
pmcid = {PMC13536004}
}

RIS

TY - JOUR
AU - Liu, Xiaozheng
AU - Guo, Zhongwei
TI - Connectome Gradient-Based Subtyping of Major Depressive Disorder Reveals Distinct Neurobiological and Transcriptomic Signatures
T2 - Depression and anxiety
J2 - Depress Anxiety
PY - 2026
DA - 2026/09/02
VL - 2026
IS - 1
SP - 8792696
SN - 1091-4269
PB - Wiley
DO - 10.1155/da/8792696
UR - https://doi.org/10.1155/da/8792696
LA - en
ER -

CSL-JSON

{
"id": "10.1155/da/8792696",
"type": "article-journal",
"title": "Connectome Gradient-Based Subtyping of Major Depressive Disorder Reveals Distinct Neurobiological and Transcriptomic Signatures",
"container-title": "Depression and anxiety",
"author": [
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"family": "Liu",
"given": "Xiaozheng"
},
{
"family": "Guo",
"given": "Zhongwei"
}
],
"container-title-short": "Depress Anxiety",
"volume": "2026",
"issue": "1",
"page": "8792696",
"DOI": "10.1155/da/8792696",
"PMID": "42688891",
"PMCID": "PMC13536004",
"ISSN": "1091-4269",
"publisher": "Wiley",
"URL": "https://doi.org/10.1155/da/8792696",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
2
]
]
}
}

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

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