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A novel music-based real-time fMRI neurofeedback interface modulates interhemispheric connectivity and enhances mood.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Statistical analysis › Behavioral data ↔ scripts/behavioral/POMS_Scoring.ipynb, lines 227–261 · score 0.67 · Wilcoxon signed rank, POMS scores, Behavioral
  2. [2] § Methods › Paradigms ↔ interface/tbvinterface/Main_BOLD.py, lines 166–224 · score 0.61 · beep sound, white noise, correlation, feedback, imagery, music
  3. [3] § Methods › Statistical analysis › Behavioral data ↔ scripts/behavioral/Localizer_Report.ipynb, lines 52–80 · score 0.57 · Mann Whitney, unpleasant, inside, chord, localizer, Behavioral
  4. [4] § Methods › Paradigms ↔ scripts/behavioral/Localizer_Report.ipynb, lines 52–80 · score 0.52 · slightly unpleasant, neutral, pleasantness, chords, localizer, music

Paper

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

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The authors' code

Jupyter notebook · 80 lines · 2 KB · GPL-3.0 · 2 matches

  1. # %% [markdown]
  2. # # Pleasantness ratings aquired during the localizer
  4. # %%
  5. import os
  6. import pandas as pd
  7. from src.my_settings import settings
  9. sett = settings()
  11. # %%
  12. csv_path = os.path.join(sett["git_path"], "data", "psychopy")
  14. # find all csv files in csv_path
  15. files = [f for f in os.listdir(csv_path) if f.endswith(".csv")]
  17. # print the number of files found
  18. print(f"Found {len(files)} csv files in {csv_path}")
  20. # %%
  21. # Iterate over all files and extract the relevant information
  23. # create empty dataframe to store all data
  24. df = pd.DataFrame()
  26. for file in files:
  27. # read in csv file
  28. data = pd.read_csv(os.path.join(csv_path, file))
  30. # extract relevant information
  31. data = data[["image_rating_idx", "music_samples", "participant"]]
  33. # remove rows with NaNs
  34. data = data.dropna()
  36. # extract the music type from the music_samples column by extracting the third element after spliting by '_'
  37. data["music_type"] = data["music_samples"].apply(lambda x: x.split("_")[2])
  39. # remove the music_samples column
  40. data = data.drop("music_samples", axis=1)
  42. # concat data to df
  43. df = pd.concat([df, data])
  45. # %%
  46. df
  48. # %%
  49. # find unique values for image_rating_idx
  50. df["image_rating_idx"].unique()
  52. # %%
  53. # | label: fig:behav-loc-report
  54. # # stat test
  55. from statannotations.Annotator import Annotator
  57. # violin plot the ratings for each music type (p and n)
  58. import seaborn as sns
  59. import matplotlib.pyplot as plt
  61. ax = sns.violinplot(
  62. x="music_type", y="image_rating_idx", data=df, palette="Set2", hue="music_type"
  63. )
  65. annotator = Annotator(
  66. ax, [("p", "n")], data=df, x="music_type", y="image_rating_idx", order=["p", "n"]
  67. )
  68. annotator.configure(test="Mann-Whitney", text_format="star", loc="inside")
  69. annotator.apply_and_annotate()
  71. # edit y ticks and labels
  72. plt.yticks(
  73. [1, 2, 3, 4, 5],
  74. ["Unpleasant", "Slightly unpleasant", "Neutral", "Slighly pleasant", "Pleasant"],
  75. )
  76. plt.ylabel("Participants' ratings")
  78. plt.xlabel("Chord type")
  79. plt.xticks([0, 1], ["Pleasant", "Unpleasant"])
  80. plt.show()

Localizer_Report.ipynb at commit 8885204, under GPL-3.0 · at the source

Overview

Authors: Alexandre Sayal1,2, João Pereira1,2,3, Bruno Direito1,2,4, Teresa Sousa1,2,5,6, Miguel Castelo-Branco1,2,3,5,6
ORCID iDs: Teresa Sousa
  1. Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
  2. University of Minho, Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal
  3. Department of Cognitive Neuroscience, Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
  4. Centre for Informatics and Systems (CISUC), University of Coimbra, Coimbra, Portugal
  5. Institute of Physiology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
  6. Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
Institutions: University of Coimbra (Portugal); University of Minho (Portugal); Maastricht University (Netherlands)
Journal: Frontiers in psychiatry, volume 17, article 1757213
Dates: received 29 November 2025; accepted 19 March 2026; published online 30 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fpsyt.2026.1757213 · PMID 42147020 · PMCID PMC13171857 · OpenAlex W7159764871
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Statistics, Preprocessing, fMRI & imaging, Spectral & time-frequency, Physiology & signal measures
Keywords: immersive, interface, music, neurofeedback, reward
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 59 references in the paper

Abstract

Introduction: Music is a universal language that transcends cultures and is deeply rooted in human evolutionary history. Its creation and appreciation recruit the limbic and reward systems, leading to the evocation of emotions ranging from happiness and sadness to tenderness and grief. Here, we investigate the potential of music as an interventional tool in a novel neurofeedback connectivity-based experiment.

Methods: This study proposes a musical interface for real-time functional magnetic resonance imaging neurofeedback that is adaptable to diverse experimental paradigms, namely the ones aiming at improving mood and other affective dimensions. Using a previously developed motor imagery connectivity-based approach, we evaluate its feasibility and efficacy by comparing the modulation of bilateral premotor cortex activity during functional runs with real versus sham (random) feedback in 22 healthy adults. We also assess its performance against a visual feedback interface. The experiment involves a 50-minute MRI session, including anatomical scans, a premotor cortex functional localizer run, and four neurofeedback runs (two with active feedback and two with sham feedback). Pre- and post-session questionnaires assess the neurobehavioral impact on mood, musical background (as a potential predictor of neurofeedback success), and subjective feedback experiences. During neurofeedback, participants perform motor imagery of finger-tapping, with feedback delivered as a dynamic, pre-validated chord progression that evolves or regresses based on the functional connectivity between left and right premotor cortex.

Results: We found that our implementation of music-based feedback was successful, with participants managing to modulate their own connectivity using the proposed interface. The modulation performance was similar for active and sham runs, possibly due to the power of music to boost neuromodulation, but the network recruitment was stronger for active neurofeedback, including in the insula, putamen, and target regions of interest. Behaviorally, we found a decrease in tension and an improvement in the overall mood of the participants after the session.

Discussion: When comparing our results to previous neurofeedback data with a visual interface, we found stronger brain activations, in particular in neurofeedback-relevant regions such as the insula and the putamen. This work shows that it is possible to directly modulate interhemispheric connectivity using a real-time functional magnetic resonance imaging musical interface with potential effects on mood and recruitment of saliency and learning networks.

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 4 matches between paragraphs and lines of code.

CIBIT-UC/musicnf-novelinterface

License: GPL-3.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 888520458bdf2cce02dffca2aef3cda88ad64ee4, 20 February 2026
Languages: Jupyter (33), Python (21), MATLAB (9)
Size: 146 files, 63 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, license file, environment (environment.yml, setup.py), tests, continuous integration, 34 notebooks
Not found: CITATION.cff, documentation
Tools: NumPy (9 files), Matplotlib (4 files), Nilearn (4 files), pandas (4 files), SciPy (3 files), seaborn (3 files), statannotations (2 files), Dcm2Bids (1 file), FSL (1 file), TemplateFlow (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
34 files
At the source: github.com/CIBIT-UC/musicnf-novelinterface

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;
  • 32 scripts, each with its path and the digest of its content;
  • 4 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

Data availability statement

All code used in this study is available in the GitHub repository (https://github.com/CIBIT-UC/musicnf-novelinterface). The dataset, formatted in BIDS, can be accessed at Zenodo (https://doi.org/10.5281/zenodo.14803374). This study was preregistered on OSF (https://doi.org/10.17605/OSF.IO/AHXNB).

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 56 references.

Cite

This paper

Sayal, A., Pereira, J., Direito, B., Sousa, T., & Castelo-Branco, M. (2026). A novel music-based real-time fMRI neurofeedback interface modulates interhemispheric connectivity and enhances mood. Frontiers in psychiatry, 17, 1757213. https://doi.org/10.3389/fpsyt.2026.1757213

BibTeX

@article{sayal2026novel,
author = {Sayal, Alexandre and Pereira, João and Direito, Bruno and Sousa, Teresa and Castelo-Branco, Miguel},
title = {{A novel music-based real-time fMRI neurofeedback interface modulates interhemispheric connectivity and enhances mood}},
journal = {Frontiers in psychiatry},
year = {2026},
month = apr,
volume = {17},
pages = {1757213},
publisher = {Frontiers Media SA},
issn = {1664-0640},
doi = {10.3389/fpsyt.2026.1757213},
url = {https://doi.org/10.3389/fpsyt.2026.1757213},
pmid = {42147020},
pmcid = {PMC13171857}
}

RIS

TY - JOUR
AU - Sayal, Alexandre
AU - Pereira, João
AU - Direito, Bruno
AU - Sousa, Teresa
AU - Castelo-Branco, Miguel
TI - A novel music-based real-time fMRI neurofeedback interface modulates interhemispheric connectivity and enhances mood
T2 - Frontiers in psychiatry
J2 - Front Psychiatry
PY - 2026
DA - 2026/04/30
VL - 17
SP - 1757213
SN - 1664-0640
PB - Frontiers Media SA
DO - 10.3389/fpsyt.2026.1757213
UR - https://doi.org/10.3389/fpsyt.2026.1757213
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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