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A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.

Code ↔ Paper

17 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 17 matches
  1. [1] § Materials and methods › Neuroimaging preprocessing › Anatomical preprocessing ↔ MEG_preprocessing/MEG_preprocessing_driver_script.sh, lines 47–108 · score 0.90 · single shell boundary, Scalp surfaces, FreeSurfer, elements model, forward model, recon
  2. [2] § Materials and methods › Fitting classifiers ↔ barycenter_robustness/barycenter_robustness_checks.ipynb, lines 81–130 · score 0.70 · StratifiedGroupKFold, cross task classification, logistic regression, v1, Irrelevant, predict
  3. [3] § Materials and methods › Neuroimaging preprocessing › MEG preprocessing ↔ MEG_preprocessing/cogitate-msp1/coglib/meeg/qc/QC_processing_eeg.py, lines 179–221 · score 0.69 · muscle artifacts, Maxwell filter, gradiometer, magnetometer, threshold, preprocessed
  4. [4] § Materials and methods › Fitting classifiers ↔ classification/fit_pyspi_classifiers.py, lines 346–422 · score 0.67 · StratifiedGroupKFold, cross task classification, cross validated, fit, SD, train
  5. [5] § Materials and methods › Neuroimaging preprocessing › MEG preprocessing ↔ MEG_preprocessing/MEG_preprocessing_driver_script.sh, lines 47–108 · score 0.67 · event related field, brain region, pipeline, preprocessed, fit, MEG
  6. [6] § Materials and methods › Neuroimaging preprocessing › Anatomical preprocessing ↔ data_visualization/methods.ipynb, lines 26–76 · score 0.65 · intraparietal sulcus, prefrontal cortex, Category selective, inflated, fsaverage, surface
  7. [7] § Materials and methods › Neuroimaging preprocessing › Anatomical preprocessing ↔ data_visualization/methods.ipynb, lines 26–76 · score 0.62 · prefrontal cortex, category selective, parcel, Network, atlas, FreeSurfer
  8. [8] § Materials and methods › Fitting classifiers ↔ MEG_preprocessing/cogitate-msp1/coglib/ieeg/decoding/calibration.py, lines 18–89 · score 0.62 · support vector machine, cross validated, binary, regression, probability, fit
  9. [9] § Materials and methods › Fitting classifiers ↔ barycenter_robustness/barycenter_robustness_checks.ipynb, lines 153–244 · score 0.59 · robustness check, logistic regression, cross validated, fit, classifier, accuracy
  10. [10] § Materials and methods › Neuroimaging data acquisition and task paradigm ↔ MEG_preprocessing/cogitate-msp1/coglib/beh_et/behavior/quality_checks.py, lines 833–903 · score 0.59 · alarm rates, hit rates, behavioral, durations, stimuli
  11. [11] § Materials and methods › Theory-driven neural modeling › Model development and implementation ↔ modeling/CogitateModels.ipynb, lines 66–122 · score 0.56 · strong adaptation, stimulus offset, stimulus onset, simulated, CS, PFC
  12. [12] § Materials and methods › Fitting classifiers ↔ classification/fit_pyspi_classifiers.py, lines 346–422 · score 0.56 · cross task classification, cross validating, pyspi, fit, stratified, folds
  13. [13] § Results ↔ classification/fit_pyspi_classifiers.py, lines 550–631 · score 0.53 · standard deviation, Fitting classifiers, cross validation, pyspi, SD, Irrelevant
  14. [14] § Materials and methods › Neuroimaging data acquisition and task paradigm ↔ MEG_preprocessing/cogitate-msp1/coglib/fmri/logfiles_and_checks/01_exp1_create_events_tsv_file.py, lines 89–223 · score 0.53 · stimulus events, alarm, hit, letters, BIDS, Irrelevant
  15. [15] § Materials and methods › Neuroimaging preprocessing › Anatomical preprocessing ↔ data_visualization/classification_analysis_visualization.ipynb, lines 65–155 · score 0.53 · prefrontal cortex, Category selective, IPS, SPIs, V2, V1
  16. [16] § Materials and methods › Neuroimaging preprocessing › Anatomical preprocessing ↔ data_visualization/classification_analysis_visualization.ipynb, lines 65–155 · score 0.52 · prefrontal cortex, category selective, V2, V1, mapping, CS
  17. [17] § Materials and methods › Theory-driven neural modeling › Quantitative model evaluation ↔ modeling/CogitateModels.ipynb, lines 66–122 · score 0.50 · GNWT models, sweep, smaller, stimulus onset, noise, simulated

Paper

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

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The authors' code

Python · 894 lines · 52 KB · no license · 3 matches

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It can be read at the source: classification/fit_pyspi_classifiers.py.

Overview

Authors: Annie G Bryant1,2, Christopher J Whyte2,3
ORCID iDs: Annie G Bryant, Christopher J Whyte
  1. School of Physics, The University of Sydney, Camperdown, NSW, Australia
  2. Centre for Complex Systems, The University of Sydney, Camperdown, NSW, Australia
  3. Neuroscience Research Theme, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
Institutions: The University of Sydney (Australia)
Journal: Neuroscience of consciousness, volume 2026, issue 1, article niag029
Dates: received 7 April 2025; accepted 17 May 2026; published online 7 July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/nc/niag029 · PMID 42415873 · PMCID PMC13338905 · OpenAlex W7167585600
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), none (in silico) (organism), cognitive (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Statistics, Preprocessing
Keywords: complex systems, consciousness, visual perception, functional connectivity
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 80 references in the paper

Abstract

Identifying the neural correlates of conscious visual perception remains a major challenge in neuroscience, requiring theories that bridge between subjective experience and measurable neural correlates. However, theoretical interpretation of empirical evidence is often post hoc and susceptible to confirmation bias. Building upon the adversarial collaboration mediated by the COGITATE Consortium, we present a generalizable approach for the data-driven identification, evaluation, and theoretical modeling of connectivity-based neural correlates of conscious visual perception. Using the same magnetoencephalography (MEG) dataset and accompanying pre-registered hypotheses from the COGITATE Consortium, we systematically compared 246 functional connectivity (FC) measures between regions predicted to underlie conscious vision by Integrated Information Theory (IIT) and/or Global Neuronal Workspace Theory (GNWT). We identified a family of FC measures based on the barycenter—tracking the ‘center of mass’ between two signals—as the top-performing stimulus decoding measures that generalize across regions central to predictions of both IIT and GNWT. To interpret these findings within a theoretical framework, we developed neural mass models that recapitulate the neural dynamics hypothesized to underlie conscious perception by each theory. Comparing simulated barycenter values from these models against empirically measured MEG data revealed that both the GNWT-based model, featuring delayed ignition dynamics, and the IIT-based model, which relied on synchronous sensory dynamics, captured the observed connectivity patterns. These results lend tentative support to GNWT, as the presence of ignition dynamics independent of task-demand conditions contradicts the predictions of IIT. Beyond dataset-specific conclusions and limitations, we introduce a framework for systematically identifying and testing candidate neural correlates of conscious visual perception in an unbiased and interpretable manner.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 17 matches between paragraphs and lines of code.

anniegbryant/MEG_functional_connectivity

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cab648bbe2eab7093f9ab7b9a5fcddabf28e60a0, 28 January 2026
Languages: Python (223), MATLAB (31), Shell (19), Jupyter (12), R (3)
Size: 945 files, 288 scripts
Software Heritage: not archived
Found in: “Code and data availability”
Holds: README, environment (requirements.txt, MEG_preprocessing/cogitate-msp1/coglib/bayesFactor/requirements.txt, MEG_preprocessing/cogitate-msp1/coglib/xnat/requirements_xnat.txt, MEG_preprocessing/cogitate-msp1/coglib/ieeg/plotting_uniformization/requirements.txt), tests, 12 notebooks
Not found: license file, CITATION.cff, continuous integration, documentation
Tools: NumPy (149 files), pandas (101 files), Matplotlib (89 files), MNE-Python (81 files), SciPy (66 files), MNE-BIDS (39 files), scikit-learn (29 files), seaborn (28 files), NiBabel (15 files), Nilearn (12 files), statsmodels (12 files), tidyverse (11 files), cowplot (9 files), easystats (9 files), broom (8 files), patchwork (8 files), scikit-image (8 files), Pingouin (6 files), FreeSurfer (5 files), MNE-Connectivity (5 files), autoreject (4 files), ggpubr (4 files), PyPREP (4 files), SPM (4 files), afex (2 files), BayesFactor (2 files), emmeans (2 files), FSL (2 files), ggplot2 (2 files), lmerTest (2 files), xarray (2 files), AFNI (1 file), car (1 file), ggseg (1 file), Statistics and Machine Learning Toolbox (1 file), NetworkX (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
289 files, not copied: shown from their source

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At the source: github.com/anniegbryant/MEG_functional_connectivity

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.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 288 scripts, each with its path and the digest of its content;
  • 17 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

Datasets cited

Code and data availability

All MEG data analyzed in this study are openly available upon registration at https://doi.org/10.17617/1.wqa3-wk71 (http://dx.doi.org/10.17617/1.wqa3-wk71) (Liu et al. 2024). All code needed to reproduce our analyses and visuals are freely available in our GitHub repository at https://github.com/anniegbryant/MEG_functional_connectivity. Preprocessed empirical data are also shared as a Zenodo repository at https://doi.org/10.5281/zenodo.18294156 (http://dx.doi.org/10.5281/zenodo.18294156).

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 4 keywords, 68 references.

Cite

This paper

Bryant, A. G., & Whyte, C. J. (2026). A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception. Neuroscience of consciousness, 2026(1), niag029. https://doi.org/10.1093/nc/niag029

BibTeX

@article{bryant2026data,
author = {Bryant, Annie G and Whyte, Christopher J},
title = {{A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception}},
journal = {Neuroscience of consciousness},
year = {2026},
month = jul,
volume = {2026},
number = {1},
pages = {niag029},
publisher = {Oxford University Press},
issn = {2057-2107},
doi = {10.1093/nc/niag029},
url = {https://doi.org/10.1093/nc/niag029},
pmid = {42415873},
pmcid = {PMC13338905}
}

RIS

TY - JOUR
AU - Bryant, Annie G
AU - Whyte, Christopher J
TI - A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception
T2 - Neuroscience of consciousness
J2 - Neurosci Conscious
PY - 2026
DA - 2026/07/07
VL - 2026
IS - 1
SP - niag029
SN - 2057-2107
PB - Oxford University Press
DO - 10.1093/nc/niag029
UR - https://doi.org/10.1093/nc/niag029
LA - en
ER -

CSL-JSON

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