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Traveling waves in the human visual cortex: An MEG-EEG model-based approach

Code ↔ Paper

6 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 6 matches
  1. [1] § Materials and methods › Comparison between predicted and measured data ↔ toolbox/comparison.py, lines 87–144 · score 0.78 · Fast Fourier transform, phase shift, correlation coefficient, model predictions, FFT, global
  2. [2] § Results › Differences of model performance across conditions ↔ 1_code_figures.zip/plot_fig5.rmd, lines 125–187 · score 0.56 · post hoc, linear mixed model, interactions, crossed, matched, GRAD
  3. [3] § Materials and methods › Stimuli for the MEG-EEG sessions ↔ toolbox/entry.py, lines 8–32 · score 0.55 · temporal frequency, traveling wave, offset, cortex, mm, space
  4. [4] § Results › The model is specific to conditions and wave parameters ↔ 1_code_figures.zip/plot_fig5.rmd, lines 125–187 · score 0.55 · Post hoc, linear mixed model, interaction, cross, match, GRAD
  5. [5] § Materials and methods › Stimuli for the MEG-EEG sessions ↔ toolbox/utils.py, lines 23–40 · score 0.54 · initial phase, temporal frequency, offset, amplitude, traveling, wave
  6. [6] § Materials and methods › Stimuli for the MEG-EEG sessions ↔ toolbox/simulation.py, lines 48–56 · score 0.53 · magnification inverse, E2, M0, eccentricity

Paper

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The authors' code

R Markdown · 188 lines · 8.1 KB · CC-BY-4.0 · 2 matches

  1. ---
  2. title: "plot_fig5"
  3. author: "LGrabot"
  4. date: "16/10/2024"
  5. output:
  6. pdf_document: default
  7. html_document: default
  8. ---
  10. ```{r setup, include=FALSE}
  11. knitr::opts_chunk$set(echo = TRUE)
  13. library(lme4)
  14. library(ggplot2)
  15. library(reshape2)
  16. library(nlme)
  18. # function sem
  19. sem <- function(x) sdt=sd(x)/sqrt(length(x))
  20. ```
  22. ## Test for frequency specificity (Figure 5A)
  24. Read correlation coefficient data (R2)
  25. subject: 19 subjects
  26. comparison: matched/crossed
  27. condition of the measured data: all (trav_out/stand/trav_in) concatenated
  28. channel: mag/grad/eeg
  29. temporal frequency: 3,4,5,6,7
  30. spatial frequency: 0.01, 0.05,0.1
  32. ```{r}
  33. # Read data
  34. figpath <- 'results\\figures\\' # to save figures
  35. datapath <- 'data\\' # to load the data
  36. d = read.csv(paste(datapath, 'Pipeline6_frequencySpecificity_V1modelComp_R2.csv', sep=""))
  38. # Subjects list
  39. subjects = c('EWTO6I','QNP39U','OF4IP5','XZJ7KI','03TPZ5', 'UEGW36', 'S1VW75', 'QFLDFC','JRRWDT','Q95PQG','90WCLR','TX0JPL','U2XPZV','575ZC9', 'D1AQHG','8KYLY7','O3YE19','DOYJLH','NOT7EZ')
  41. # Prepare data frame
  42. d$subject <- factor(d$subject)
  43. d$comparison <- factor(d$comparison)
  44. d$channel <- factor(d$channel)
  45. d$tempFreq <- factor(d$tempFreq)
  46. d$spatFreq <- factor(d$spatFreq)
  48. levels(d$comparison)[levels(d$comparison)=="c"] <- "crossed"
  49. levels(d$comparison)[levels(d$comparison)=="m"] <- "matched"
  50. levels(d$channel)[levels(d$channel)=="m"] <- "mag"
  51. levels(d$channel)[levels(d$channel)=="e"] <- "eeg"
  52. levels(d$channel)[levels(d$channel)=="g"] <- "grad"
  54. ```
  56. ```{r}
  58. a = aov(R2 ~ comparison*channel + comparison*tempFreq + comparison*spatFreq + Error(subject/(comparison*channel*tempFreq*spatFreq)),data=d)
  59. summary(a)
  61. plot_perFreq <- function (d,chan)
  62. {
  63. d_diff = subset(d, comparison == 'matched')
  64. d_diff$R2 = d$R2[d$comparison == 'matched'] - d$R2[d$comparison == 'crossed']
  65. d_diff = subset(d_diff, channel == chan) # change here the channel to get plot for mag, grad or eeg.
  66. m = aggregate(R2 ~ tempFreq + spatFreq + channel, d_diff, FUN = mean)
  67. std = aggregate(R2 ~ tempFreq + spatFreq + channel, d_diff, FUN = sem)
  68. m$std = std$R2
  70. # plot figure 5A
  71. theme <-theme(panel.background = element_rect(fill='white'), axis.line.y = element_line(colour = 'black'), axis.line.x = element_line(colour = 'black'),text = element_text(size=15, color='black'), axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))
  73. ysc <- scale_y_continuous(name = 'R2 (match-cross)',breaks=c(0,0.1,0.2), labels= as.character(c(0,0.1,0.2)), limits = c(-0.05, 0.25))
  75. f= ggplot(m, aes(factor(tempFreq), R2, fill = spatFreq)) + geom_bar(stat="identity", position = "dodge", colour='black') + geom_errorbar(aes(ymin=R2 - std, ymax=R2 + std), width=.2,position=position_dodge(.9)) + theme + ggtitle("R2") + ysc
  76. return(f)
  77. }
  79. # Plot for each sensor
  80. plot_perFreq(d,'mag')
  81. plot_perFreq(d,'grad')
  82. plot_perFreq(d,'eeg')
  84. ```
  86. ## R2 for each condition (Figure 5B)
  88. Read correlation coefficient data (R2) calculated in test_reref_cplxCorr.py
  89. R2 (calculated on complex values for session 2)
  90. subject: 19 subjects
  91. comparison: matched/crossed
  92. condition of the measured data: trav_out/stand/trav_in
  93. condition for crossed comparison: trav_out/stand/trav_in
  94. channel: mag/grad/eeg
  97. ```{r}
  98. # Read and prepare data frame
  99. figpath <- 'results\\figures\\' # to save figures
  100. datapath <- 'data\\' # to load the data
  101. d = read.csv(paste(datapath, 'Pipeline6_eachCondSepara_V1modelComp_R2.csv', sep=""))
  103. d$subject <- factor(d$subject)
  104. d$dataset <- factor(d$dataset)
  105. d$cross_cond <- factor(d$cross_cond)
  106. d$channel <- factor(d$channel)
  108. levels(d$dataset)[levels(d$dataset)=="i"] <- "travIN"
  109. levels(d$dataset)[levels(d$dataset)=="o"] <- "travOUT"
  110. levels(d$dataset)[levels(d$dataset)=="s"] <- "stand"
  111. levels(d$cross_cond)[levels(d$cross_cond)=="i"] <- "travIN"
  112. levels(d$cross_cond)[levels(d$cross_cond)=="o"] <- "travOUT"
  113. levels(d$cross_cond)[levels(d$cross_cond)=="s"] <- "stand"
  114. levels(d$channel)[levels(d$channel)=="m"] <- "mag"
  115. levels(d$channel)[levels(d$channel)=="e"] <- "eeg"
  116. levels(d$channel)[levels(d$channel)=="g"] <- "grad"
  118. # create dataset with matched and crossed
  119. d2 = d
  120. d2$comparison <- ifelse(d2$dataset == d2$cross_cond, "matched", "crossed")
  121. d2$comparison <- factor(d2$comparison)
  123. ```
  125. Linear mixed model. An effect of comparisons is expected, and we want to test
  126. the interactions between comparisons, dataset and cross_cond
  127. ```{r}
  129. a = aov(R2 ~ comparison*dataset*channel + Error(subject/(comparison*dataset*channel)),data=d2)
  130. summary(a)
  132. # Post hoc t-test on dataset
  133. dbis = aggregate(R2 ~ dataset + subject, d, FUN=mean)
  134. t.test(d2$R2[d2$dataset == 'travIN'], d2$R2[d2$dataset == 'travOUT'], paired=T)
  135. t.test(d2$R2[d2$dataset == 'travIN'], d2$R2[d2$dataset == 'stand'], paired=T)
  136. t.test(d2$R2[d2$dataset == 'stand'], d2$R2[d2$dataset == 'travOUT'], paired=T)
  138. # Post hoc t-test on comparison*dataset
  139. dbis = aggregate(R2 ~ comparison + dataset + subject + channel, d2, FUN=mean)
  140. t.test(dbis$R2[dbis$comparison == 'matched' & dbis$dataset == 'travIN'], dbis$R2[dbis$comparison == 'crossed' & dbis$dataset == 'travIN'], paired=T)
  141. t.test(dbis$R2[dbis$comparison == 'matched' & dbis$dataset == 'travOUT'], dbis$R2[dbis$comparison == 'crossed' & dbis$dataset == 'travOUT'], paired=T)
  142. t.test(dbis$R2[dbis$comparison == 'matched' & dbis$dataset == 'stand'], dbis$R2[dbis$comparison == 'crossed' & dbis$dataset == 'stand'], paired=T)
  144. # Post hoc t-test on comparison*dataset*channel
  145. chan = 'eeg' # change here to plot "eeg", "mag" or "grad"
  146. t.test(dbis$R2[dbis$comparison == 'matched' & dbis$dataset == 'travIN'& dbis$channel == chan], dbis$R2[dbis$comparison == 'crossed' & dbis$dataset == 'travIN'& dbis$channel == chan], paired=T)
  147. t.test(dbis$R2[dbis$comparison == 'matched' & dbis$dataset == 'travOUT'& dbis$channel == chan], dbis$R2[dbis$comparison == 'crossed' & dbis$dataset == 'travOUT'& dbis$channel == chan], paired=T)
  148. t.test(dbis$R2[dbis$comparison == 'matched' & dbis$dataset == 'stand'& dbis$channel == chan], dbis$R2[dbis$comparison == 'crossed' & dbis$dataset == 'stand'& dbis$channel == chan], paired=T)
  150. # create dataset with cond1/cond2 for cross conditions
  151. d_matched = subset(d2, comparison == 'matched')
  152. d_diff = subset(d2, comparison == 'crossed')
  154. d_diff$crossCd = factor(d_diff,c('cond1', 'cond2'))
  155. d_diff$crossCd[d_diff$dataset == 'travIN' & d_diff$cross_cond == 'travOUT'] = 'cond1'
  156. d_diff$crossCd[d_diff$dataset == 'travIN' & d_diff$cross_cond == 'stand'] = 'cond2'
  157. d_diff$crossCd[d_diff$dataset == 'travOUT' & d_diff$cross_cond == 'travIN'] = 'cond1'
  158. d_diff$crossCd[d_diff$dataset == 'travOUT' & d_diff$cross_cond == 'stand'] = 'cond2'
  159. d_diff$crossCd[d_diff$dataset == 'stand' & d_diff$cross_cond == 'travOUT'] = 'cond1'
  160. d_diff$crossCd[d_diff$dataset == 'stand' & d_diff$cross_cond == 'travIN'] = 'cond2'
  162. d_diff = subset(d_diff, select=c('R2', 'subject', 'dataset', 'crossCd','channel'))
  163. d_diff$R2[d_diff$crossCd == 'cond1'] = d2$R2[d2$comparison == 'matched'] - d_diff$R2[d_diff$crossCd == 'cond1']
  164. d_diff$R2[d_diff$crossCd == 'cond2'] = d2$R2[d2$comparison == 'matched'] - d_diff$R2[d_diff$crossCd == 'cond2']
  166. plot_perCond <- function (d_diff,chan)
  167. {
  168. dd = subset(d_diff, channel == chan)
  169. m = aggregate(R2 ~ dataset + crossCd , dd, FUN = mean)
  170. std = aggregate(R2 ~ dataset + crossCd , dd, FUN = sem)
  171. m$std = std$R2
  173. # plot
  174. theme <-theme(panel.background = element_rect(fill='white'), axis.line.y = element_line(colour = 'black'), axis.line.x = element_line(colour = 'black'),text = element_text(size=15, color='black'), axis.text.x=element_text(colour="black"), axis.text.y=element_text(colour="black"))
  176. ysc <- scale_y_continuous(name = 'R2 (match-cross)',breaks=c(-0.1, 0,0.1,0.2), labels= as.character(c(-0.1, 0,0.1,0.2)), limits = c(-0.11, 0.21))
  178. f=ggplot(m, aes(factor(dataset), R2, fill = crossCd)) + geom_bar(stat="identity", position = "dodge", colour='black') + geom_errorbar(aes(ymin=R2 - std, ymax=R2 + std), width=.2,position=position_dodge(.9)) + theme + ggtitle("R2") + ysc + ggtitle(chan)
  179. return(f)
  180. }
  182. # Plot for each channel
  183. plot_perCond(d_diff,'mag')
  184. plot_perCond(d_diff,'grad')
  185. plot_perCond(d_diff,'eeg')
  187. ```

plot_fig5.rmd, under CC-BY-4.0 · at the source

Overview

Authors: Laetitia Grabot1,2, Garance Merholz1, Jonathan Winawer3,4, David J. Heeger3,4, Laura Dugué1,5
ORCID iDs: Laetitia Grabot, Laura Dugué
  1. Université Paris Cité, CNRS, Integrative Neuroscience and Cognition Center, Paris, France
  2. Laboratoire des Systèmes Perceptifs, Département d’études Cognitives, École normale supérieure, PSL University, CNRS, Paris, France
  3. Department of Psychology, New York University, New York, New York, United States of America
  4. Center for Neural Science, New York University, New York, New York, United States of America
  5. Institut Universitaire de France (IUF), Paris, France
Institutions: Centre National de la Recherche Scientifique (France); Université Paris Cité (France); Université Paris Sciences et Lettres (France); École Normale Supérieure - PSL (France); Laboratoire des Systèmes Perceptifs (France); Centre Neurosciences intégratives et Cognition (France); New York University (United States); Institut Universitaire de France (France)
Journal: —, volume 21, issue 4, article e1013007
Dates: received 23 October 2024; accepted 27 March 2025; published online 17 April 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1371/journal.pcbi.1013007 · PMCID PMC12037073 · OpenAlex W4409560851
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), fMRI (modality), MEG (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Source localization, Physiology & signal measures
MeSH: Electroencephalography*, Magnetoencephalography*, Models, Neurological*, Visual Cortex*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Journal subjects: Physical Sciences, Physics, Waves, Traveling Waves, Research and Analysis Methods, Bioassays and Physiological Analysis, Electrophysiological Techniques, Brain Electrophysiology, Electroencephalography, Biology and Life Sciences, Physiology, Electrophysiology, Neurophysiology, Neuroscience, Brain Mapping, Medicine and Health Sciences, Clinical Medicine, Clinical Neurophysiology, Imaging Techniques, Neuroimaging, Cognitive Science, Cognitive Psychology, Perception, Sensory Perception, Psychology, Social Sciences, Sensory Physiology, Visual System, Sensory Systems, Vision, Engineering and Technology, Equipment, Measurement Equipment, Magnetometers, Functional Magnetic Resonance Imaging, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Radiology and Imaging
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (852139)
Citations: cited by 15 papers (Europe PMC); 67 references in the paper
Research resources: a Nipype 1.5.1 version [50 RRID:SCR_002502, RRID:SCR_016216

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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Zenodo 13968952

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 18 files
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), MNE-Python (5 files), Matplotlib (2 files), SciPy (2 files), ggplot2 (1 file), lme4 (1 file), NiBabel (1 file), nlme (1 file), Numba (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
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12 files
At the source: doi.org/10.5281/zenodo.13968952

LaetitiaG/wavesmodel

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Evidence: files inventoried
Commit: 6ed191cb443b0801364178efc1dabc263ff2f3fc, 19 May 2025
Languages: Python (9)
Size: 21 files, 9 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, environment (environment.yml, pyproject.toml), continuous integration
Not found: license file, CITATION.cff, tests, documentation
Tools: NumPy (5 files), MNE-Python (4 files), SciPy (2 files), Matplotlib (1 file), NiBabel (1 file), Numba (1 file)
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Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 11 MeSH terms, 1 funder, 62 references, 2 RRIDs.

Cite

This paper

Grabot, L., Merholz, G., Winawer, J., Heeger, D. J., & Dugué, L. (2025). Traveling waves in the human visual cortex: An MEG-EEG model-based approach. PLOS Computational Biology, 21(4), e1013007. https://doi.org/10.1371/journal.pcbi.1013007

BibTeX

@article{grabot2025traveling,
author = {Grabot, Laetitia and Merholz, Garance and Winawer, Jonathan and Heeger, David J. and Dugué, Laura},
title = {{Traveling waves in the human visual cortex: An MEG-EEG model-based approach}},
journal = {PLOS Computational Biology},
year = {2025},
volume = {21},
number = {4},
pages = {e1013007},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1013007},
url = {https://doi.org/10.1371/journal.pcbi.1013007},
pmcid = {PMC12037073}
}

RIS

TY - JOUR
AU - Grabot, Laetitia
AU - Merholz, Garance
AU - Winawer, Jonathan
AU - Heeger, David J.
AU - Dugué, Laura
TI - Traveling waves in the human visual cortex: An MEG-EEG model-based approach
T2 - PLOS Computational Biology
J2 - PLoS Comput Biol
PY - 2025
DA - 2025
VL - 21
IS - 4
SP - e1013007
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1013007
UR - https://doi.org/10.1371/journal.pcbi.1013007
LA - en
ER -

CSL-JSON

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"id": "10.1371/journal.pcbi.1013007",
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"title": "Traveling waves in the human visual cortex: An MEG-EEG model-based approach",
"container-title": "PLOS Computational Biology",
"author": [
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"given": "Laetitia"
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{
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"container-title-short": "PLoS Comput Biol",
"volume": "21",
"issue": "4",
"page": "e1013007",
"DOI": "10.1371/journal.pcbi.1013007",
"PMCID": "PMC12037073",
"ISSN": "1553-734X",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pcbi.1013007",
"language": "en",
"issued": {
"date-parts": [
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2025
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}

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