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The turbulent brain: Modeling vortex interactions for understanding human cognition.

Code ↔ Paper

7 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 7 matches
  1. [1] § METHODS › CPM ↔ Behavior_Ceff_7T_v1.m, lines 89–144 · score 0.83 · training fold, CPM conventions, feature selection, standardized predictors, behavior, scores
  2. [2] § METHODS › Empirical fMRI Data › Preprocessing and extraction of functional timeseries in fMRI resting data. ↔ spiral_HCP100unrelated.m, lines 1–41 · score 0.69 · ft_read_cifti, FieldTrip, 0.008 Hz, filtering, Schaefer
  3. [3] § METHODS › Theoretical Turbulent Vortices Framework › Spiral vorticity. ↔ spiral_HCP100unrelated.m, lines 43–175 · score 0.68 · phase vector field, smoothed, voxel, width, spiral, curl
  4. [4] § METHODS › CPM ↔ Behavior_Ceff_7T_v1.m, lines 89–144 · score 0.60 · entire training, refitted, outer, fold, behavioral, scores
  5. [5] § RESULTS › Predicting Behavior From Modeling of Interactions Between Turbulent Vortices ↔ Behavior_Ceff_7T_v1.m, lines 21–63 · score 0.58 · Pic Vocab, Card Sort, PMAT24, Speed, behavior
  6. [6] § RESULTS › Predicting Behavior From Modeling of Interactions Between Turbulent Vortices ↔ Behavior_Ceff_7T_v1.m, lines 21–63 · score 0.57 · Card Sorting, crystallized, executive, PMAT24, Speed, behavioral
  7. [7] § METHODS › Theoretical Turbulent Vortices Framework › Spiral vorticity. ↔ spiral_HCP100unrelated.m, lines 43–175 · score 0.52 · phase vector field, spiral, curl, vertex

Paper

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

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The authors' code

MATLAB · 180 lines · 4.8 KB · no license · 4 matches

  1. clear all
  2. path2=[ '../../Nonequilibrium/'];
  3. addpath(genpath(path2));
  4. path3=[ '../../Tenet/TENET/'];
  5. addpath(genpath(path3));
  6. path4=[ '../../TaskReservoir/DataHCP100ordered'];
  7. addpath(genpath(path4));
  8. path5=[ '../../TaskReservoir/DMF_Receptors'];
  9. addpath(genpath(path5));
  10. path6=[ '../../QuantumDiffMap/CHARM'];
  11. addpath(genpath(path6));
  12. path7=[ '../../EntropyProdandFlow/HCP'];
  13. addpath(genpath(path7));
  14. path8=[ '../../TurbuThermo/HCPrest'];
  15. addpath(genpath(path8));
  17. NSUB=182;
  19. load results_model_vortex_rest_7T.mat;
  21. %% Behav G-factor
  23. load('hcpbehaviouraldata.mat')
  24. load (['hcp7t_rfMRI_REST1_PA_schaefer1000.mat']);
  26. behav=hcpbehaviouraldata;
  27. for jj=1:1113
  28. behavsub(jj)=behav{jj,1};
  29. end
  31. for nsub=1:NSUB
  32. idsub=subject{nsub}.id;
  33. idx=find(idsub==behavsub);
  34. behav_PMAT24_A_CR(nsub)=behav{idx,122}; %% fluid int
  35. behav_ProcSpeed_Unadj(nsub)=behav{idx,129}; %% speed
  36. behav_CardSort_Unadj(nsub)=behav{idx,118}; %% executive
  37. behav_ListSort_Unadj(nsub)=behav{idx,158};
  38. behav_Flanker_Unadj(nsub)=behav{idx,120};
  39. behav_VSPLOT_TC(nsub)=behav{idx,145};
  40. behav_IWRD_TOT(nsub)=behav{idx,156};
  41. behav_PicSeq_Unadj(nsub)=behav{idx,116};
  42. behav_RedEng_Unadj(nsub)=behav{idx,125};
  43. behav_PicVocab_Unadj(nsub)=behav{idx,127}; %% crystallized int
  45. behav_all(1,nsub)=behav{idx,122};
  46. behav_all(2,nsub)=behav{idx,129};
  47. behav_all(3,nsub)=behav{idx,118};
  48. behav_all(4,nsub)=behav{idx,158};
  49. behav_all(5,nsub)=behav{idx,120};
  50. behav_all(6,nsub)=behav{idx,145};
  51. behav_all(7,nsub)=behav{idx,156};
  52. behav_all(8,nsub)=behav{idx,116};
  53. behav_all(9,nsub)=behav{idx,125};
  54. behav_all(10,nsub)=behav{idx,127};
  55. end
  57. % behav_y=behav_PMAT24_A_CR'; %% 334
  58. behav_y=behav_ProcSpeed_Unadj'; %% 200
  59. % behav_y=behav_CardSort_Unadj'; %% 234
  60. % behav_y=behav_PicVocab_Unadj'; %% 382
  62. [~ , indx]=find(isnan(behav_y'));
  63. behav_y(indx)=[];
  65. %% g factor
  66. [~, indx]=find(isnan(behav_all));
  67. % mean_measure= nanmean(behav_all,2);
  68. % for kk=1:size(a1,1)
  69. % behav_all(a1(kk),b1(kk))=mean_measure(a1(kk));
  70. % end
  71. behav_all(:,indx)=[];
  72. [lambda,psi,T,stats,F] = factoran(behav_all',1);
  73. behav_y=F;
  75. %% Start!!
  77. for nsub=1:NSUB
  78. % C=squeeze(Ceff(nsub,:,:));
  79. C=squeeze(FCempbold(nsub,:,:));
  80. Cvec(nsub,:)=C(:);
  81. end
  83. Cvec(indx,:)=[];
  85. Threshold2=0.14;%0.17
  86. alpha_EN=0.01;
  88. %%
  89. %% ===================================================
  90. % 1. Feature selection on entire dataset
  91. % ====================================================
  93. y = behav_y(:);
  94. Xall = Cvec;
  96. for i = 1:size(Xall,2)
  97. co2(i) = corr(Xall(:,i), y, 'rows','pairwise');
  98. end
  100. selected = find(abs(co2) > Threshold2); %,200);
  102. fprintf("Selected %d features.\n", length(selected));
  104. X = Xall(:, selected);
  106. % 2) Standardize predictors on full data (CPM convention)
  107. X = zscore(X); % you said you want this on full data
  109. % 3) Outer CV to get honest per-subject predictions
  110. for repe=1:100
  111. K = 10;
  112. cv = cvpartition(length(y),'KFold',K);
  113. yhat = zeros(size(y));
  115. for fold = 1:K
  116. % fprintf('Fold %d/%d\n', fold, K);
  117. train_idx = training(cv, fold);
  118. test_idx = test(cv, fold);
  120. Xtrain = X(train_idx, :);
  121. ytrain = y(train_idx);
  123. Xtest = X(test_idx, :);
  125. % Inside-fold: pick lambda with internal CV on the training set
  126. % NOTE: 'Standardize',false because we already z-scored globally by convention
  127. [B, FitInfo] = lasso(Xtrain, ytrain, 'Alpha', alpha_EN, 'CV', 10, 'Standardize', false);
  129. % Get lambda index that minimized MSE *on the training fold's internal CV*
  130. idxMin = FitInfo.IndexMinMSE;
  131. lambda_min = FitInfo.Lambda(idxMin);
  133. % Refit on the entire training set at the selected lambda
  134. % (lasso allows specifying Lambda to fit)
  135. [B_retrain, FitInfo2] = lasso( ...
  136. Xtrain, ytrain, ...
  137. 'Alpha', alpha_EN, ...
  138. 'Lambda', lambda_min, ...
  139. 'Standardize', false);
  140. intercept = FitInfo2.Intercept;
  142. % Predict test set
  143. yhat(test_idx) = Xtest * B_retrain + intercept;
  144. end
  146. %% ===================================================
  147. % 4. Metrics
  148. % ===================================================
  150. SST = sum((y - mean(y)).^2);
  151. SSE = sum((y - yhat).^2);
  153. R2 = 1 - SSE/SST;
  154. Corr = corr(y, yhat);
  156. fprintf('\n========== RESULTS ==========\n');
  157. fprintf('Cross-validated R2 = %.4f\n', R2);
  158. fprintf('Cross-validated Corr = %.4f\n', Corr);
  159. R2all(repe)=R2;
  160. Corrall(repe)=Corr;
  161. yhatall(repe,:)=yhat;
  162. end
  164. yhatall=squeeze(mean(yhatall));
  166. figure;
  167. violinplot([R2all' Corrall']);
  169. figure;
  170. scatter(yhatall,y,'filled');
  171. hold on;
  172. p = polyfit(yhatall, y, 1);
  173. xvals = linspace(min(yhatall), max(yhatall));
  174. plot(xvals, polyval(p, xvals), 'r', 'LineWidth', 2)
  175. hold off;
  176. [cc pp]=corrcoef(y,yhatall)
  177. cc
  178. pp
  180. save results_CPM_style_gfactor_FCbold.mat y yhatall R2all Corrall;

Behavior_Ceff_7T_v1.m at commit 0e40319, no license · at the source

Overview

Authors: Gustavo Deco1,2,3, Yonatan Sanz Perl1,3,4, Jianfeng Feng5, Morten L Kringelbach3,6,7,8
  1. Center for Brain and Cognition, Computational Neuroscience Group, Faculty of Medicine and Life Science, Universitat Pompeu Fabra, Barcelona, Spain
  2. Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
  3. International Centre for Flourishing, Universities of Oxford (UK), Aarhus (Denmark), and Pompeu Fabra (Spain)
  4. Department of Engineering, Universidad de San Andrés, Buenos Aires, Argentina
  5. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
  6. Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
  7. Department of Psychiatry, University of Oxford, Oxford, UK
  8. Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
Institutions: Institució Catalana de Recerca i Estudis Avançats (Spain); Universitat Pompeu Fabra (Spain); University of Oxford (United Kingdom); University of San Andrés (Argentina); Shanghai Medical College of Fudan University (China); Fudan University (China); Aarhus University (Denmark)
Journal: Network neuroscience (Cambridge, Mass.), volume 10, issue 3, pages 630-654
Dates: received 29 September 2025; accepted 23 February 2026; published online 20 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/netn.a.554 · PMID 42529694 · PMCID PMC13418524 · OpenAlex W7135198311
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, Statistics, Connectivity, Preprocessing, fMRI & imaging
Keywords: Turbulence, Brain dynamics, Oscillators, Hopf, Whole-brain modeling, Cognition
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research Foundation (DNRF117); Danmarks Grundforskningsfond (PID2022-136216NB-I00, DNRF 117, MICIU/AEI/10.13039/501100011033); HORIZON EUROPE Framework Programme (MICIU/AEI/10.13039/501100011033); Agència de Gestió d'Ajuts Universitaris i de Recerca (AEI/10.13039/501100011033, 10.13039/501100011033, 2021SGR00917, MICIU/AEI/ 10.13039/501100011033, 13039/501100011033); European Regional Development Fund (PID2022-136216NB-I00, 101071900, 10.13039/501100011033, 13039/501100011033, MICIU/ AEI /10.13039/501100011033, AEI/10.13039/501100011033, 501100011033, MICIU/AEI/10); Agencia Estatal de Investigación (AEI//10.13039/501100011033/, 501100011033, PID2022-136216NB-I00, MICIU/AEI /10.13039/501100011033, 10.13039/501100011033, 13039, 13039/501100011033, 10.13039, AEI/10)
Citations: cited by 1 paper (Europe PMC); 59 references in the paper

Abstract

The human brain needs distributed, time-critical computation to efficiently solve complex problems. Turbulence provides such highly efficient spacetime information processing and transmission across widespread brain networks, yet we have been missing a mechanistic understanding of the interactions of turbulent vortices underlying human cognition. Here, we build the first whole-brain model of turbulent vortices as defined by the levels of local synchronization in brain signals quantifying turbulent interactions in vortex space. Specifically, using large-scale human neuroimaging data, we found that the interactions of turbulent vortices is an excellent framework for understanding cognition and brain computation. In particular, we show that when combined with connectome-based predictive modeling, this significantly predict the g-factor and the scores on the underlying tasks. In addition, turbulent vortices also distinguish the detailed spacetime dynamics of rest and cognition—and can even distinguish between subtle subcomponents of cognitive tasks, where manipulation of vortices can be shown to change cognition. Overall, this whole-brain framework creates a natural vortex space for the brain computation underlying cognition, as well as potentially providing novel ways of controlling turbulent interactions in disease.

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 7 matches between paragraphs and lines of code.

decolab/TTB_modellingvortexinteraction

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0e403199f9523a399083a028e71f0299d552af4b, 11 February 2026
Languages: MATLAB (19)
Size: 19 files, 19 scripts
Software Heritage: not archived
Found in: “DATA AND CODE AVAILABILITY”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (14 files), Signal Processing Toolbox (7 files), Violinplot-Matlab (3 files), Optimization Toolbox (2 files), FieldTrip (1 file), GIfTI library for MATLAB (1 file), shadedErrorBar (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
19 files
At the source: github.com/decolab/TTB_modellingvortexinteraction

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;
  • 19 scripts, each with its path and the digest of its content;
  • 7 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

The open-source MATLAB code can be found here: https://github.com/decolab/TTB_modellingvortexinteraction.

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 2, 28 September 2026

  • Funding: added National Research Foundation: DNRF117; Danmarks Grundforskningsfond: PID2022-136216NB-I00, DNRF 117, MICIU/AEI/10.13039/501100011033; HORIZON EUROPE Framework Programme: MICIU/AEI/10.13039/501100011033; Agència de Gestió d'Ajuts Universitaris i de Recerca: AEI/10.13039/501100011033, 10.13039/501100011033, 2021SGR00917, MICIU/AEI/ 10.13039/501100011033, 13039/501100011033; European Regional Development Fund: PID2022-136216NB-I00, 101071900, 10.13039/501100011033, 13039/501100011033, MICIU/ AEI /10.13039/501100011033, AEI/10.13039/501100011033, 501100011033, MICIU/AEI/10; Agencia Estatal de Investigación: AEI//10.13039/501100011033/, 501100011033, PID2022-136216NB-I00, MICIU/AEI /10.13039/501100011033, 10.13039/501100011033, 13039, 13039/501100011033, 10.13039, AEI/10

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 6 keywords, 57 references.

Cite

This paper

Deco, G., Sanz Perl, Y., Feng, J., & Kringelbach, M. L. (2026). The turbulent brain: Modeling vortex interactions for understanding human cognition. Network neuroscience (Cambridge, Mass.), 10(3), 630-654. https://doi.org/10.1162/netn.a.554

BibTeX

@article{deco2026turbulent,
author = {Deco, Gustavo and Sanz Perl, Yonatan and Feng, Jianfeng and Kringelbach, Morten L},
title = {{The turbulent brain: Modeling vortex interactions for understanding human cognition}},
journal = {Network neuroscience (Cambridge, Mass.)},
year = {2026},
month = jul,
volume = {10},
number = {3},
pages = {630--654},
publisher = {MIT Press},
issn = {2472-1751},
doi = {10.1162/netn.a.554},
url = {https://doi.org/10.1162/netn.a.554},
pmid = {42529694},
pmcid = {PMC13418524}
}

RIS

TY - JOUR
AU - Deco, Gustavo
AU - Sanz Perl, Yonatan
AU - Feng, Jianfeng
AU - Kringelbach, Morten L
TI - The turbulent brain: Modeling vortex interactions for understanding human cognition
T2 - Network neuroscience (Cambridge, Mass.)
J2 - Netw Neurosci
PY - 2026
DA - 2026/07/20
VL - 10
IS - 3
SP - 630
EP - 654
SN - 2472-1751
PB - MIT Press
DO - 10.1162/netn.a.554
UR - https://doi.org/10.1162/netn.a.554
LA - en
ER -

CSL-JSON

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