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Long-Term Variability in Visual Processing versus Perceptual Stability.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and Methods › Acquisition and preprocessing of MEG data ↔ preprocessing/run_ica.py, lines 1–15 · score 0.78 · low pass filtered, Independent component, Bad channels, python, ICA, preprocessing
  2. [2] § Materials and Methods › Analysis › Decoding scanning day and scanning order ↔ decoding/time_elapsed/plot_results.py, lines 265–336 · score 0.61 · Benjamin Hochberg, explained variance, FDR, bootstrap, Decoding

Paper

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

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The authors' code

Python · 67 lines · 2.3 KB · no license · 1 match

  1. '''
  2. Usage, e.g., python run_ica.py -i '/media/8.1/raw_data/raw_data/memory_01.fif'
  4. This script is used for initial preprocessing. The following steps are included:
  5. 1) Excludes bad-channels based on file 'session_info.txt'. These channels were marked as bad based on visual expection of raw MEG data.
  6. 2) Crops the ends of the MEG recordings according to times specified in 'session_info.txt'
  7. 3) High and low pass filtering
  8. 4) Running independent component analysis (ICA)
  10. The script saves the ICA to a file. Unwanted components are manually detected and removed the file 'check_ica.ipynb'.
  11. '''
  13. import argparse
  14. import mne
  15. import json
  17. def main(filepath):
  18. filename = filepath.split('/')[-1]
  19. outpath = '/media/8.1/intermediate_data/laurap/ica/ica_solution/' + filename.split('.')[0] + '-ica.fif'
  21. # loading in the raw data
  22. raw = mne.io.read_raw_fif(filepath, on_split_missing = 'ignore');
  23. raw.load_data();
  24. raw.pick_types(meg=True, eeg=False, stim=True)
  26. ### EXCLUDING BAD CHANNELS ###
  27. # loading in the txt file with the channels that should be labeled as bad channels
  28. with open('../session_info.txt', 'r') as f:
  29. file = f.read()
  30. session_info = json.loads(file)
  32. # using dict[] notation to get the bad channels for the specific file. Not using dict.get() as this does not raise a key-error if the key does not exist
  33. list_bad_channels = session_info[filename]['bad_channels']
  35. # marking the channels as bad
  36. raw.info['bads'] = list_bad_channels
  38. ### CROPPING OF BEGGINNING AND ENDING OF MEG RECORDING ###
  39. tmin = session_info[filename]['tmin']
  40. tmax = session_info[filename]['tmax']
  41. cropped = raw.copy().crop(tmin = tmin, tmax = tmax)
  42. del raw
  45. ### BAND PASS FILTER ###
  46. filt_raw = cropped.copy().filter(l_freq=1, h_freq=40)
  47. del cropped
  50. ### RESAMPLING ###
  51. resampled_raw = filt_raw.copy().resample(250)
  52. del filt_raw
  55. ### ICA ###
  56. ica = mne.preprocessing.ICA(n_components=None, random_state=97, method='fastica', max_iter=3000, verbose=None)
  57. ica.fit(resampled_raw)
  59. # saving the ICA solution
  60. ica.save(outpath, overwrite=True)
  63. if __name__ == '__main__':
  64. ap = argparse.ArgumentParser()
  65. ap.add_argument('-in', '--infile', required=True, help='path to fif file')
  66. args = vars(ap.parse_args())
  67. main(args['infile'])

run_ica.py at commit ff52edf, no license · at the source

Overview

Authors: Laura Bock Paulsen1,2, Laura Masaracchia2, Francesca Fardo2, Christine Ahrends2,3, Diego Vidaurre2,4
ORCID iDs: Laura Bock Paulsen, Laura Masaracchia, Francesca Fardo
  1. Department of Linguistics, Cognitive Science and Semiotics, Aarhus University, Aarhus C 8000, Denmark
  2. Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus C 8000, Denmark
  3. Oxford Centre for Integrative Neuroimaging (OxCIN), University of Oxford, Oxford OX3 0BP, United Kingdom
  4. Oxford Centre for Human Brain Activity (OHBA), Department of Psychiatry, University of Oxford, Oxford OX3 0BP, United Kingdom
Institutions: Aarhus University (Denmark); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom)
Journal: eNeuro, volume 13, issue 6, pages ENEURO.0344-25.2026
Dates: received 15 September 2025; accepted 31 March 2026; published online 19 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0344-25.2026 · PMID 42270404 · PMCID PMC13286707 · OpenAlex W7164189738
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing
Keywords: decoding, magnetoencephalography, variability, visual processing
MeSH: Brain*, Pattern Recognition, Visual*, Visual Perception*, Adult, Female, Humans, Magnetoencephalography, Photic Stimulation, Time Factors, Young Adult (* major topic)
Journal subjects: Research Article: New Research, Cognition and Behavior
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 35 references in the paper

Abstract

Our brain is in constant change due to neural plasticity, but, still, our experience of the world feels relatively stable to us. Focusing on visual processing, we hypothesize that brain responses to stimuli may change over long periods of time, but in a way that is orthogonal to the dimensions that are relevant to stimulus category discrimination. To test this hypothesis, we acquired and analyzed a magnetoencephalography (MEG) dataset containing recordings from one female adult participant, with several scanning days spanning over 6 months. The participant passively attended to visual stimuli with the same stimulus presented within each session. We demonstrate that the specific scanning day can be predicted from the brain responses in a simple passive viewing paradigm, suggesting continuous temporal changes in neural activity over long time scales. However, information from one scanning day could be used to robustly decode the animacy of objects on a different scanning day, and importantly, decoding accuracy did not suffer with increasing time intervals between scans. That is, cross-decoding accuracy remained stable over months, despite between-scanning-day variability. These findings suggest that while processing of visual stimuli shows variability over long time scales, the core neural structure underlying object recognition remains stable and non-stimulus-specific. The results were validated in the open-access THINGS-MEG dataset, which employs a similar paradigm but covers a shorter longitudinal timespan. We find similar results across the four additional participants.

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 2 matches between paragraphs and lines of code.

laurabpaulsen/VisualVariability

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ff52edfceb1a4b3614651cee62a237316ee34551, 23 February 2026
Languages: Python (25), Shell (2), Jupyter (1)
Size: 56 files, 28 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: environment (requirements.txt), 1 notebook
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (18 files), MNE-Python (9 files), Matplotlib (6 files), SciPy (5 files), scikit-learn (4 files), pandas (3 files), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
28 files
At the source: github.com/laurabpaulsen/VisualVariability

Code availability

All analysis code is available as Extended Data (https://doi.org/10.1523/ENEURO.0344-25.2026) and on Github (https://github.com/laurabpaulsen/VisualVariability).

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

Tracing map

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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;
  • 28 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 keywords, 10 MeSH terms, 34 references.

Cite

This paper

Paulsen, L. B., Masaracchia, L., Fardo, F., Ahrends, C., & Vidaurre, D. (2026). Long-Term Variability in Visual Processing versus Perceptual Stability. eNeuro, 13(6), ENEURO.0344-25.2026. https://doi.org/10.1523/eneuro.0344-25.2026

BibTeX

@article{paulsen2026long,
author = {Paulsen, Laura Bock and Masaracchia, Laura and Fardo, Francesca and Ahrends, Christine and Vidaurre, Diego},
title = {{Long-Term Variability in Visual Processing versus Perceptual Stability}},
journal = {eNeuro},
year = {2026},
month = jun,
volume = {13},
number = {6},
pages = {ENEURO.0344--25.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0344-25.2026},
url = {https://doi.org/10.1523/eneuro.0344-25.2026},
pmid = {42270404},
pmcid = {PMC13286707}
}

RIS

TY - JOUR
AU - Paulsen, Laura Bock
AU - Masaracchia, Laura
AU - Fardo, Francesca
AU - Ahrends, Christine
AU - Vidaurre, Diego
TI - Long-Term Variability in Visual Processing versus Perceptual Stability
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/06/22
VL - 13
IS - 6
SP - ENEURO.0344
EP - 25.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0344-25.2026
UR - https://doi.org/10.1523/eneuro.0344-25.2026
LA - en
ER -

CSL-JSON

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