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The Perioperative Neurocognitive Disorder Prediction Based on AI-Assisted EEG Dynamic Features in Anesthetized Mice.

Code ↔ Paper

4 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 4 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § 3. Results › 3.4. Machine Learning Model for Predicting PND vs. Non-PND ↔ jfeeg.m, the whole file · a weak match · score 0.80 · Log Root Sum, Log Energy Entropy, Sequential Variation, Hjorth Complexity, band power, Arithmetic
  2. [2] § 3. Results › 3.4. Machine Learning Model for Predicting PND vs. Non-PND ↔ jfeeg.m, the whole file · a weak match · score 0.80 · Log Root Sum, Log Energy Entropy, Sequential Variation, Hjorth Complexity, band power, Arithmetic
  3. [3] § 3. Results › 3.4. Machine Learning Model for Predicting PND vs. Non-PND ↔ jLogRootSumOfSequentialVariation.m, lines 4–11 · score 0.63 · Log Root Sum, Sequential Variation
  4. [4] § 3. Results › 3.4. Machine Learning Model for Predicting PND vs. Non-PND ↔ jLogRootSumOfSequentialVariation.m, lines 4–11 · score 0.63 · Log Root Sum, Sequential Variation

Paper

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

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The authors' code

MATLAB · 41 lines · 1.5 KB · BSD-3-Clause · 2 matches

  1. % Feature Extraction Toolbox by Jingwei Too - 12/12/2020
  3. function feat = jfeeg(type,X,opts)
  4. switch type
  5. case 'mcl' ; fun = @jMeanCurveLength;
  6. case 'ha' ; fun = @jHjorthActivity;
  7. case 'hm' ; fun = @jHjorthMobility;
  8. case 'hc' ; fun = @jHjorthComplexity;
  9. case '1d' ; fun = @jFirstDifference;
  10. case 'n1d' ; fun = @jNormalizedFirstDifference;
  11. case '2d' ; fun = @jSecondDifference;
  12. case 'n2d' ; fun = @jNormalizedSecondDifference;
  13. case 'me' ; fun = @jMeanEnergy;
  14. case 'mte' ; fun = @jMeanTeagerEnergy;
  15. case 'lrssv' ; fun = @jLogRootSumOfSequentialVariation;
  16. case 'te' ; fun = @jTsallisEntropy;
  17. case 'sh' ; fun = @jShannonEntropy;
  18. case 'le' ; fun = @jLogEnergyEntropy;
  19. case 're' ; fun = @jRenyiEntropy;
  20. case 'am' ; fun = @jArithmeticMean;
  21. case 'sd' ; fun = @jStandardDeviation;
  22. case 'var' ; fun = @jVariance;
  23. case 'md' ; fun = @jMedian;
  24. case 'max' ; fun = @jMaximum;
  25. case 'min' ; fun = @jMinimum;
  26. case 'ar' ; fun = @jAutoRegressiveModel;
  27. case 'kurt' ; fun = @jKurtosis;
  28. case 'skew' ; fun = @jSkewness;
  29. case 'bpd' ; fun = @jBandPowerDelta;
  30. case 'bpt' ; fun = @jBandPowerTheta;
  31. case 'bpa' ; fun = @jBandPowerAlpha;
  32. case 'bpb' ; fun = @jBandPowerBeta;
  33. case 'bpg' ; fun = @jBandPowerGamma;
  34. case 'rba' ; fun = @jRatioBandPowerAlphaBeta;
  35. end
  36. if nargin < 3
  37. opts = [];
  38. end
  39. feat = fun(X,opts);
  40. end

jfeeg.m at commit 9014541, under BSD-3-Clause · at the source

Overview

Authors: Xinyang Li1, Hui Wang2,3,4, Qingyuan Miao1, Rui Zhou1, Mengfan He1, Hanxi Wan1, Yuxin Zhang1, Qian Zhang1, Zhouxiang Li1, Qianqian Wu1, Zhi Tao1, Xinwei Huang1, Enduo Feng1, Qiong Liu1, Yinggang Zheng1, Guangchao Zhao2,3,4, Lize Xiong1
ORCID iDs: Xinyang Li, Rui Zhou, Yuxin Zhang, Qiong Liu, Guangchao Zhao, Lize Xiong
  1. Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Translational Research Institute of Brain and Brain-like Intelligence, Clinical Research Centre for Anesthesiology and Perioperative Medicine, Department of Anesthesiology and Perioperative Medicine, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China; (X.L.); (Q.M.); (R.Z.); (M.H.); (H.W.); (Y.Z.); (Q.Z.); (Z.L.); (Q.W.); (Z.T.); (X.H.); (E.F.); (Q.L.)
  2. Department of Anesthesiology and Perioperative Medicine, Xijing Hospital, The Fourth Military Medical University, Xi’an 710032, China
  3. Key Laboratory of Anesthesiology, Ministry of Education of China, Xi’an 710032, China
  4. Innovation Research Institute, Xijing Hospital, The Fourth Military Medical University, Xi’an 710032, China
Institutions: Tongji University (China); Xijing Hospital (China); Air Force Medical University (China)
Journal: Diagnostics (Basel, Switzerland), volume 16, issue 8, article 1186
Dates: received 3 February 2026; accepted 2 April 2026; published online 16 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/diagnostics16081186 · PMID 42072815 · PMCID PMC13114885 · OpenAlex W7154605608
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), mouse (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Physiology & signal measures
Keywords: electroencephalography (EEG), perioperative neurocognitive disorder (PND), aging brain, machine learning, general anesthesia, neurophysiological biomarkers
Topic: Intensive Care Unit Cognitive Disorders (Critical Care and Intensive Care Medicine, Medicine), according to OpenAlex
Funding: the Major Project of the National Natural Science Foundation of China (No. 82293643, No. 82293640); the Scientific and Technological Innovation 2030-major project of Brain Science and Brain-Like Intelligence Technology (No. 2021ZD0202804)
Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Background: Postoperative neurocognitive disorders (PND) are frequent complications in the elderly surgical patients, with aging recognized as a major risk factor. This study aimed to identify electrophysiological markers and establish an exploratory machine learning framework for PND-related vulnerability prediction using anesthetic electroencephalography (EEG) features in aged mice. Methods: Young and aged mice underwent laparotomy under isoflurane anesthesia with EEG recording. Neurocognitive performance was quantified by 16 standardized behavioral fractions. A semi-supervised K-means algorithm, anchored on young-surgery mice, stratified aged-surgery mice into PND and non-PND clusters. EEG dynamics during anesthesia maintenance and emergence were analyzed, and machine learning models were trained to predict PND from EEG features. Results: At baseline, neurocognitive function was comparable across groups. After anesthesia/surgery, aged mice exhibited selective spatial and contextual memory impairments, with two-thirds classified as PND. During emergence, PND mice displayed elevated δ power and reduced α and β ratios. A Multi-layer Perceptron classifier showed discriminatory performance for PND classification in one evaluation setting (AUC = 0.94). Conclusions: This study identifies emergence-related EEG features associated with postoperative neurocognitive vulnerability in aged mice and provides an exploratory machine learning framework for preclinical risk stratification. These findings support further mechanistic investigation and warrant future validation in human perioperative EEG datasets.

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 4 matches between paragraphs and lines of code.

JingweiToo/EEG-Feature-Extraction-Toolbox

License: BSD-3-Clause
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 901454170bda3a425d19f40c4ff64dd740b3b22b, 10 January 2021
Languages: MATLAB (32)
Size: 34 files, 32 scripts
Software Heritage: not archived
Found in: the text, “2.8.1. Feature Extraction and Dataset Definition”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (6 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
34 files
At the source: github.com/JingweiToo/EEG-Feature-Extraction-Toolbox

cran.r-project.org/package=xgboost

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source: cran.r-project.org/package=xgboost

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 32 scripts, each with its path and the digest of its content;
  • 4 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

The data presented in this study are available on request from the corresponding author. The data are not publicly available because they have not been deposited in a public repository.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 6 keywords, 2 funders, 71 references.

Cite

This paper

Li, X., Wang, H., Miao, Q., Zhou, R., He, M., Wan, H., Zhang, Y., Zhang, Q., Li, Z., Wu, Q., Tao, Z., Huang, X., Feng, E., Liu, Q., Zheng, Y., Zhao, G., & Xiong, L. (2026). The Perioperative Neurocognitive Disorder Prediction Based on AI-Assisted EEG Dynamic Features in Anesthetized Mice. Diagnostics (Basel, Switzerland), 16(8), 1186. https://doi.org/10.3390/diagnostics16081186

BibTeX

@article{li2026perioperative,
author = {Li, Xinyang and Wang, Hui and Miao, Qingyuan and Zhou, Rui and He, Mengfan and Wan, Hanxi and Zhang, Yuxin and Zhang, Qian and Li, Zhouxiang and Wu, Qianqian and Tao, Zhi and Huang, Xinwei and Feng, Enduo and Liu, Qiong and Zheng, Yinggang and Zhao, Guangchao and Xiong, Lize},
title = {{The Perioperative Neurocognitive Disorder Prediction Based on AI-Assisted EEG Dynamic Features in Anesthetized Mice}},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = apr,
volume = {16},
number = {8},
pages = {1186},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2075-4418},
doi = {10.3390/diagnostics16081186},
url = {https://doi.org/10.3390/diagnostics16081186},
pmid = {42072815},
pmcid = {PMC13114885}
}

RIS

TY - JOUR
AU - Li, Xinyang
AU - Wang, Hui
AU - Miao, Qingyuan
AU - Zhou, Rui
AU - He, Mengfan
AU - Wan, Hanxi
AU - Zhang, Yuxin
AU - Zhang, Qian
AU - Li, Zhouxiang
AU - Wu, Qianqian
AU - Tao, Zhi
AU - Huang, Xinwei
AU - Feng, Enduo
AU - Liu, Qiong
AU - Zheng, Yinggang
AU - Zhao, Guangchao
AU - Xiong, Lize
TI - The Perioperative Neurocognitive Disorder Prediction Based on AI-Assisted EEG Dynamic Features in Anesthetized Mice
T2 - Diagnostics (Basel, Switzerland)
J2 - Diagnostics (Basel)
PY - 2026
DA - 2026/04/16
VL - 16
IS - 8
SP - 1186
SN - 2075-4418
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/diagnostics16081186
UR - https://doi.org/10.3390/diagnostics16081186
LA - en
ER -

CSL-JSON

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"id": "10.3390/diagnostics16081186",
"type": "article-journal",
"title": "The Perioperative Neurocognitive Disorder Prediction Based on AI-Assisted EEG Dynamic Features in Anesthetized Mice",
"container-title": "Diagnostics (Basel, Switzerland)",
"author": [
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"family": "Li",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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