OSCR

Predict neuromuscular performance in human epidural electrical stimulation: phase 1 trial interim results.

Code ↔ Paper

The paper beside its authors' code: matches between them have not been computed for this paper yet.

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 49 lines · 1.8 KB · no license

  1. import torch
  3. def relaxed_distortion_measure(func, z, eta=0.2, metric='identity', create_graph=True):
  4. if metric == 'identity':
  5. bs = len(z)
  6. z_perm = z[torch.randperm(bs)]
  7. if eta is not None:
  8. alpha = (torch.rand(bs) * (1 + 2*eta) - eta).unsqueeze(1).to(z)
  9. z_augmented = alpha*z + (1-alpha)*z_perm
  10. else:
  11. z_augmented = z
  12. v = torch.randn(z.size()).to(z)
  13. Jv = torch.autograd.functional.jvp(func, z_augmented, v=v, create_graph=create_graph)[1]
  14. TrG = torch.sum(Jv.view(bs, -1)**2, dim=1).mean()
  15. JTJv = (torch.autograd.functional.vjp(func, z_augmented, v=Jv, create_graph=create_graph)[1]).view(bs, -1)
  16. TrG2 = torch.sum(JTJv**2, dim=1).mean()
  17. return TrG2/TrG**2
  18. else:
  19. raise NotImplementedError
  21. def get_flattening_scores(G, mode='condition_number'):
  22. if mode == 'condition_number':
  23. S = torch.svd(G).S
  24. scores = S.max(1).values/S.min(1).values
  25. elif mode == 'variance':
  26. G_mean = torch.mean(G, dim=0, keepdim=True)
  27. A = torch.inverse(G_mean)@G
  28. scores = torch.sum(torch.log(torch.svd(A).S)**2, dim=1)
  29. else:
  30. pass
  31. return scores
  33. def jacobian_decoder_jvp_parallel(func, inputs, v=None, create_graph=True):
  34. batch_size, z_dim = inputs.size()
  35. if v is None:
  36. v = torch.eye(z_dim).unsqueeze(0).repeat(batch_size, 1, 1).view(-1, z_dim).to(inputs)
  37. inputs = inputs.repeat(1, z_dim).view(-1, z_dim)
  38. jac = (
  39. torch.autograd.functional.jvp(
  40. func, inputs, v=v, create_graph=create_graph
  41. )[1].view(batch_size, z_dim, -1).permute(0, 2, 1)
  42. )
  43. return jac
  45. def get_pullbacked_Riemannian_metric(func, z):
  46. J = jacobian_decoder_jvp_parallel(func, z, v=None)
  47. G = torch.einsum('nij,nik->njk', J, J)
  48. return G

geometry.py at commit 3c4eaf1, no license · at the source

Overview

Authors: Hongda Li1,2, Yunyue Wei2, Yanan Sui2, Xi Zhang1,2, Xuesong Luo1,2, Boyang Zhang1,2, Bozhi Ma1,2
ORCID iDs: Yanan Sui, Xuesong Luo, Bozhi Ma
  1. National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University,Beijing, China
  2. School of Aerospace Engineering, Tsinghua University,Beijing, China
Institutions: Tsinghua University (China)
Journal: Communications medicine, volume 6, issue 1, article 457
Dates: received 4 December 2024; accepted 21 May 2026; published online 29 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s43856-026-01695-3 · PMID 42209714 · PMCID PMC13503752 · OpenAlex W7162654706
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Evoked potentials, Connectivity, Physiology & signal measures
Keywords: Network models, Spinal cord diseases
Topic: Pain Management and Treatment (Anesthesiology and Pain Medicine, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (52207254, 03-02)
Citations: not cited yet (Europe PMC); 87 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

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

yunyuewei/HdSafeBO

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 3c4eaf167c0a410f1a0f937119120b8b6f0a004d, 7 November 2024
Languages: Python (34)
Size: 58 files, 34 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (28 files), NumPy (23 files), Matplotlib (13 files), pandas (2 files), scikit-learn (2 files), SciPy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
35 files
At the source: github.com/yunyuewei/HdSafeBO

Code availability statement

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

  • it points to the authors' code: yunyuewei/HdSafeBO
  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s43856-026-01695-3.

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;
  • 34 scripts, 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

Datasets cited

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/s43856-026-01695-3.

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 2, 28 September 2026

  • Funding: added National Natural Science Foundation of China: 52207254, 03-02

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 80 references.

Cite

This paper

Li, H., Wei, Y., Sui, Y., Zhang, X., Luo, X., Zhang, B., & Ma, B. (2026). Predict neuromuscular performance in human epidural electrical stimulation: phase 1 trial interim results. Communications medicine, 6(1), 457. https://doi.org/10.1038/s43856-026-01695-3

BibTeX

@article{li2026predict,
author = {Li, Hongda and Wei, Yunyue and Sui, Yanan and Zhang, Xi and Luo, Xuesong and Zhang, Boyang and Ma, Bozhi},
title = {{Predict neuromuscular performance in human epidural electrical stimulation: phase 1 trial interim results}},
journal = {Communications medicine},
year = {2026},
month = may,
volume = {6},
number = {1},
pages = {457},
publisher = {Nature Publishing Group},
issn = {2730-664X},
doi = {10.1038/s43856-026-01695-3},
url = {https://doi.org/10.1038/s43856-026-01695-3},
pmid = {42209714},
pmcid = {PMC13503752}
}

RIS

TY - JOUR
AU - Li, Hongda
AU - Wei, Yunyue
AU - Sui, Yanan
AU - Zhang, Xi
AU - Luo, Xuesong
AU - Zhang, Boyang
AU - Ma, Bozhi
TI - Predict neuromuscular performance in human epidural electrical stimulation: phase 1 trial interim results
T2 - Communications medicine
J2 - Commun Med (Lond)
PY - 2026
DA - 2026/05/29
VL - 6
IS - 1
SP - 457
SN - 2730-664X
PB - Nature Publishing Group
DO - 10.1038/s43856-026-01695-3
UR - https://doi.org/10.1038/s43856-026-01695-3
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s43856-026-01695-3",
"type": "article-journal",
"title": "Predict neuromuscular performance in human epidural electrical stimulation: phase 1 trial interim results",
"container-title": "Communications medicine",
"author": [
{
"family": "Li",
"given": "Hongda"
},
{
"family": "Wei",
"given": "Yunyue"
},
{
"family": "Sui",
"given": "Yanan"
},
{
"family": "Zhang",
"given": "Xi"
},
{
"family": "Luo",
"given": "Xuesong"
},
{
"family": "Zhang",
"given": "Boyang"
},
{
"family": "Ma",
"given": "Bozhi"
}
],
"container-title-short": "Commun Med (Lond)",
"volume": "6",
"issue": "1",
"page": "457",
"DOI": "10.1038/s43856-026-01695-3",
"PMID": "42209714",
"PMCID": "PMC13503752",
"ISSN": "2730-664X",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s43856-026-01695-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
29
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1063/5.0308450 [code]
Computational modeling of human vagus nerve stimulation with three-dimensional fascicular morphology.
Journal: APL bioengineering
In common: pandas, SciPy, Matplotlib, 1 other tool, computational modeling (no new data), 4 references
[2] doi:10.1038/s41467-026-75128-z
Surface circumferential spinal cord recording in freely moving rodents.
Journal: Nature communications
In common: other condition, 5 references
[3] doi:10.1016/j.xcrm.2026.102925
Spinal cord stimulation modulates post-synaptic inhibition to improve arm neuromotor control after chronic stroke.
Journal: Cell reports. Medicine
In common: clinical / translational, 5 references
[4] doi:10.1016/j.celrep.2026.117420 [code]
Neural population dynamics of direct electrical stimulation of neocortex.
Journal: Cell reports
In common: scikit-learn, pandas, SciPy, 2 other tools, 2 references
[5] doi:10.1038/s41598-026-57519-w [code]
Automated segmentation of neurons and spinal cord structures in immunofluorescence images using SpineDL.
Journal: Scientific reports
In common: PyTorch, scikit-learn, pandas, 3 other tools, other condition, 1 reference
[6] doi:10.1002/hbm.70593 [code]
Test-Retest Reliability of Sensorimotor Activity Measured With Spinal Cord fMRI.
Journal: Human brain mapping
In common: pandas, SciPy, Matplotlib, 1 other tool, 2 references
[7] doi:10.1038/s41598-026-53415-5 [code]
Computational design and immunoinformatics validation of a T cell multi-epitope vaccine targeting glioblastoma stem cells.
Journal: Scientific reports
In common: PyTorch, scikit-learn, pandas, 3 other tools, computational modeling (no new data), other condition
[8] doi:10.1038/s41467-026-71759-4 [code]
CellNiche represents cellular microenvironments in atlas-scale spatial omics data with contrastive learning.
Journal: Nature communications
In common: PyTorch, scikit-learn, pandas, 3 other tools, computational modeling (no new data), other condition
[9] doi:10.1371/journal.pone.0345287
Directional leads applied to spinal cord stimulation: A computational modelling study.
Journal: PloS one
In common: computational modeling (no new data), 3 references
[10] doi:10.1038/s41586-026-10653-x [code]
A mosaic of whole-body representations on the human precentral gyrus.
Journal: Nature
In common: PyTorch, scikit-learn, SciPy, 2 other tools, other condition, 1 reference

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.

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: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

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