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Exploring the mechanisms of biofield therapy through joint electrophysiological recordings in humans and mice.

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Paper

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The authors' code

MATLAB · 58 lines · 1.9 KB · no license

  1. % simple coherence, pairwise between all channels of 2 datasets
  3. function [cohSelected,freqs] = simple_coherence(EEG1, EEG2, freqBands)
  5. if size(EEG1.data,2) ~= size(EEG2.data,2) || size(EEG1.data,3) ~= size(EEG2.data,3)
  6. error('The data must be synchronous')
  7. end
  9. if ~isequal(EEG1.srate, EEG2.srate)
  10. error('The sampling rate must be the same')
  11. end
  13. if length(EEG1.event) ~= length(EEG2.event)
  14. error('The event structure must have the same number of events')
  15. end
  17. if EEG1.trials == 1
  18. % create data epochs of winSize (truncate the end of data)
  19. EEG1 = eeg_regepochs(EEG1, 1, [0 1]);
  20. EEG2 = eeg_regepochs(EEG2, 1, [0 1]);
  21. if size(EEG1.data,2) ~= size(EEG2.data,2) || size(EEG1.data,3) ~= size(EEG2.data,3)
  22. error('Error when extracting epochs. The two datasets should have the same number of epochs')
  23. end
  24. end
  26. % compute spectral decomposition
  27. win = hanning(EEG1.pnts);
  28. data1 = bsxfun(@times, EEG1.data, win');
  29. data2 = bsxfun(@times, EEG2.data, win');
  31. datafft1 = fft( data1, [], 2);
  32. datafft2 = fft( data2, [], 2);
  33. freqs = linspace(0, EEG1.srate-1, size(datafft1,2));
  35. datafft1(:,end/2:end,:) = [];
  36. datafft2(:,end/2:end,:) = [];
  37. freqs(end/2:end) = [];
  39. coh = zeros(EEG1.nbchan, EEG2.nbchan, size(datafft2,2));
  40. for iChan1 = 1:EEG1.nbchan
  41. for iChan2 = 1:EEG2.nbchan
  42. % coherres = sum(alltfX .* conj(alltfY) , 3) ./ sqrt( sum(abs(alltfX ).^2,3) .* sum(abs(alltfY ).^2,3) ); % from newcrossf
  43. coh(iChan1, iChan2, :) = sum(datafft1(iChan1,:,:).* conj(datafft2(iChan2,:,:)), 3) ./ sqrt( sum(abs(datafft1(iChan1,:,:)).^2,3) .* sum(abs(datafft2(iChan2,:,:)).^2,3) );
  44. end
  45. end
  47. if nargin < 3
  48. cohSelected = coh;
  49. return
  50. end
  52. % select frequencies
  53. cohSelected = zeros(EEG1.nbchan, EEG2.nbchan, length(freqBands));
  54. for iFreq = 1:length(freqBands)
  55. [~,minFreq] = min(abs(freqs-freqBands{iFreq}(1)));
  56. [~,maxFreq] = min(abs(freqs-freqBands{iFreq}(2)));
  57. cohSelected(:,:,iFreq) = mean( coh(:, :, minFreq:maxFreq), 3 );
  58. end

simple_coherence.m at commit 5166416, no license · at the source

Overview

Authors: Arnaud Delorme1,2, Andrew Cusimano3, Megan Tran3, Phuong Nguyen3, Defeng Deng3, Chris Fields4, Libor Velíšek5, Richard Wagner3, Peiying Yang3, Lorenzo Cohen3
ORCID iDs: Arnaud Delorme, Lorenzo Cohen
  1. Institute of Noetic Sciences, Petaluma, CA, USA
  2. University of California, La Jolla, San Diego, CA, USA
  3. The University of Texas MD Anderson Cancer Center, Houston, TX, USA
  4. Independent Researcher, 11160 Caunes Minervois, France
  5. New York Medical College, Valhalla, NY, USA
Institutions: Institute of Noetic Sciences (United States); The University of Texas MD Anderson Cancer Center (United States); New York Medical College (United States)
Journal: IBRO neuroscience reports, volume 21, pages 52-62
Dates: received 14 October 2025; accepted 26 May 2026; published online 28 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.ibneur.2026.05.011 · PMID 42305856 · PMCID PMC13266234 · OpenAlex W7162678007
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), mouse (organism), other condition (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Connectivity, Preprocessing, Physiology & signal measures
Keywords: Biofield Therapy (BT), Mice, Double-blind, Electroencephalogram (EEG)
Topic: Biofield Effects and Biophysics (Physiology, Medicine), according to OpenAlex
Funding: Emerald Gate Charitable Trust (RCTS LS2022−00061373-LK)
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

In this case study, a self-described biofield therapy (BT) therapist and a sham therapist participated in multiple (n = 24) treatment and control (non-treatment) sessions under double-blind conditions. During the treatment phases, BT and sham therapists attempted to influence mice with cancer and control mice, alternating BT with rest phases where no such efforts were made. Both the 64-channel EEG of the human participants and the simultaneous 3-channel EEG and 1-channel EMG of the mice were recorded. For human participants and for the analysis of human and mouse EEG comodulation, the EEG experimental setup was a 2 × 2 design, contrasting mouse type (cancer vs. control) against session type (BT vs. non-treatment; N = 8 in each of the four groups). For the mice EEG, the experimental setup was a 2x2x2 design, contrasting mouse type (cancer vs. control) against session type (treatment vs. non-treatment) and human participant (BT participant vs. sham participant). Although no changes in spectral power were detected in mice, a significant increase in theta band coherence indicates that this type of biofield therapy may influence large-scale neural coordination rather than localized activity. Concurrently, robust and reproducible alterations in the therapist’s EEG across all frequency bands during treatment periods, irrespective of mouse condition, suggest a consistent physiological signature associated with the act of intentional BT. Treatment was associated with changes in EEG coherence and spectral correlation between human and mouse signals. In particular, we observed an interaction in which treatment differentially affected brain-to-brain coherence in cancer versus control mice. These findings describe condition-dependent alterations in coupled physiological measures and suggest a complex relationship between human and mouse neural activity during BT sessions, while remaining agnostic about the underlying mechanism. We also outline the study's limitations and potential for follow-up investigations, acknowledging that these exploratory physiological findings do not have any clinical implications and should not be interpreted as justification for cancer treatment or as a substitute for evidence-based medical care.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

OSF n2gwj

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file, 0 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: osf.io/n2gwj/

arnodelorme/simple_coherence

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 516641633955da30e595bb5eda02cbf1fabe7504, 23 April 2026
Languages: MATLAB (1)
Size: 1 file, 1 script
Software Heritage: not archived
Found in: the text, “Statistical analysis”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
1 file
At the source: github.com/arnodelorme/simple_coherence

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 was converted to the BIDS EEG format (Pernet et al., 2018) using the EEG-BIDS plugin of the EEGLAB software (Delorme and Makeig, 2004). It is large (350 Gb) and available upon request.

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 Arnaud Delorme (0000-0002-0799-3557); Lorenzo Cohen (0000-0003-4372-9208); removed Arnaud Delorme; Lorenzo Cohen

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 10 authors, 4 keywords, 1 funder, 35 references.

Cite

This paper

Delorme, A., Cusimano, A., Tran, M., Nguyen, P., Deng, D., Fields, C., Velíšek, L., Wagner, R., Yang, P., & Cohen, L. (2026). Exploring the mechanisms of biofield therapy through joint electrophysiological recordings in humans and mice. IBRO neuroscience reports, 21, 52-62. https://doi.org/10.1016/j.ibneur.2026.05.011

BibTeX

@article{delorme2026exploring,
author = {Delorme, Arnaud and Cusimano, Andrew and Tran, Megan and Nguyen, Phuong and Deng, Defeng and Fields, Chris and Velíšek, Libor and Wagner, Richard and Yang, Peiying and Cohen, Lorenzo},
title = {{Exploring the mechanisms of biofield therapy through joint electrophysiological recordings in humans and mice}},
journal = {IBRO neuroscience reports},
year = {2026},
month = may,
volume = {21},
pages = {52--62},
publisher = {Elsevier},
issn = {2667-2421},
doi = {10.1016/j.ibneur.2026.05.011},
url = {https://doi.org/10.1016/j.ibneur.2026.05.011},
pmid = {42305856},
pmcid = {PMC13266234}
}

RIS

TY - JOUR
AU - Delorme, Arnaud
AU - Cusimano, Andrew
AU - Tran, Megan
AU - Nguyen, Phuong
AU - Deng, Defeng
AU - Fields, Chris
AU - Velíšek, Libor
AU - Wagner, Richard
AU - Yang, Peiying
AU - Cohen, Lorenzo
TI - Exploring the mechanisms of biofield therapy through joint electrophysiological recordings in humans and mice
T2 - IBRO neuroscience reports
J2 - IBRO Neurosci Rep
PY - 2026
DA - 2026/05/28
VL - 21
SP - 52
EP - 62
SN - 2667-2421
PB - Elsevier
DO - 10.1016/j.ibneur.2026.05.011
UR - https://doi.org/10.1016/j.ibneur.2026.05.011
LA - en
ER -

CSL-JSON

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"family": "Delorme",
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"PMCID": "PMC13266234",
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