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A week in the life of the human brain reveals stable states punctuated by chaotic-like transitions.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › Seizure network removal ↔ PCA_SeizureNetwork_Removal.m, lines 1–122 · score 0.81 · seizure network, seizure related areas, electrodes part, principal component, PCA, removal
  2. [2] § Methods › Robust principal components analysis ↔ RandomConsensus_PCA.m, lines 1–34 · score 0.59 · consensus PCA, RANSAC PCA, covary, bands, network
  3. [3] § Methods › Robust principal components analysis ↔ PCA_SeizureNetwork_Removal.m, lines 1–122 · score 0.59 · seizure network, RANSAC PCA, components, bands
  4. [4] § Methods › Intracranial EEG data collection ↔ FilterElectrodes.py, lines 18–116 · score 0.58 · bandpass filter, 115 Hz, EEG, 0.2 Hz, Electrodes
  5. [5] § Results › Functional parcels showed consistent fluctuations over days that could be linked to behavior and physiology with consistent anatomic trends governing their dynamics ↔ PCA_SeizureNetwork_Removal.m, lines 145–201 · score 0.52 · seizure onset zones, gamma, theta, beta, alpha, 30 Hz
  6. [6] § Results › Sleep deprivation affects both the stable and chaotic components of neural kinematics ↔ Fig6_Generation.m, lines 436–489 · score 0.51 · outwardly oriented, wakeful rest, Center manifold, baseline, Figure 6, sleep
  7. [7] § Methods › Neurocognitive states form an hourglass-like shape with a center manifold that separates stages of consciousness (Fig. 5) ↔ Fig5_Generation.m, lines 356–386 · score 0.51 · center manifold, N1, N2, N3, REM, sleep
  8. [8] § Results › Brain network transitions were circuitous, unpredictable, and chaotic ↔ Fig3_Generation.m, lines 469–499 · score 0.51 · error bounds, late behavioral, ratio, 0–1, displacement, chaos

Paper

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

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The authors' code

MATLAB · 201 lines · 6 KB · no license · 3 matches

  1. % Compare principal components to seizure networks
  2. % Created on 20210811 by Max B Wang
  4. % LIST OF SUBJECT IDs TO PROCESS
  5. subjList={'EP1155','EP1156'};
  7. snetSimilarity=cell(length(subjList),1);
  8. simCutoff=zeros(length(subjList),1);
  10. for sInd=1:length(subjList)
  11. disp(sInd)
  12. subjID=subjList{sInd};
  13. featClass=2;
  14. bandSelector=1;
  15. useFixed=1;
  16. aCorr=3;
  18. if aCorr==1
  19. append='';
  20. elseif aCorr==2
  21. append='_ECorr';
  22. elseif aCorr==3
  23. append='_Fixed_SACorr_Mean';
  24. end
  26. ECOG_Dir='/media/mwang/easystore/Processed_Data/';
  27. bandNames={'Theta','Alpha','Beta_l','Beta_u','Gamma'};
  29. figurePath=[ECOG_Dir subjID '/'];
  31. % Load in a variable universalElectrodes which has a list of all electrodes part of the patient's seizure related areas that you want to remove
  32. electrodeName_Data=load([figurePath 'electrodeIndexing.mat']);
  33. universalElectrodes=cellstr(electrodeName_Data.universalElectrodes);
  35. PCA_Data=load([figurePath 'RANSAC_PCA' append '.mat'],'useScores','useCoefs','feature_coefs','mu');
  37. useCoefs=PCA_Data.useCoefs;
  38. %
  39. % FOR EACH SUBJECT, LABEL ALL ELECTRODES BELONGING TO THE SEIZURE NETWORK
  40. if strcmp(subjID,'EP1155')
  41. snet={'LAMY4','LAMY5','LAMY6','LAMY7','LHH1','LHH2','RHH1','RHH2','RAMY1','RAMY2'};
  42. elseif strcmp(subjID,'EP1156')
  43. snet={'LHH1','LHH2','LHH3','LHT1','LHT2','LBT1'};
  44. end
  46. [~,ia,~] = intersect(universalElectrodes,snet);
  47. snetVec=zeros(length(universalElectrodes),1);
  48. snetVec(ia)=1;
  49. snetVec=snetVec/sqrt(snetVec.'*snetVec);
  51. % Store diagonals of feature coefs
  52. numBands=5;
  53. numElectrodes=size(VAR_Data.feature_coefs,3);
  54. numFeats=size(VAR_Data.feature_coefs,1);
  56. diag_coefs=zeros(numBands,numFeats,numElectrodes);
  58. for bInd=1:5
  59. for fInd=1:numFeats
  60. diag_coefs(bInd,fInd,:)=diag(squeeze(VAR_Data.feature_coefs(fInd,bInd,:,:)));
  61. end
  62. end
  64. numPCs=size(useCoefs,2);
  65. snetSims=zeros(numPCs,1);
  67. for plotInd=1:numPCs
  68. plot_Feat_Coefs=useCoefs(:,plotInd);
  70. if sum(plot_Feat_Coefs)<0
  71. plot_Feat_Coefs=-1*plot_Feat_Coefs;
  72. end
  74. bandMat=zeros(numElectrodes,5);
  75. for bInd=1:5
  76. bandMat(:,bInd)=(plot_Feat_Coefs.'*squeeze(diag_coefs(bInd,:,:))).';
  77. end
  78. pcaVec=mean(abs(bandMat),2);
  79. pcaVec=pcaVec./sqrt(pcaVec.'*pcaVec);
  81. snetSims(plotInd,1)=abs(snetVec.'*pcaVec);
  82. end
  84. % Permutation testing to assess significance
  85. numTrials=1000;
  86. maxSims=zeros(numTrials,1);
  88. for tInd=1:numTrials
  89. randBandSims=zeros(numPCs,1);
  91. for plotInd=1:numPCs
  92. coefsVec=rand(numFeats,1);
  94. randMat=zeros(numElectrodes,5);
  95. for bInd=1:5
  96. randMat(:,bInd)=(coefsVec.'*squeeze(diag_coefs(bInd,:,:))).';
  97. end
  99. randVec=mean(abs(randMat),2);
  100. randVec=randVec./sqrt(randVec.'*randVec);
  102. randBandSims(plotInd)=abs(snetVec.'*randVec);
  103. end
  105. maxSims(tInd)=max(randBandSims);
  106. end
  108. subplot(5,4,sInd)
  109. plot(snetSims,'-o','LineWidth',2); hold on
  110. plot([1 numPCs],[quantile(maxSims,0.95) quantile(maxSims,0.95)],'r','LineWidth',2)
  111. xlabel('Component')
  112. ylabel('Similarity')
  113. set(gca,'FontSize',15)
  114. title(subjID,'FontSize',20)
  116. snetSimilarity{sInd}=snetSims;
  117. simCutoff(sInd)=quantile(maxSims,0.95);
  118. end
  120. set(gcf,'color','w');
  122. % save('Data/20220421_SozSimilarity.mat','subjList','snetSimilarity','simCutoff')
  124. %%
  125. load('Data/20220421_SozSimilarity.mat','subjList','snetSimilarity','simCutoff')
  126. % figure('units','normalized','outerposition',[0 0 1 1])
  127. for sInd=1:length(subjList)
  128. snetSims=snetSimilarity{sInd};
  129. plotCutoff=simCutoff(sInd);
  130. numPCs=length(snetSims);
  132. subplot(5,4,sInd)
  133. plot(snetSims,'-o','LineWidth',2); hold on
  134. plot([1 numPCs],[plotCutoff plotCutoff],'r','LineWidth',2)
  135. xlabel('Subnetwork')
  136. ylabel('Similarity')
  137. set(gca,'FontSize',11)
  138. title(subjList{sInd},'FontSize',13)
  139. end
  141. set(gcf,'color','w');
  142. % orient(gcf,'landscape')
  143. % print('/home/mwang/SevenDayFigures/SOZ_Similarity','-dpdf','-fillpage')
  145. %%
  146. load('Data/20220421_SozSimilarity.mat','subjList','snetSimilarity','simCutoff')
  147. for sInd=1:length(subjList)
  148. disp(sInd)
  149. % subplot(5,4,sInd)
  150. % plot(snetSimilarity{sInd},'-o','LineWidth',2); hold on
  151. % plot([1 length(snetSimilarity{sInd})],[simCutoff(sInd) simCutoff(sInd)],'r','LineWidth',2)
  152. % xlabel('Component')
  153. % ylabel('Similarity')
  154. % set(gca,'FontSize',15)
  155. % % title(['Eigenmode Similarity to Seizure Onset Zone: ' subjID],'FontSize',30)
  156. % title(subjList{sInd},'FontSize',20)
  158. subjID=subjList{sInd};
  159. featClass=2;
  160. bandSelector=1;
  161. useFixed=1;
  162. aCorr=3;
  164. if aCorr==1
  165. append='';
  166. elseif aCorr==2
  167. append='_ECorr';
  168. elseif aCorr==3
  169. append='_Fixed_SACorr_Mean';
  170. end
  172. ECOG_Dir='/media/mwang/easystore/Processed_Data/';
  173. bandNames={'Theta','Alpha','Beta_l','Beta_u','Gamma'};
  175. figurePath=[ECOG_Dir subjID '/'];
  177. PCA_Data=load([figurePath 'RANSAC_PCA' append '_Trimmed.mat'],'trimScores','useCoefs','mu','feature_coefs','allScores','allCoefs','allMu');
  178. OG_PC_Data=load([figurePath 'RANSAC_PCA' append '.mat'],'feature_coefs');
  179. % allScores=PCA_Data.trimScores;
  180. % allCoefs=PCA_Data.useCoefs;
  181. % allMu=PCA_Data.mu;
  183. allScores=PCA_Data.allScores;
  184. allCoefs=PCA_Data.allCoefs;
  185. allMu=PCA_Data.allMu;
  187. if ~isnan(simCutoff(sInd))
  188. passCoefs=snetSimilarity{sInd}<simCutoff(sInd);
  189. else
  190. passCoefs=true(size(allScores,2),1);
  191. end
  193. trimScores=allScores(:,passCoefs);
  194. useCoefs=allCoefs(:,passCoefs);
  195. mu=allMu(passCoefs);
  196. feature_coefs=OG_PC_Data.feature_coefs;
  197. save([figurePath 'RANSAC_PCA' append '_Trimmed.mat'],'trimScores',...
  198. 'useCoefs','feature_coefs','mu','allScores','allCoefs','allMu');
  199. end
  201. % set(gcf,'color','w');

PCA_SeizureNetwork_Removal.m at commit 8e95b3c, no license · at the source

Overview

Authors: Maxwell B. Wang1,2,3,4,5, Max G’Sell2,6, James F. Castellano7, R. Mark Richardson3,8,9, Avniel Singh Ghuman1,3,5
ORCID iDs: Maxwell B. Wang, R. Mark Richardson, Avniel Singh Ghuman
  1. Neuroscience Institute, Carnegie Mellon University,Pittsburgh, PA USA
  2. Machine Learning Department, Carnegie Mellon University,Pittsburgh, PA USA
  3. Department of Neurological Surgery, University of Pittsburgh,Pittsburgh, PA USA
  4. Department of Neurological Surgery, University of California San Francisco,San Francisco, CA USA
  5. Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University,Pittsburgh, PA USA
  6. Department of Statistics and Data Science, Carnegie Mellon University,Pittsburgh, PA USA
  7. Department of Neurology, University of Pittsburgh,Pittsburgh, PA USA
  8. Department of Neurosurgery, Massachusetts General Hospital,Boston, MA USA
  9. Harvard Medical School,Boston, MA USA
Institutions: Carnegie Mellon University (United States); University of Pittsburgh (United States); University of California, San Francisco (United States); Massachusetts General Hospital (United States); Harvard University (United States)
Journal: Nature communications, volume 17, issue 1, article 7215
Dates: received 1 May 2025; accepted 29 April 2026; published online 5 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73347-y · PMID 42248858 · PMCID PMC13396633 · OpenAlex W7163674960
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), computational (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Graphs, Physiology & signal measures
Keywords: Cognitive neuroscience, Dynamical systems
MeSH: Brain*, Adult, Algorithms, Circadian Rhythm, Female, Heart Rate, Humans, Male, Nonlinear Dynamics, Sleep, Sleep Deprivation, Young Adult (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Hertz Foundation (Fannie and John Hertz Foundation); National Institutes of Health (T32GM008208)
Citations: not cited yet (Europe PMC); 80 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 8 matches between paragraphs and lines of code.

MNobodyWang/WeekLongBrain

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8e95b3c9d8628e036a150169d00b931aafcc8646, 3 March 2026
Languages: Python (18), MATLAB (11), Shell (4), Jupyter (1)
Size: 38 files, 34 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (environment.yml), 1 notebook
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (19 files), SciPy (17 files), Matplotlib (14 files), scikit-learn (10 files), Statistics and Machine Learning Toolbox (6 files), MNE-Python (6 files), h5py (5 files), Keras (5 files), TensorFlow (5 files), fdr_bh (Benjamini-Hochberg FDR) (2 files), statsmodels (2 files), igraph (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
35 files
At the source: github.com/MNobodyWang/WeekLongBrain

Zenodo 18718538

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: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (19 files), SciPy (17 files), Matplotlib (14 files), scikit-learn (10 files), Statistics and Machine Learning Toolbox (6 files), MNE-Python (6 files), h5py (5 files), Keras (5 files), TensorFlow (5 files), fdr_bh (Benjamini-Hochberg FDR) (2 files), statsmodels (2 files), igraph (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
35 files
At the source:

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Data

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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/s41467-026-73347-y.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 2 keywords, 12 MeSH terms, 2 funders, 69 references.

Cite

This paper

Wang, M. B., G’Sell, M., Castellano, J. F., Richardson, R. M., & Ghuman, A. S. (2026). A week in the life of the human brain reveals stable states punctuated by chaotic-like transitions. Nature communications, 17(1), 7215. https://doi.org/10.1038/s41467-026-73347-y

BibTeX

@article{wang2026week,
author = {Wang, Maxwell B. and G’Sell, Max and Castellano, James F. and Richardson, R. Mark and Ghuman, Avniel Singh},
title = {{A week in the life of the human brain reveals stable states punctuated by chaotic-like transitions}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7215},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73347-y},
url = {https://doi.org/10.1038/s41467-026-73347-y},
pmid = {42248858},
pmcid = {PMC13396633}
}

RIS

TY - JOUR
AU - Wang, Maxwell B.
AU - G’Sell, Max
AU - Castellano, James F.
AU - Richardson, R. Mark
AU - Ghuman, Avniel Singh
TI - A week in the life of the human brain reveals stable states punctuated by chaotic-like transitions
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/05
VL - 17
IS - 1
SP - 7215
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73347-y
UR - https://doi.org/10.1038/s41467-026-73347-y
LA - en
ER -

CSL-JSON

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"family": "Wang",
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"PMCID": "PMC13396633",
"ISSN": "2041-1723",
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