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Integrating multidimensional nociceptive-related cortical features for unsupervised assessment of anesthesia states in rats.

Code ↔ Paper

3 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 3 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 › Unsupervised machine learning ↔ AssistantFunctions.zip/AssistantFunctions/align_labels_to_centers.m, the whole file · a weak match · score 0.80 · Hungarian assignment, reference centroid, greedy, mapping, reordered, match
  2. [2] § STAR★Methods › Method details › Relative information sharing analysis ↔ PCMI.zip/PCMI/PCrossMI.m, the whole file · a weak match · score 0.53 · Mutual information, cross, dimensional, embedding, PCMI, PE
  3. [3] § STAR★Methods › Method details › Permutation entropy (PE) and permutation cross mutual information (PCMI) estimation ↔ PCMI.zip/PCMI/PCrossMI.m, the whole file · a weak match · score 0.51 · Permutation entropy, cross, PCMI, mutual

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

MATLAB · 21 lines · 735 B · CC-BY-4.0 · 2 matches

  1. function pcmi = PCrossMI(x, y, m, tau, num_bins)
  2. % x, y: input signals (time series data)
  3. % m: embedding dimension
  4. % tau: time delay
  5. % num_bins: number of bins to compute the histogram for joint entropy
  7. % Step 1: Create permuted vectors of x and y
  8. X = embedding(x, m, tau);
  9. Y = embedding(y, m, tau);
  11. % Step 2: Compute the permutation entropy (PE) for x and y
  12. PE_x = permutation_entropy(X, num_bins);
  13. PE_y = permutation_entropy(Y, num_bins);
  15. % Step 3: Compute the joint entropy of X and Y
  16. joint_EN = joint_entropy(X, Y, num_bins);
  18. % Step 4: Compute the Permutation Cross Mutual Information (PCMI)
  19. % MI = H(X) + H(Y) - H(X, Y)
  20. pcmi = PE_x + PE_y - joint_EN;
  21. end

PCrossMI.m, under CC-BY-4.0 · at the source

Overview

Authors: Fengrui Zhang1,2, Wenqian Zhou3, Wen Liang4, Ruoyu Wang5, Libo Zhang1,2, Xiao Zhang6, Meizi Liu7, Tong Li8, Lupeng Yue1,2, Li Hu1,2
  1. State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
  2. Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
  3. Department of Psychology, Shandong Second Medical University, Weifang 261000, China
  4. Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA 92093, USA
  5. School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
  6. Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA 19104, USA
  7. Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
  8. Expedia Group, Seattle, WA 98119, US
Institutions: Institute of Psychology, Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China); University of California San Diego (United States); UNSW Sydney (Australia); University of Pennsylvania (United States); Washington University in St. Louis (United States); Expedia Group (United States) (United States)
Journal: iScience, volume 29, issue 6, article 116152
Dates: received 23 November 2025; accepted 13 May 2026; published online 28 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.116152 · PMID 42256304 · PMCID PMC13233798 · OpenAlex W7162664727
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: rat (organism), pain (population)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Complexity, fMRI & imaging
Keywords: Biological sciences, Neuroscience, Artificial intelligence, Machine learning
Topic: Anesthesia and Sedative Agents (Anesthesiology and Pain Medicine, Medicine), according to OpenAlex
Funding: Natural Science Foundation of Beijing Municipality (L246074, JQ22018); Beijing Natural Science Foundation
Citations: not cited yet (Europe PMC); 50 references in the paper
Research resources: MATLAB RRID:SCR_001622, EEGLAB 2021a RRID:SCR_007292

Abstract

Accurate anesthesia monitoring remains challenging because current approaches primarily assess consciousness while overlooking nociceptive processing. Although electroencephalography (EEG)-based metrics such as permutation entropy (PE) and permutation cross-mutual information (PCMI) are widely used, nociceptive-evoked cortical responses, especially gamma-band oscillations (GBOs), a robust index of nociceptive intensity, are rarely incorporated into anesthesia assessment. Here, we recorded electrocorticography from 23 rats under isoflurane anesthesia with nociceptive laser stimulation during both induction and emergence. We extracted GBOs, PE, and PCMI to evaluate their sensitivity to anesthesia states. GBOs and PE tracked nociceptive-related changes during induction, whereas PCMI was more sensitive during emergence. Integrating these multidimensional features, an unsupervised k-means framework identified four latent states: awake, shallow anesthesia, moderate anesthesia, and burst suppression. These findings establish nociceptive-evoked cortical responses as label-free markers and provide a scalable foundation for real-time, closed-loop anesthesia monitoring that jointly assesses consciousness and analgesia.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Zenodo 20069771

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 4 files
Software Heritage: not checked
Found in: “Data and code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
10 files
At the source: doi.org/10.5281/zenodo.20069771

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;
  • 8 scripts, each with its path and the digest of its content;
  • 3 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 and code availability

Data reported in this paper will be shared by the lead contact upon reasonable request.

Custom MATLAB code used for PCMI analysis, feature extraction, clustering, and auxiliary analyses has been deposited in Zenodo and is publicly available at https://doi.org/10.5281/zenodo.20069771.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 4 keywords, 2 funders, 49 references, 2 RRIDs.

Cite

This paper

Zhang, F., Zhou, W., Liang, W., Wang, R., Zhang, L., Zhang, X., Liu, M., Li, T., Yue, L., & Hu, L. (2026). Integrating multidimensional nociceptive-related cortical features for unsupervised assessment of anesthesia states in rats. iScience, 29(6), 116152. https://doi.org/10.1016/j.isci.2026.116152

BibTeX

@article{zhang2026integrating,
author = {Zhang, Fengrui and Zhou, Wenqian and Liang, Wen and Wang, Ruoyu and Zhang, Libo and Zhang, Xiao and Liu, Meizi and Li, Tong and Yue, Lupeng and Hu, Li},
title = {{Integrating multidimensional nociceptive-related cortical features for unsupervised assessment of anesthesia states in rats}},
journal = {iScience},
year = {2026},
month = may,
volume = {29},
number = {6},
pages = {116152},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116152},
url = {https://doi.org/10.1016/j.isci.2026.116152},
pmid = {42256304},
pmcid = {PMC13233798}
}

RIS

TY - JOUR
AU - Zhang, Fengrui
AU - Zhou, Wenqian
AU - Liang, Wen
AU - Wang, Ruoyu
AU - Zhang, Libo
AU - Zhang, Xiao
AU - Liu, Meizi
AU - Li, Tong
AU - Yue, Lupeng
AU - Hu, Li
TI - Integrating multidimensional nociceptive-related cortical features for unsupervised assessment of anesthesia states in rats
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/05/28
VL - 29
IS - 6
SP - 116152
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116152
UR - https://doi.org/10.1016/j.isci.2026.116152
LA - en
ER -

CSL-JSON

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