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A unique neural signature of long-term memory encoding from EEG inter-electrode correlation.

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 · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Method › EEG-based predictive modeling ↔ cpm_cv_crosstraining.m, the whole file · a weak match · score 0.50 · cross validation, predictive modeling, fold, behavior, connectome, training
  2. [2] § Method › EEG-based predictive modeling ↔ cpm_cv_crosstraining_residual.m, the whole file · a weak match · score 0.50 · cross validation, predictive modeling, fold, behavior, connectome, training

Paper

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The authors' code

MATLAB · 46 lines · 1.4 KB · no license · 1 match

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Overview

Authors: Chong Zhao1,2, Edward K Vogel1,2,3, Monica D Rosenberg1,2,3
ORCID iDs: Chong Zhao, Monica D Rosenberg
  1. Department of Psychology, University of Chicago, Chicago, IL, United States
  2. Institute for Mind and Biology, University of Chicago, Chicago, IL, United States
  3. Neuroscience Institute, University of Chicago, Chicago, IL, United States
Institutions: University of Chicago (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1228
Dates: received 21 October 2025; accepted 20 March 2026; published online 4 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1228 · PMID 42094076 · PMCID PMC13142893 · OpenAlex W4415482989
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.21.683799
Status: code verified
Categories: EEG (modality), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Evoked potentials, Connectivity, Physiology & signal measures
Keywords: visual long-term memory, individual differences, interelectrode correlation, EEG
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Office of Naval Research (N00014-12-1-0972, MURI N00014-23-1-2768); National Institute of Mental Health (ROIMH087214)
Citations: cited by 1 paper (Europe PMC); 56 references in the paper

Abstract

Classic memory models proposed that the encoding process involved in visual working memory (VWM) controls the bandwidth of encoding in long-term memory (LTM). Behaviorally, VWM and LTM accuracies are reliably correlated at the behavioral level, raising the question of whether LTM encoding uniquely engages processes that are distinct from VWM encoding. To investigate this, we recorded EEG activity as participants completed recognition memory tasks with set sizes of 32 and 128, far beyond typical VWM capacity. Using interelectrode correlation (IC) analysis, we found that IC patterns reliably predicted individual differences in LTM encoding across both set sizes, indicating a robust, domain-general neural signature. Importantly, this predictive power remained even after controlling for VWM and attentional control performance, suggesting that the model captures variance specific to LTM encoding. Temporally, predictive signals emerged only after stimulus onset and persisted for 500–600 ms. Early and late encoding phases involved distinct network structures, reflecting dynamic neural processes underlying individual differences in LTM encoding. Lastly, we showed that alpha band-passed IC, but not theta or beta band-passed IC, selectively predicted individual differences in LTM performance. Together, our findings reveal a unique and temporally dynamic neural signature that supports individual differences in LTM encoding, independent of general cognitive abilities.

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.

OSF 8kz4d

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (14)
Size: 36 files, 14 scripts
Software Heritage: not checked
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 (4 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
14 files, to read at the source

This repository has no license: its authors keep all rights. Read it at the source.

At the source: osf.io/8kz4d

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;
  • 14 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);
  • 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 data and code were made available at Open Science Framework (https://osf.io/8kz4d).

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

  • Authors: added Monica D Rosenberg (0000-0001-6179-4025); removed Monica D Rosenberg

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 4 keywords, 2 funders, 56 references.

Cite

This paper

Zhao, C., Vogel, E. K., & Rosenberg, M. D. (2026). A unique neural signature of long-term memory encoding from EEG inter-electrode correlation. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1228. https://doi.org/10.1162/imag.a.1228

BibTeX

@article{zhao2026unique,
author = {Zhao, Chong and Vogel, Edward K and Rosenberg, Monica D},
title = {{A unique neural signature of long-term memory encoding from EEG inter-electrode correlation}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {4},
pages = {IMAG.a.1228},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1228},
url = {https://doi.org/10.1162/imag.a.1228},
pmid = {42094076},
pmcid = {PMC13142893}
}

RIS

TY - JOUR
AU - Zhao, Chong
AU - Vogel, Edward K
AU - Rosenberg, Monica D
TI - A unique neural signature of long-term memory encoding from EEG inter-electrode correlation
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/05/04
VL - 4
SP - IMAG.a.1228
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1228
UR - https://doi.org/10.1162/imag.a.1228
LA - en
ER -

CSL-JSON

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