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Cingulate Gradient Dysfunction in End-Stage Renal Disease: Associations With Clinical Phenotypes and Exploratory Transcriptomic Signatures.

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Paper

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The authors' code

Python · 62 lines · 2.8 KB · no license

  1. import nibabel as nib
  2. import numpy as np
  4. def cifti_gradient(
  5. cifti_path, out_prefix, masks_path, threshold=10, not_fisher=False, n_components=10, method='NA', alpha=0.5,
  6. ref=None, n_iter=100, verbose=False
  7. ):
  8. from sklearn.metrics.pairwise import cosine_distances
  9. from .utils import column_wise_corr, row_wise_threshold
  10. from mapalign import embed
  12. cifti = nib.load(cifti_path)
  13. mask1 = nib.load(masks_path[0])
  14. if len(masks_path) > 1:
  15. mask2 = nib.load(masks_path[1])
  16. else:
  17. mask2 = mask1
  19. if not brain_models_are_equal(cifti.header, mask1.header) & brain_models_are_equal(cifti.header, mask2.header):
  20. raise ValueError('brain models are not equal')
  22. data1 = cifti.get_fdata()[:, mask1.get_fdata().flatten() > 0]
  23. data2 = cifti.get_fdata()[:, mask2.get_fdata().flatten() > 0]
  25. corr_matrix = column_wise_corr(data1, data2)
  26. corr_matrix = np.nan_to_num(corr_matrix)
  27. np.savetxt(out_prefix + 'corr_full.txt', corr_matrix) if verbose else None
  29. corr_matrix = row_wise_threshold(corr_matrix, threshold=threshold)
  30. corr_matrix = np.nan_to_num(corr_matrix)
  31. np.savetxt(out_prefix + 'corr_threshold.txt', corr_matrix) if verbose else None
  33. corr_matrix = np.nan_to_num(np.arctanh(corr_matrix)) if not not_fisher else corr_matrix
  35. match method:
  36. case 'CS':
  37. aff = 1 - cosine_distances(corr_matrix)
  38. case 'NA':
  39. aff = 1 - cosine_distances(corr_matrix)
  40. aff = 1 - (np.arccos(aff) / np.pi)
  41. case _:
  42. raise ValueError('method is not valid')
  44. np.savetxt(out_prefix + 'affine.txt', aff) if verbose else None
  45. emb, res = embed.compute_diffusion_map(aff, n_components=n_components, alpha=alpha, return_result=True)
  46. np.savetxt(out_prefix + 'emb.txt', emb)
  47. np.savetxt(out_prefix + 'emb_lambda.txt', res['lambdas'])
  49. if ref is not None:
  50. ref = np.loadtxt(ref)
  51. from brainspace.gradient import ProcrustesAlignment
  52. pa = ProcrustesAlignment(n_iter=n_iter)
  53. pa.fit([ref, emb])
  54. np.savetxt(out_prefix + 'emb_aligned.txt', pa.aligned_[1])
  55. np.savetxt(out_prefix + 'emb_aligned_range.txt',
  56. np.max(pa.aligned_[1], axis=0) - np.min(pa.aligned_[1], axis=0))
  57. np.savetxt(out_prefix + 'emb_aligned_std.txt', np.std(pa.aligned_[1], axis=0))
  59. ## for_each `echo derivates/xcp_abcd/sub*/ | xargs -n1 basename` : cd derivatives/xcp_abcd/IN/func \; \
  60. ## mkdir -p /mnt/i/250401LiPeng/data/gradients/CC/IN \; \
  61. ## hky.py cifti-gradient data.dscalar.nii /mnt/i/250401LiPeng/data/gradients/CC/IN/ /mnt/i/250401LiPeng/data/atlas/CC.dscalar.nii /mnt/i/250401LiPeng/data/atlas/CTX_noCC.dscalar.nii -r /mnt/i/250401LiPeng/data/gradients/CC_HC_mean_NA_emb.txt

Gradient.py, no license · at the source

Overview

Authors: Peng Li1,2,3, Jun‐Ya Mu1, Xin‐Yi Zhu1, Hui‐Jie Yuan1, Zhao‐Yao Luo1, Qian‐Ge Zhu1, Xuan Niu1, Xiu‐Long Feng3, Yu Han3, Ting Ge3, Chen‐Xi Wang3, Wen Wang3, Ming Zhang1
ORCID iDs: Jun‐Ya Mu, Chen‐Xi Wang, Wen Wang, Ming Zhang
  1. Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
  2. Department of Medical Imaging, Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, Shaanxi, China
  3. Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
Institutions: First Affiliated Hospital of Xi'an Jiaotong University (China); Tang Du Hospital (China); Air Force Medical University (China)
Journal: CNS neuroscience & therapeutics, volume 32, issue 7, article e70976
Dates: received 27 December 2025; accepted 30 May 2026; published online 1 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/cns.70976 · PMID 42381608 · PMCID PMC13320367 · OpenAlex W7166886308
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), fMRI (modality), human (organism), clinical / translational (subfield)
Methods: Statistics, Connectivity, fMRI & imaging, Machine learning, Smoothing, state filtering, decompositions
Keywords: cingulate cortex, end‐stage renal disease, functional magnetic resonance imaging, gene expression, gradient
MeSH: Gyrus Cinguli*, Kidney Failure, Chronic*, Transcriptome*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Phenotype, Prospective Studies (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Clinical research award of the first affiliated hospital of Xi’an Jiaotong university (XJTU1AF-CRF-2023-021); Talent Foundation of Tangdu Hospital (2018BJ003); 7T MRI Precision Neurology Platform of Shaanxi Province and Innovative Team for Early Warning and Rehabilitation of Mental Fatigue (2025PT-08); Health Research and Innovation Capacity Strengthening Platform Program of Shaanxi Province (2023PT-09); National Natural Science Foundation of China (82202121); Hovering Program of Fourth Military Medical University (axjhww); Science and Technology Research Project of Shaanxi Nuclear Industry Group Co. Ltd (61240302)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Aims: The cingulate cortex is highly vulnerable in end‐stage renal disease (ESRD) patients, but whether ESRD disrupts its functional gradient and the links to clinical phenotypes and underlying molecular mechanisms remain unclear.

Methods: We prospectively enrolled clinical and resting‐state functional MRI data from 125 participants (77 ESRD patients, 48 healthy controls) to explore cingulate gradient alterations. Associations between cingulate gradients and canonical functional networks, clinical phenotypes, and meta‐analytic behavioral domains were analyzed. A gene expression decoding analysis based on the Allen Human Brain Atlas was performed to advance understanding of how molecular mechanisms relate to hierarchical changes in ESRD.

Results: Across global, network, and regional scales, patients with ESRD showed significant cingulate gradient dysfunction, with these anomalies exhibiting associations across multiple functional domains. Notably, serum urea and hemoglobin levels were correlated with cingulate gradient dysfunction. Spatially, these alterations correlated with genes enriched in neurodegenerative processes. Excitatory and inhibitory neurons' specific transcriptional changes account for most of the observed correlation with ESRD‐specific cingulate gradient alterations.

Conclusion: Our findings highlight ESRD‐related cingulate gradient dysfunction and its links to clinical phenotypes and gene expression profiles, providing critical insights into the neurodegenerative underpinnings of cerebral dysfunction in ESRD.

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

Repository

Its files are read in the Code ↔ Paper reader above.

OSF f2t9j

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (1)
Size: 4 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: BrainSpace (1 file), NiBabel (1 file), NumPy (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/f2t9j/

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:

  • 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;
  • no match between paragraphs and code yet;
  • 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 discovery data and main analysis codes that support the findings are publicly available in the study's Open Science Framework repository (https://osf.io/f2t9j/). Diffusion embedding code is publicly accessible in the BrainSpace toolbox (https://github.com/MICA‐MNI/BrainSpace (https://github.com/MICA-MNI/BrainSpace)). Transcriptional level matrices were obtained using the abagen toolbox (v.0.1.4, https://github.com/rmarkello/abagen) on the AHBA dataset (http://human.brain‐map.org (http://human.brain-map.org)). The probe‐to‐gene annotations were obtained by the Re‐annotator toolkit (v1.0.0, https://sourceforge.net/projects/reannotator/). Gene enrichments were analyzed at https://metascape.org/gp/index.html#/main/step1.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 11 MeSH terms, 7 funders, 63 references.

Cite

This paper

Li, P., Mu, J., Zhu, X., Yuan, H., Luo, Z., Zhu, Q., Niu, X., Feng, X., Han, Y., Ge, T., Wang, C., Wang, W., & Zhang, M. (2026). Cingulate Gradient Dysfunction in End-Stage Renal Disease: Associations With Clinical Phenotypes and Exploratory Transcriptomic Signatures. CNS neuroscience & therapeutics, 32(7), e70976. https://doi.org/10.1002/cns.70976

BibTeX

@article{li2026cingulate,
author = {Li, Peng and Mu, Jun‐Ya and Zhu, Xin‐Yi and Yuan, Hui‐Jie and Luo, Zhao‐Yao and Zhu, Qian‐Ge and Niu, Xuan and Feng, Xiu‐Long and Han, Yu and Ge, Ting and Wang, Chen‐Xi and Wang, Wen and Zhang, Ming},
title = {{Cingulate Gradient Dysfunction in End-Stage Renal Disease: Associations With Clinical Phenotypes and Exploratory Transcriptomic Signatures}},
journal = {CNS neuroscience \& therapeutics},
year = {2026},
month = jul,
volume = {32},
number = {7},
pages = {e70976},
publisher = {Wiley},
issn = {1755-5930},
doi = {10.1002/cns.70976},
url = {https://doi.org/10.1002/cns.70976},
pmid = {42381608},
pmcid = {PMC13320367}
}

RIS

TY - JOUR
AU - Li, Peng
AU - Mu, Jun‐Ya
AU - Zhu, Xin‐Yi
AU - Yuan, Hui‐Jie
AU - Luo, Zhao‐Yao
AU - Zhu, Qian‐Ge
AU - Niu, Xuan
AU - Feng, Xiu‐Long
AU - Han, Yu
AU - Ge, Ting
AU - Wang, Chen‐Xi
AU - Wang, Wen
AU - Zhang, Ming
TI - Cingulate Gradient Dysfunction in End-Stage Renal Disease: Associations With Clinical Phenotypes and Exploratory Transcriptomic Signatures
T2 - CNS neuroscience & therapeutics
J2 - CNS Neurosci Ther
PY - 2026
DA - 2026/07/01
VL - 32
IS - 7
SP - e70976
SN - 1755-5930
PB - Wiley
DO - 10.1002/cns.70976
UR - https://doi.org/10.1002/cns.70976
LA - en
ER -

CSL-JSON

{
"id": "10.1002/cns.70976",
"type": "article-journal",
"title": "Cingulate Gradient Dysfunction in End-Stage Renal Disease: Associations With Clinical Phenotypes and Exploratory Transcriptomic Signatures",
"container-title": "CNS neuroscience & therapeutics",
"author": [
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{
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{
"family": "Luo",
"given": "Zhao‐Yao"
},
{
"family": "Zhu",
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{
"family": "Niu",
"given": "Xuan"
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{
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],
"container-title-short": "CNS Neurosci Ther",
"volume": "32",
"issue": "7",
"page": "e70976",
"DOI": "10.1002/cns.70976",
"PMID": "42381608",
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"issued": {
"date-parts": [
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