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Dynamic fMRI networks of human emotion.

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Paper

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The authors' code

Python · 124 lines · 3.7 KB · MIT

  1. import tkinter as tk
  2. from PIL import Image, ImageTk
  4. import glob
  5. import subprocess
  6. import os
  7. import sys
  10. def saveICs (indices, path, f):
  11. outfile = path + f
  12. with open(outfile, 'w') as file:
  13. for item in indices:
  14. file.write("{},".format(item))
  16. def handler (event):
  17. if event.char=='r':
  18. removers[i]=1
  19. root.destroy()
  20. elif event.char=='k':
  21. keepers[i]=1
  22. root.destroy()
  24. def quit (event):
  25. sys.exit()
  27. def displayICs (path, root, removers, keepers, rem_indices, keep_indices):
  29. root.title("IC num" + str(i))
  31. # pick an image file you have .bmp .jpg .gif. .png
  32. # load the file and covert it to a Tkinter image object
  33. ICfile = path + "IC_" + str(i) + "_thresh.png"
  34. image1 = Image.open(ICfile)
  35. #image1 = image.resize((689, 557), Image.ANTIALIAS) #The (250, 250) is (height, width)
  36. image1 = ImageTk.PhotoImage(image1)
  38. ts = path + "t" + str(i) + ".png"
  39. timeseries = Image.open(ts)
  40. #timeseries1 = timeseries.resize((622,118), Image.ANTIALIAS)
  41. timeseries1 = ImageTk.PhotoImage(timeseries)
  43. fs = path + "f" + str(i) + ".png"
  44. power = Image.open(fs)
  45. power1 = ImageTk.PhotoImage(power)
  47. # get the image size
  48. wic = image1.width()
  49. hic = image1.height()
  50. wt = timeseries1.width()
  51. ht = timeseries1.height()
  52. # position coordinates of root 'upper left corner'
  53. x = 0
  54. y = 0
  56. # make the root window the size of the image
  57. root.geometry("%dx%d+%d+%d" % (wic+wt, hic, x, y))
  59. # root has no image argument, so use a label as a panel
  60. panel1 = tk.Label(root, image=image1)
  61. panel1.grid(row=0, column=0, rowspan=4)
  63. panel2 = tk.Label(root, image=timeseries1)
  64. panel2.grid(row=1, column=1, sticky="NW")
  66. panel3 = tk.Label(root, image=power1)
  67. panel3.grid(row=2, column=1, sticky="NW")
  69. # save the panel's image from 'garbage collection'
  70. panel1.image = image1
  71. panel2.image = timeseries1
  72. panel3.image = power1
  74. root.bind('<k>', handler)
  75. root.bind('<r>', handler)
  76. root.bind('<q>', quit)
  78. # start the event loop
  79. root.mainloop()
  81. rem_indices = [a for a, x in enumerate(removers) if x == 1]
  82. keep_indices = [a for a, x in enumerate(keepers) if x == 1]
  84. return rem_indices, keep_indices
  87. if __name__ == "__main__":
  88. print("Display Melodics v0.01 (09-2014)")
  89. print("(c) Niels Janssen, [email hidden]")
  90. print("")
  91. print("Usage:")
  92. print("Press 'r' to remove IC, press 'k' to keep IC, and 'q' to quit")
  93. print("Decisions are saved in files 'to_remove.txt' and 'to_keep.txt' for further use (e.g., fsl_regfilt)\n")
  95. path = os.getcwd() + "/"
  96. ICfiles = glob.glob(path + 'IC_*_thresh.png')
  97. num_ics = len(ICfiles)
  99. if num_ics==0:
  100. print("*** No ICs found!")
  101. print("*** Run program in 'report' folder of melodic output")
  102. print("*** e.g., '/subject01/out.ica/report/python /my_python_scripts/display_melodics.py'")
  103. sys.exit()
  105. removers = [0] * (num_ics+1)
  106. keepers = [0] * (num_ics+1)
  107. rem_indices = []
  108. keep_indices = []
  110. f_rem = "to_remove.txt"
  111. f_keep ="to_keep.txt"
  113. #num_ics=10
  114. print("*** Found %d ICs in folder %s\n" % (num_ics, path))
  115. nb = raw_input("*** Continue (y/n)?:")
  116. if nb=='y':
  117. for i in range(1,num_ics+1):
  118. root = tk.Tk()
  119. rem_indices, keep_indices = displayICs(path, root, removers, keepers, rem_indices, keep_indices)
  120. saveICs(rem_indices, path, f_rem)
  121. saveICs(keep_indices, path, f_keep)
  123. print("*** Written %s" % (path + f_rem))
  124. print("*** Written %s" % (path + f_keep))

display_melodics.py at commit a349c4a, under MIT · at the source

Overview

Authors: Niels Janssen1,2,3, Uriel KA Elvira1, Joost Janssen4,5,6, Theo GM van Erp7,8
ORCID iDs: Niels Janssen, Joost Janssen, Theo GM van Erp
  1. Department of Psychology, Universidad de la Laguna, La Laguna, Spain
  2. Institute of Biomedical Technologies, Universidad de La Laguna, La Laguna, Spain
  3. Institute of Neurosciences, Universidad de la Laguna, La Laguna, Spain
  4. Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, Madrid, Spain
  5. Ciber del Área de Salud Mental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
  6. Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
  7. Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, United States
  8. Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, United States
Institutions: Universidad de La Laguna (Spain); Hospital General Universitario Gregorio Marañón (Spain); University Medical Center Utrecht (Netherlands); Instituto de Investigación Sanitaria Gregorio Marañón (Spain); University of California, Irvine (United States)
Journal: eLife, volume 14, article RP106070
Dates: published online 28 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.106070 · PMID 42663321 · PMCID PMC13524378 · OpenAlex W4411540762
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Evoked potentials, fMRI & imaging, Preprocessing
Keywords: Human
MeSH: Brain*, Emotions*, Magnetic Resonance Imaging*, Nerve Net*, Adult, Brain Mapping, Female, Humans, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Ministerio de Ciencia e Innovación (PID2021-127611NB-I00)
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

The experience of emotions is that of dynamic, time-changing processes. Yet, many functional MRI (fMRI) studies of emotion average across time to focus on maps of static activations, overlooking the temporal dimension of emotional responses. In this study, we used time-resolved fMRI, group spatial independent component analysis (ICA), dual regression, and Gaussian curve fitting to examine both the spatial and temporal properties of whole-brain networks during a behavioral task. This task included trials that spanned over 25 s of watching short, emotionally evocative movie clips, making emotion-related decisions, and an intertrial rest period. We identified four whole-brain networks with unique spatial and temporal features that mapped onto different stages of the task. A network activated early in the course of the task included perceptual and affective evaluation regions, while two later networks supported semantic interpretation and decision-making, and a final network aligned with default mode activity. Both spatial and temporal properties of all four networks were modulated by the emotional content of the movie clips. Our findings extend current models of emotion by integrating temporal dynamics with large-scale network activity, offering a richer framework for understanding how emotions unfold across distributed circuits. Such temporal-spatial markers of emotional processing may prove valuable for identifying and tracking alterations in clinical populations.

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

Repositories

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

iamnielsjanssen/display_melodics

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a349c4a940c4757585d83d631a35199c902b0a9f, 23 August 2022
Languages: Python (1)
Size: 4 files, 1 script
Software Heritage: archived
Found in: the references
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Pillow (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
3 files
At the source: github.com/iamnielsjanssen/display_melodics

OSF xy43a

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 0 files, 0 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.17605/OSF.IO/XY43A

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 and analysis code supporting the findings of this study are publicly available on the Open Science Framework at https://doi.org/10.17605/OSF.IO/XY43A.

The following dataset was generated:

Janssen N. 2025. Dynamic fMRI networks of emotion. Open Science Framework.

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, pages, dates, 4 authors, 1 keyword, 10 MeSH terms, 1 funder, 76 references.

Cite

This paper

Janssen, N., Elvira, U. K., Janssen, J., & van Erp, T. G. (2026). Dynamic fMRI networks of human emotion. eLife, 14, RP106070. https://doi.org/10.7554/elife.106070

BibTeX

@article{janssen2026dynamic,
author = {Janssen, Niels and Elvira, Uriel KA and Janssen, Joost and van Erp, Theo GM},
title = {{Dynamic fMRI networks of human emotion}},
journal = {eLife},
year = {2026},
month = aug,
volume = {14},
pages = {RP106070},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.106070},
url = {https://doi.org/10.7554/elife.106070},
pmid = {42663321},
pmcid = {PMC13524378}
}

RIS

TY - JOUR
AU - Janssen, Niels
AU - Elvira, Uriel KA
AU - Janssen, Joost
AU - van Erp, Theo GM
TI - Dynamic fMRI networks of human emotion
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/08/28
VL - 14
SP - RP106070
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.106070
UR - https://doi.org/10.7554/elife.106070
LA - en
ER -

CSL-JSON

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"id": "10.7554/elife.106070",
"type": "article-journal",
"title": "Dynamic fMRI networks of human emotion",
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"author": [
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"family": "Janssen",
"given": "Niels"
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"family": "Elvira",
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{
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"given": "Joost"
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{
"family": "van Erp",
"given": "Theo GM"
}
],
"container-title-short": "eLife",
"volume": "14",
"page": "RP106070",
"DOI": "10.7554/elife.106070",
"PMID": "42663321",
"PMCID": "PMC13524378",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.106070",
"language": "en",
"issued": {
"date-parts": [
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