Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control.

Code ↔ Paper

18 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 18 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted

[1] § Results › Between-subjects and within-subjects analysis of brain-behavior associations ↔ taskfmri/6_plotting/plotting_utils.py, lines 9–24 · score 0.90 · basal ganglia, AntSalience, PostSalience, VisualAssoc, lFPN, rFPN
[2] § Methods › Inclusion criteria ↔ taskfmri/0_subjectlist/inclusions_1.py, lines 70–126 · score 0.88 · abcd_imgincl01.txt, iqc_sst_good_ser, mriqcrp20301.txt, imgincl sst
[3] § Results › Between-subjects and within-subjects analysis of brain-behavior associations ↔ taskfmri/6_plotting/plotting_utils.py, lines 4–7 · score 0.86 · AntSalience, BasalGanglia, PostSalience, VisualAssoc, lFPN, rFPN
[4] § Results › Within-subjects analysis reveals that subgroups with distinct control strategies have distinct brain-behavior associations ↔ taskfmri/6_plotting/plotting_utils.py, lines 4–7 · score 0.85 · AntSalience, BasalGanglia, PostSalience, VisualAssoc, lFPN, rFPN
[5] § Methods › Networks and regions of interest ↔ image_masks/make_DMN.py, the whole file · a weak match · score 0.82 · connected_components, connected_regions, Neurosynth automatic, retrieved, masks
[6] § Methods › Between- and within-subjects, general linear model analysis of fMRI ↔ taskfmri/6_plotting/ds_WB.py, lines 71–119 · score 0.81 · fetch_surf_fsaverage, plot_surf_stat_map, nilearn, mesh, surface, brain
[7] § Methods › Networks and regions of interest ↔ image_masks/make_subcortical_ROIs.py, lines 1–10 · score 0.80 · nonlinear asymmetric space, high resolution, MNI152, masks, subcortical, thresholded
[8] § Methods › Inclusion criteria ↔ taskfmri/0_subjectlist/inclusions_1_scanner_info_included_family_member.py, lines 25–78 · score 0.72 · genetic paired subjectid, pihat, siblings, family, scanner, variables
[9] § Methods › Networks and regions of interest ↔ taskfmri/6_plotting/resample.ipynb, lines 4–49 · score 0.69 · subcortical regions, Shirer regions, cognitive control regions, Shirer networks, Yeo, Pearson
[10] § Methods › Brain imaging ↔ taskfmri/1_preprocess/im_proc.py, lines 31–88 · score 0.68 · smooth_img, affine, template, epi, motion, volumes
[11] § Methods › Networks and regions of interest ↔ taskfmri/5_more_second_level/resample.py, lines 87–134 · score 0.66 · subcortical regions, Shirer regions, cognitive control regions, Shirer networks, Yeo, Pearson
[12] § Methods › Between- and within-subjects, general linear model analysis of fMRI ↔ taskfmri/3_individualstats/individualstats.py, lines 126–175 · score 0.64 · FirstLevelModel, design matrices, nilearn, signal, fit, models
[13] § Methods › Statistical testing ↔ taskfmri/5_more_second_level/permutations.ipynb, lines 390–475 · score 0.63 · permutation_test, Pearson correlation, correct go, coefficients, resamples, SSRT
[14] § Results › Nonergodic brain-behavior associations using observed reaction time ↔ taskfmri/4_groupstats/groupstats.py, lines 165–295 · score 0.61 · dorsal attention, salience network, sensorimotor, precuneus, anterior, posterior
[15] § Methods › Statistical testing ↔ taskfmri/5_more_second_level/permutations.ipynb, lines 60–106 · score 0.57 · false_discovery_control, permutation_test, Scipy
[16] § Methods › Representational similarity analysis ↔ taskfmri/6_plotting/rsa.ipynb, lines 58–89 · score 0.56 · common_norm, kdeplot, Density, clip, proactive delaying, correlation
[17] § Methods › Between- and within-subjects, general linear model analysis of fMRI ↔ taskfmri/3_individualstats/individualstats.py, lines 57–68 · score 0.55 · FirstLevelModel, regression coefficients, voxel, fit, SST, fMRI
[18] § Results › Dynamic cognitive process model of behavior ↔ taskfmri/6_plotting/overview.ipynb, lines 219–260 · score 0.53 · stopping expectancy, Stop signal, go process, arrow, proactivity

Paper

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

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The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

Python · 27 lines · 870 B · no license · 3 matches

"""Utilities used for plotting."""
# Order of Shirer networks:
SHIRER_ORDER = ['AntSalience','PostSalience','lFPN','rFPN','DMN','RSC/PHG',
'Precuneus','Visuospatial','Language','VisualAssoc','Visual','Auditory',
'Sensorimotor','BasalGanglia']
# Mapping from network names used in code to network names used in plots:
MAPPING = {
# Yeo-17
'SalVentAttn-1':'SalVAttn-1',
'SalVentAttn-2':'SalVAttn-2',
# Shirer networks
'anterior_Salience':'AntSalience',
'post_Salience':'PostSalience',
'LECN':'lFPN',
'RECN':'rFPN',
'dorsal_DMN':'DMN',
'ventral_DMN':'RSC/PHG',
'high_Visual':'VisualAssoc',
'prim_Visual':'Visual',
'Basal_Ganglia':'BasalGanglia',
}
def final_roi_names(roi_names):
return [MAPPING[roi] if roi in MAPPING else roi for roi in roi_names]

plotting_utils.py at commit b066a52, no license · at the source

Overview

Authors: Percy K Mistry¹, Nicholas K Branigan¹, Zhiyao Gao¹, Weidong Cai^1,2, Vinod Menon^1,2,3

ORCID iDs: Percy K Mistry, Nicholas K Branigan, Zhiyao Gao, Weidong Cai, Vinod Menon

Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Stanford, CA USA
Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA USA
Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA USA

Institutions: Stanford Medicine (United States); Stanford University (United States)

Journal: Nature communications, volume 17, issue 1, article 3494

Dates: received 29 January 2025; accepted 12 March 2026; published online 27 April 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41467-026-71404-0 · PMID 42045209 · PMCID PMC13121725 · OpenAlex W4400490780

Open access: gold, a free copy (OpenAlex)

Preprint: doi:10.1101/2024.07.05.602273

Status: code verified

Categories: human (organism), cognitive (subfield)

Methods: Connectivity, Statistics, Preprocessing, fMRI & imaging, Machine learning

Keywords: Cognitive control, Computational neuroscience, Human behaviour

MeSH: Brain*, Cognition*, Bayes Theorem, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: U.S. Department of Health & Human Services | National Institutes of Health (NIH) (MH121069, MH124816); National Science Foundation (NSF) (2024856)

Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

Nonergodicity and Simpson’s paradox present significant, yet underappreciated challenges in cognitive neuroscience. Leveraging brain imaging and behavioral data from over 4000 individuals and a Bayesian computational model of cognitive dynamics, we investigated brain-behavior relationships underlying cognitive control at both between-subjects and within-subjects levels. Strikingly, brain-behavior associations reversed across levels of analysis, revealing pervasive nonergodicity. Within-subjects analysis uncovered dissociated neural representations of reactive and proactive control and revealed that individuals who adaptively versus maladaptively regulated cognitive control exhibited distinct brain-behavior associations. Our findings demonstrate that between-subjects analyses can fundamentally mischaracterize within-individuals mechanisms, as group-level patterns not only disagreed with individual-level patterns but often reversed them. This work highlights the necessity of distinguishing between-subjects and within-subjects inferences in neuroscience, with implications for understanding cognitive mechanisms and designing personalized interventions.

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 18 matches between paragraphs and lines of code.

Zenodo 18626601

License: CC-BY-4.0

State: the link answers, verified on 30 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 (35 files), pandas (29 files), Matplotlib (13 files), Nilearn (11 files), SciPy (10 files), seaborn (8 files), scikit-learn (5 files), FSL (2 files), Statistics and Machine Learning Toolbox (1 file), statsmodels (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

75 files

PRAD_Behavioral_Dynamics/StartHere_PRADs.m, MATLAB, 40 lines
PRAD_Behavioral_Dynamics/SummarizeResults.m, MATLAB, 451 lines
PRAD_Behavioral_Dynamics/chkconv.m, MATLAB, 144 lines
PRAD_Behavioral_Dynamics/matjagsRAND.m, MATLAB, 904 lines
PRAD_Behavioral_Dynamics/sst_jags_and_save.m, MATLAB, 109 lines
PRAD_Behavioral_Dynamics/sst_prepare_for_jags.m, MATLAB, 203 lines
behavioral/correlations.py, Python, 166 lines
behavioral/get_SST_pm_behav.m, MATLAB, 14 lines
behavioral/get_SST_pm_cog.m, MATLAB, 6 lines
behavioral/get_SST_pm_cog_control_models.py, Python, 57 lines
behavioral/get_SST_pm_cog_control_models_test.py, Python, 34 lines
behavioral/npz_rip_cog_behav.py, Python, 72 lines
behavioral/npz_rip_cog_behav_test.py, Python, 29 lines
get_prad.sh, Shell, 7 lines
image_masks/make_DMN.py, Python, 29 lines
image_masks/make_dACC.py, Python, 23 lines
image_masks/make_subcortical_ROIs.py, Python, 40 lines
paths.py, Python, 31 lines
taskfmri/0_subjectlist/inclusions_1.py, Python, 483 lines
taskfmri/0_subjectlist/inclusions_1_scanner_info_included_family_member.py, Python, 90 lines
taskfmri/0_subjectlist/inclusions_control_models.py, Python, 35 lines
taskfmri/0_subjectlist/sex.py, Python, 36 lines
taskfmri/0_subjectlist/sl.py, Python, 112 lines
taskfmri/0_subjectlist/sl_test.py, Python, 24 lines
taskfmri/1_preprocess/im_proc.py, Python, 100 lines
taskfmri/1_preprocess/im_proc.sh, Shell, 16 lines
taskfmri/2_design/design.py, Python, 659 lines
taskfmri/2_design/run_design.py, Python, 83 lines
taskfmri/2_design/run_design_pradG.py, Python, 69 lines
taskfmri/2_design/run_design_pradIR.py, Python, 69 lines
taskfmri/2_design/run_design_rvm.py, Python, 67 lines
taskfmri/3_individualstats/individualstats.py, Python, 182 lines
taskfmri/3_individualstats/run_individualstats.py, Python, 82 lines
taskfmri/3_individualstats/run_individualstats_oom.py, Python, 88 lines
taskfmri/3_individualstats/run_individualstats_pradG.py, Python, 52 lines
taskfmri/3_individualstats/run_individualstats_pradIR.py, Python, 52 lines
taskfmri/3_individualstats/run_individualstats_rvm.py, Python, 52 lines
taskfmri/4_groupstats/groupstats.py, Python, 617 lines
taskfmri/4_groupstats/groupstats_one_sample_WB.py, Python, 132 lines
taskfmri/4_groupstats/groupstats_rema.py, Python, 75 lines
taskfmri/4_groupstats/print_n.py, Python, 27 lines
taskfmri/4_groupstats/run_groupstats_one_sample_WB.py, Python, 58 lines
taskfmri/4_groupstats/run_groupstats_one_sample_WB_pradG.py, Python, 53 lines
taskfmri/4_groupstats/run_groupstats_one_sample_WB_pradIR.py, Python, 53 lines
taskfmri/4_groupstats/run_groupstats_one_sample_WB_rvm.py, Python, 53 lines
taskfmri/4_groupstats/subject_average_mods.ipynb, Jupyter, 50 lines
taskfmri/5_more_second_level/anatomical_nonergodicity.ipynb, Jupyter, 108 lines
taskfmri/5_more_second_level/permutations.ipynb, Jupyter, 475 lines
taskfmri/5_more_second_level/permutations.py, Python, 27 lines
taskfmri/5_more_second_level/resample.py, Python, 151 lines
taskfmri/5_more_second_level/rsa.py, Python, 98 lines
taskfmri/6_plotting/anatomical_nonergodicity.ipynb, Jupyter, 224 lines
taskfmri/6_plotting/compare_correlations_ds.ipynb, Jupyter, 336 lines
taskfmri/6_plotting/compare_correlations_ds_test.py, Python, 22 lines
taskfmri/6_plotting/correlate_betas_WB.py, Python, 120 lines
taskfmri/6_plotting/ds_WB.py, Python, 151 lines
taskfmri/6_plotting/ds_WB_control_models.py, Python, 91 lines
taskfmri/6_plotting/overview.ipynb, Jupyter, 324 lines
taskfmri/6_plotting/plotting_utils.py, Python, 27 lines
taskfmri/6_plotting/print_p_values.py, Python, 100 lines
taskfmri/6_plotting/reliability_group.py, Python, 112 lines
taskfmri/6_plotting/reliability_individual.py, Python, 175 lines
taskfmri/6_plotting/rema.py, Python, 149 lines
taskfmri/6_plotting/resample.ipynb, Jupyter, 110 lines
taskfmri/6_plotting/rsa.ipynb, Jupyter, 257 lines
taskfmri/6_plotting/subgroups.ipynb, Jupyter, 240 lines
taskfmri/6_plotting/within_subjects_distributions.py, Python, 132 lines
taskfmri/make_source_data.sh, Shell, 59 lines
taskfmri/submit_chain.sh, Shell, 21 lines
utilities/backup_data_to_box.py, Python, 19 lines
utilities/backup_results_to_box.py, Python, 30 lines
utilities/parallel.py, Python, 19 lines
utilities/report_error.py, Python, 32 lines
utilities/submit.py, Python, 221 lines
README.md, Text, 118 lines

At the source:

scsnl/mistry_branigan_nonergodicity_2026

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: b066a52ce857aff8287f75ae6971f15c3b4815a1, 12 February 2026

Languages: Python (53), Jupyter (9), MATLAB (8), Shell (4)

Size: 82 files, 74 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: README, environment (environment.yml), tests, 9 notebooks

Not found: license file, CITATION.cff, continuous integration, documentation

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

75 files

PRAD_Behavioral_Dynamics/StartHere_PRADs.m, MATLAB, 40 lines
PRAD_Behavioral_Dynamics/SummarizeResults.m, MATLAB, 451 lines
PRAD_Behavioral_Dynamics/chkconv.m, MATLAB, 144 lines
PRAD_Behavioral_Dynamics/matjagsRAND.m, MATLAB, 904 lines
PRAD_Behavioral_Dynamics/sst_jags_and_save.m, MATLAB, 109 lines
PRAD_Behavioral_Dynamics/sst_prepare_for_jags.m, MATLAB, 203 lines
behavioral/correlations.py, Python, 166 lines
behavioral/get_SST_pm_behav.m, MATLAB, 14 lines
behavioral/get_SST_pm_cog.m, MATLAB, 6 lines
behavioral/get_SST_pm_cog_control_models.py, Python, 57 lines
behavioral/get_SST_pm_cog_control_models_test.py, Python, 34 lines
behavioral/npz_rip_cog_behav.py, Python, 72 lines
behavioral/npz_rip_cog_behav_test.py, Python, 29 lines
get_prad.sh, Shell, 7 lines
image_masks/make_DMN.py, Python, 29 lines, 1 match
image_masks/make_dACC.py, Python, 23 lines
image_masks/make_subcortical_ROIs.py, Python, 40 lines, 1 match
paths.py, Python, 31 lines
taskfmri/0_subjectlist/inclusions_1.py, Python, 483 lines, 1 match
taskfmri/0_subjectlist/inclusions_1_scanner_info_included_family_member.py, Python, 90 lines, 1 match
taskfmri/0_subjectlist/inclusions_control_models.py, Python, 35 lines
taskfmri/0_subjectlist/sex.py, Python, 36 lines
taskfmri/0_subjectlist/sl.py, Python, 112 lines
taskfmri/0_subjectlist/sl_test.py, Python, 24 lines
taskfmri/1_preprocess/im_proc.py, Python, 100 lines, 1 match
taskfmri/1_preprocess/im_proc.sh, Shell, 16 lines
taskfmri/2_design/design.py, Python, 659 lines
taskfmri/2_design/run_design.py, Python, 83 lines
taskfmri/2_design/run_design_pradG.py, Python, 69 lines
taskfmri/2_design/run_design_pradIR.py, Python, 69 lines
taskfmri/2_design/run_design_rvm.py, Python, 67 lines
taskfmri/3_individualstats/individualstats.py, Python, 182 lines, 2 matches
taskfmri/3_individualstats/run_individualstats.py, Python, 82 lines
taskfmri/3_individualstats/run_individualstats_oom.py, Python, 88 lines
taskfmri/3_individualstats/run_individualstats_pradG.py, Python, 52 lines
taskfmri/3_individualstats/run_individualstats_pradIR.py, Python, 52 lines
taskfmri/3_individualstats/run_individualstats_rvm.py, Python, 52 lines
taskfmri/4_groupstats/groupstats.py, Python, 617 lines, 1 match
taskfmri/4_groupstats/groupstats_one_sample_WB.py, Python, 132 lines
taskfmri/4_groupstats/groupstats_rema.py, Python, 75 lines
taskfmri/4_groupstats/print_n.py, Python, 27 lines
taskfmri/4_groupstats/run_groupstats_one_sample_WB.py, Python, 58 lines
taskfmri/4_groupstats/run_groupstats_one_sample_WB_pradG.py, Python, 53 lines
taskfmri/4_groupstats/run_groupstats_one_sample_WB_pradIR.py, Python, 53 lines
taskfmri/4_groupstats/run_groupstats_one_sample_WB_rvm.py, Python, 53 lines
taskfmri/4_groupstats/subject_average_mods.ipynb, Jupyter, 50 lines
taskfmri/5_more_second_level/anatomical_nonergodicity.ipynb, Jupyter, 108 lines
taskfmri/5_more_second_level/permutations.ipynb, Jupyter, 475 lines, 2 matches
taskfmri/5_more_second_level/permutations.py, Python, 27 lines
taskfmri/5_more_second_level/resample.py, Python, 151 lines, 1 match
taskfmri/5_more_second_level/rsa.py, Python, 98 lines
taskfmri/6_plotting/anatomical_nonergodicity.ipynb, Jupyter, 224 lines
taskfmri/6_plotting/compare_correlations_ds.ipynb, Jupyter, 336 lines
taskfmri/6_plotting/compare_correlations_ds_test.py, Python, 22 lines
taskfmri/6_plotting/correlate_betas_WB.py, Python, 120 lines
taskfmri/6_plotting/ds_WB.py, Python, 151 lines, 1 match
taskfmri/6_plotting/ds_WB_control_models.py, Python, 91 lines
taskfmri/6_plotting/overview.ipynb, Jupyter, 324 lines, 1 match
taskfmri/6_plotting/plotting_utils.py, Python, 27 lines, 3 matches
taskfmri/6_plotting/print_p_values.py, Python, 100 lines
taskfmri/6_plotting/reliability_group.py, Python, 112 lines
taskfmri/6_plotting/reliability_individual.py, Python, 175 lines
taskfmri/6_plotting/rema.py, Python, 149 lines
taskfmri/6_plotting/resample.ipynb, Jupyter, 110 lines, 1 match
taskfmri/6_plotting/rsa.ipynb, Jupyter, 257 lines, 1 match
taskfmri/6_plotting/subgroups.ipynb, Jupyter, 240 lines
taskfmri/6_plotting/within_subjects_distributions.py, Python, 132 lines
taskfmri/make_source_data.sh, Shell, 59 lines
taskfmri/submit_chain.sh, Shell, 21 lines
utilities/backup_data_to_box.py, Python, 19 lines
utilities/backup_results_to_box.py, Python, 30 lines
utilities/parallel.py, Python, 19 lines
utilities/report_error.py, Python, 32 lines
utilities/submit.py, Python, 221 lines
README.md, Text, 118 lines

At the source: github.com/scsnl/mistry_branigan_nonergodicity_2026/tree/v1.1

Code availability

All code used in this study has been archived at 10.5281/zenodo.18626601.

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

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;
148 scripts, each with its path and the digest of its content;
18 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

Data used in this study were from the ABCD study (https://abcdstudy.org/), held in the National Institute of Mental Health Data Archive. These data are available to eligible researchers. Source data are provided with this paper.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 3 keywords, 8 MeSH terms, 2 funders, 68 references.

Cite

This paper

Mistry, P. K., Branigan, N. K., Gao, Z., Cai, W., & Menon, V. (2026). Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control. Nature communications, 17(1), 3494. https://doi.org/10.1038/s41467-026-71404-0

BibTeX

@article{mistry2026nonergodicity, author = {Mistry, Percy K and Branigan, Nicholas K and Gao, Zhiyao and Cai, Weidong and Menon, Vinod}, title = {{Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control}}, journal = {Nature communications}, year = {2026}, month = apr, volume = {17}, number = {1}, pages = {3494}, publisher = {Nature Publishing Group}, issn = {2041-1723}, doi = {10.1038/s41467-026-71404-0}, url = {https://doi.org/10.1038/s41467-026-71404-0}, pmid = {42045209}, pmcid = {PMC13121725} }

RIS

TY - JOUR AU - Mistry, Percy K AU - Branigan, Nicholas K AU - Gao, Zhiyao AU - Cai, Weidong AU - Menon, Vinod TI - Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control T2 - Nature communications J2 - Nat Commun PY - 2026 DA - 2026/04/27 VL - 17 IS - 1 SP - 3494 SN - 2041-1723 PB - Nature Publishing Group DO - 10.1038/s41467-026-71404-0 UR - https://doi.org/10.1038/s41467-026-71404-0 LA - en ER -

CSL-JSON

{ "id": "10.1038/s41467-026-71404-0", "type": "article-journal", "title": "Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control", "container-title": "Nature communications", "author": [ { "family": "Mistry", "given": "Percy K" }, { "family": "Branigan", "given": "Nicholas K" }, { "family": "Gao", "given": "Zhiyao" }, { "family": "Cai", "given": "Weidong" }, { "family": "Menon", "given": "Vinod" } ], "container-title-short": "Nat Commun", "volume": "17", "issue": "1", "page": "3494", "DOI": "10.1038/s41467-026-71404-0", "PMID": "42045209", "PMCID": "PMC13121725", "ISSN": "2041-1723", "publisher": "Nature Publishing Group", "URL": "https://doi.org/10.1038/s41467-026-71404-0", "language": "en", "issued": { "date-parts": [ [ 2026, 4, 27 ] ] } }

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[10] doi:10.1038/s41586-026-10631-3 [code]: A prognostic human brain network for diffuse midline glioma.
Journal: Nature
In common: FSL, Nilearn, Statistics and Machine Learning Toolbox, 6 other tools, 2 references

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Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control.

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