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Adaptive shifts in amygdala-hippocampal theta coupling govern aversive learning and extinction.

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] § Methods › Connectivity analyses › Transfer Entropy Estimation ↔ calculate_transfer_entropy.m, the whole file · a weak match · score 0.72 · Transfer entropy, nearest neighbour, EEGLAB, bidirectionally, delay, TE
  2. [2] § Methods › Connectivity analyses › Weighted phase lag index (wPLI) ↔ calculate_wpli.m, the whole file · a weak match · score 0.50 · Weighted phase lag, wPLI, signals

Paper

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

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The authors' code

MATLAB · 70 lines · 2.4 KB · no license · 1 match

  1. function [TE_1to2, TE_2to1] = calculate_transfer_entropy(signal1, signal2, fs, params)
  4. %Saurabh Sonkusare
  5. %University of CAmbridge
  6. %March 2023
  9. % CALCULATE_TRANSFER_ENTROPY - Computes bidirectional Transfer Entropy between two signals
  10. %
  11. % Inputs:
  12. % signal1 - First time series (N_samples x 1 or 1 x N_samples)
  13. % signal2 - Second time series (same dimensions as signal1)
  14. % fs - Sampling frequency (Hz)
  15. % params - Struct with optional parameters:
  16. % .freq_band - [low_freq high_freq] for filtering (default: no filtering)
  17. % .tau - Time delay in samples (default: 25)
  18. % .k - k-nearest neighbors (default: 4)
  19. % .n_trials - For trial-based data (default: 1)
  20. %
  21. % Outputs:
  22. % TE_1to2 - Transfer Entropy from signal1 ? signal2
  23. % TE_2to1 - Transfer Entropy from signal2 ? signal1
  25. % Set default parameters
  26. if nargin < 4
  27. params = struct();
  28. end
  30. if ~isfield(params, 'tau'), params.tau = 25; end
  31. if ~isfield(params, 'k'), params.k = 4; end
  32. if ~isfield(params, 'n_trials'), params.n_trials = 1; end
  34. % Ensure column vectors
  35. signal1 = signal1(:);
  36. signal2 = signal2(:);
  38. % Optional bandpass filtering
  39. % I have used EEGLAB filter functions though
  40. if isfield(params, 'freq_band')
  41. [b,a] = butter(4, params.freq_band/(fs/2), 'bandpass');
  42. signal1 = filtfilt(b, a, signal1);
  43. signal2 = filtfilt(b, a, signal2);
  44. end
  46. % Reshape for trial-based data
  47. if params.n_trials > 1
  48. signal1 = reshape(signal1, [], params.n_trials)';
  49. signal2 = reshape(signal2, [], params.n_trials)';
  50. end
  52. % Initialize TE arrays
  53. TE_1to2 = zeros(params.n_trials, 1);
  54. TE_2to1 = zeros(params.n_trials, 1);
  56. % Calculate TE for each trial
  57. for trial = 1:params.n_trials
  58. TE_1to2(trial) = compute_transfer_entropy_knn(...
  59. signal1(trial,:), signal2(trial,:), params.k, params.tau);
  61. TE_2to1(trial) = compute_transfer_entropy_knn(...
  62. signal2(trial,:), signal1(trial,:), params.k, params.tau);
  63. end
  65. % Average across trials if needed
  66. if params.n_trials > 1
  67. TE_1to2 = mean(TE_1to2);
  68. TE_2to1 = mean(TE_2to1);
  69. end
  70. end

calculate_transfer_entropy.m at commit 569b655, no license · at the source

Overview

Authors: Saurabh Sonkusare1,2,3, Qiong Ding1,3, Christopher Weirich1, Yashu Feng1, Wei Liu3, Ruoqi Yang3, Alekhya Mandali2,4, Samantha Sallie2, Violeta Casero2, Chunyan Cao3, Dianyou Li3, Bomin Sun3, Shikun Zhan3, Valerie Voon1,2,3
ORCID iDs: Saurabh Sonkusare, Qiong Ding, Christopher Weirich, Ruoqi Yang, Alekhya Mandali, Samantha Sallie, Dianyou Li, Bomin Sun, Valerie Voon
  1. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University,Shanghai, China
  2. Department of Psychiatry, University of Cambridge,Cambridge, UK
  3. Department of Neurosurgery, Centre for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine,Shanghai, China
  4. School of Psychology, University of Sheffield,Sheffield, UK
Institutions: Fudan University (China); University of Cambridge (United Kingdom); Shanghai Jiao Tong University (China); University of Sheffield (United Kingdom)
Journal: Nature communications, volume 17, issue 1, article 7835
Dates: received 2 June 2025; accepted 21 May 2026; published online 10 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-73967-4 · PMID 42270631 · PMCID PMC13439568 · OpenAlex W7164200923
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Machine learning, fMRI & imaging
Keywords: Fear conditioning, Extinction, Hippocampus, Amygdala
MeSH: Amygdala*, Avoidance Learning*, Extinction, Psychological*, Hippocampus*, Theta Rhythm*, Adult, Electroencephalography, Female, Humans, Male, Memory, Young Adult (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute for Health Research (NIHR) (NIHR203312)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

Adaptive behaviour relies on the flexible encoding and suppression of aversive associations often underpinned by amygdala-hippocampal interactions. Yet the spectral and directional dynamics underlying these interactions in humans remain poorly understood. Using intracranial EEG recordings from the amygdala and the hippocampus acquired during a two-day aversive learning and extinction task, we identified frequency-specific shifts: amygdala theta (3–8 Hz) and gamma (30–45 Hz) power increased during conditioning and decreased during extinction, while hippocampal alpha and gamma activity gave way to theta and gamma during extinction. Directional phase connectivity, results showed frequency-specific reversals: amygdala-to-hippocampus dominance at 3-5 Hz and hippocampus-to-amygdala predominance at 6-8 Hz, a reconfiguration validated by computational modelling. These findings uncover distinct theta sub-bands coordinating dynamic, bidirectional communication in the human amygdala–hippocampal circuit, elucidating a neural mechanism for the flexible regulation of emotional memory.

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.

srbsonkusare/amygdala_hippocampal_dynamics_in_conditioning-extinction

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 569b65584a86ac9f13f0db5c8bdbd8e76db83a0e, 18 May 2025
Languages: MATLAB (2)
Size: 2 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/srbsonkusare/amygdala_hippocampal_dynamics_in_conditioning-extinction

Code availability

The Code for this project were acquired from patients undergoing clinical care and consenting to additional research protocols. Researchers wishing to access these data will require local ethics approval and a data sharing agreement with Ruijin Hospital, Shanghai, China. Open-source toolboxes have been used for analyses of this study. Open-source toolboxes have been used for analyses of this study. Specific codes are made available on github https://github.com/srbsonkusare/amygdala_hippocampal_dynamics_in_conditioning-extinction.

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 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 availability

The data for this project were acquired from patients undergoing clinical care and consenting to additional research protocols. Researchers wishing to access these data will require local ethics approval and a data sharing agreement with Ruijin Hospital, Shanghai, China. Open-source toolboxes have been used for analyses of this study. Open-source toolboxes have been used for analyses of this study. Specific codes are made available on github https://github.com/srbsonkusare/amygdala_hippocampal_dynamics_in_conditioning-extinction. 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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 4 keywords, 12 MeSH terms, 1 funder, 67 references.

Cite

This paper

Sonkusare, S., Ding, Q., Weirich, C., Feng, Y., Liu, W., Yang, R., Mandali, A., Sallie, S., Casero, V., Cao, C., Li, D., Sun, B., Zhan, S., & Voon, V. (2026). Adaptive shifts in amygdala-hippocampal theta coupling govern aversive learning and extinction. Nature communications, 17(1), 7835. https://doi.org/10.1038/s41467-026-73967-4

BibTeX

@article{sonkusare2026adaptive,
author = {Sonkusare, Saurabh and Ding, Qiong and Weirich, Christopher and Feng, Yashu and Liu, Wei and Yang, Ruoqi and Mandali, Alekhya and Sallie, Samantha and Casero, Violeta and Cao, Chunyan and Li, Dianyou and Sun, Bomin and Zhan, Shikun and Voon, Valerie},
title = {{Adaptive shifts in amygdala-hippocampal theta coupling govern aversive learning and extinction}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7835},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73967-4},
url = {https://doi.org/10.1038/s41467-026-73967-4},
pmid = {42270631},
pmcid = {PMC13439568}
}

RIS

TY - JOUR
AU - Sonkusare, Saurabh
AU - Ding, Qiong
AU - Weirich, Christopher
AU - Feng, Yashu
AU - Liu, Wei
AU - Yang, Ruoqi
AU - Mandali, Alekhya
AU - Sallie, Samantha
AU - Casero, Violeta
AU - Cao, Chunyan
AU - Li, Dianyou
AU - Sun, Bomin
AU - Zhan, Shikun
AU - Voon, Valerie
TI - Adaptive shifts in amygdala-hippocampal theta coupling govern aversive learning and extinction
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/10
VL - 17
IS - 1
SP - 7835
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73967-4
UR - https://doi.org/10.1038/s41467-026-73967-4
LA - en
ER -

CSL-JSON

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"title": "Adaptive shifts in amygdala-hippocampal theta coupling govern aversive learning and extinction",
"container-title": "Nature communications",
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