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Frequency-filtered attention for cross-species fMRI time series prediction under small-sample learning.

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 · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › Input construction and temporal normalization ↔ layers/StandardNorm.py, the whole file · a weak match · score 0.64 · learnable affine parameters, numerical stability, norm
  2. [2] § STAR★Methods › Method details › Input construction and temporal normalization ↔ layers/RevIN.py, the whole file · a weak match · score 0.63 · learnable affine parameters, numerical stability, norm

Paper

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

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The authors' code

Python · 68 lines · 2.1 KB · no license · 1 match

  1. import torch
  2. import torch.nn as nn
  5. class Normalize(nn.Module):
  6. def __init__(self, num_features: int, eps=1e-5, affine=False, subtract_last=False, non_norm=False):
  7. """
  8. :param num_features: the number of features or channels
  9. :param eps: a value added for numerical stability
  10. :param affine: if True, RevIN has learnable affine parameters
  11. """
  12. super(Normalize, self).__init__()
  13. self.num_features = num_features
  14. self.eps = eps
  15. self.affine = affine
  16. self.subtract_last = subtract_last
  17. self.non_norm = non_norm
  18. if self.affine:
  19. self._init_params()
  21. def forward(self, x, mode: str):
  22. if mode == 'norm':
  23. self._get_statistics(x)
  24. x = self._normalize(x)
  25. elif mode == 'denorm':
  26. x = self._denormalize(x)
  27. else:
  28. raise NotImplementedError
  29. return x
  31. def _init_params(self):
  32. # initialize RevIN params: (C,)
  33. self.affine_weight = nn.Parameter(torch.ones(self.num_features))
  34. self.affine_bias = nn.Parameter(torch.zeros(self.num_features))
  36. def _get_statistics(self, x):
  37. dim2reduce = tuple(range(1, x.ndim - 1))
  38. if self.subtract_last:
  39. self.last = x[:, -1, :].unsqueeze(1)
  40. else:
  41. self.mean = torch.mean(x, dim=dim2reduce, keepdim=True).detach()
  42. self.stdev = torch.sqrt(torch.var(x, dim=dim2reduce, keepdim=True, unbiased=False) + self.eps).detach()
  44. def _normalize(self, x):
  45. if self.non_norm:
  46. return x
  47. if self.subtract_last:
  48. x = x - self.last
  49. else:
  50. x = x - self.mean
  51. x = x / self.stdev
  52. if self.affine:
  53. x = x * self.affine_weight
  54. x = x + self.affine_bias
  55. return x
  57. def _denormalize(self, x):
  58. if self.non_norm:
  59. return x
  60. if self.affine:
  61. x = x - self.affine_bias
  62. x = x / (self.affine_weight + self.eps * self.eps)
  63. x = x * self.stdev
  64. if self.subtract_last:
  65. x = x + self.last
  66. else:
  67. x = x + self.mean
  68. return x

StandardNorm.py at commit f48e5a4, no license · at the source

Overview

Authors: Chengcheng Du1,2, Feifei Zhao1, Yinqian Sun1, Zeyang Yue1, Ruoyu Wu1, Jihang Wang1,3, Qian Zhang1,3, Yi Zeng1,2,4
ORCID iDs: Chengcheng Du, Qian Zhang
  1. BrainCog Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China
  2. School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
  3. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
  4. Center for Long-term AI, Beijing, China
Institutions: Chinese Academy of Sciences (China); Institute of Automation (China); University of Chinese Academy of Sciences (China)
Journal: iScience, volume 29, issue 9, article 117173
Dates: received 26 March 2026; accepted 14 July 2026; published online 13 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.117173 · PMID 42633179 · PMCID PMC13499140 · OpenAlex W7202363474
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), cognitive (subfield)
Methods: Statistics, Spectral & time-frequency, Connectivity, Machine learning
Keywords: machine learning, cognitive neuroscience, medical imaging
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (32571284); Chinese Academy of Sciences (XDB1010302); Institute of Automation, Chinese Academy of Sciences (E411230101)
Citations: not cited yet (Europe PMC); 44 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.

Repositories

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

Brain-Cog-Lab/FFAformer

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cc7d5e6929267f8d6605f30eb9184d22ebef0705, 26 March 2026
Languages: Python (26)
Size: 68 files, 26 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (20 files), NumPy (13 files), pandas (3 files), Matplotlib (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
27 files
At the source: github.com/Brain-Cog-Lab/FFAformer/

Zenodo 21543542

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the resources table
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PyTorch (20 files), NumPy (13 files), pandas (3 files), Matplotlib (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
27 files
At the source:

annaoliver/ffaformer

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f48e5a49c57fd68e54c27478cc08a181ae2bf2ec, 26 March 2026
Languages: Python (26)
Size: 68 files, 26 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (20 files), NumPy (13 files), pandas (3 files), Matplotlib (1 file), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
27 files
At the source: github.com/annaoliver/FFAformer/tree/FFAformer

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;
  • 78 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

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:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1016/j.isci.2026.117173.

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

  • Authors: added Chengcheng Du (0009-0009-6127-1123); Qian Zhang (0000-0001-5314-4233); removed Chengcheng Du; Qian Zhang
  • Funding: added National Natural Science Foundation of China: 32571284; Chinese Academy of Sciences: XDB1010302; Institute of Automation, Chinese Academy of Sciences: E411230101

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 3 keywords, 33 references.

Cite

This paper

Du, C., Zhao, F., Sun, Y., Yue, Z., Wu, R., Wang, J., Zhang, Q., & Zeng, Y. (2026). Frequency-filtered attention for cross-species fMRI time series prediction under small-sample learning. iScience, 29(9), 117173. https://doi.org/10.1016/j.isci.2026.117173

BibTeX

@article{du2026frequency,
author = {Du, Chengcheng and Zhao, Feifei and Sun, Yinqian and Yue, Zeyang and Wu, Ruoyu and Wang, Jihang and Zhang, Qian and Zeng, Yi},
title = {{Frequency-filtered attention for cross-species fMRI time series prediction under small-sample learning}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {9},
pages = {117173},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117173},
url = {https://doi.org/10.1016/j.isci.2026.117173},
pmid = {42633179},
pmcid = {PMC13499140}
}

RIS

TY - JOUR
AU - Du, Chengcheng
AU - Zhao, Feifei
AU - Sun, Yinqian
AU - Yue, Zeyang
AU - Wu, Ruoyu
AU - Wang, Jihang
AU - Zhang, Qian
AU - Zeng, Yi
TI - Frequency-filtered attention for cross-species fMRI time series prediction under small-sample learning
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/13
VL - 29
IS - 9
SP - 117173
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117173
UR - https://doi.org/10.1016/j.isci.2026.117173
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.117173",
"type": "article-journal",
"title": "Frequency-filtered attention for cross-species fMRI time series prediction under small-sample learning",
"container-title": "iScience",
"author": [
{
"family": "Du",
"given": "Chengcheng"
},
{
"family": "Zhao",
"given": "Feifei"
},
{
"family": "Sun",
"given": "Yinqian"
},
{
"family": "Yue",
"given": "Zeyang"
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{
"family": "Wu",
"given": "Ruoyu"
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{
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{
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}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "9",
"page": "117173",
"DOI": "10.1016/j.isci.2026.117173",
"PMID": "42633179",
"PMCID": "PMC13499140",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.117173",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
13
]
]
}
}

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

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