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Right posterior theta reflects human parahippocampal phase resetting by salient cues during goal-directed navigation

Code ↔ Paper

22 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 22 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Experiment 2: Simultaneous EEG-fMRI › Results › Single-trial right PHG activation is predicted by RPT power ↔ experiment2/data_fusion/eegfmri_st_lme_phg.R, lines 197–266 · score 0.83 · pPHG2, aPHG2, pPHG1, beta weights, right PHG, 5–6 Hz
  2. [2] § Experiment 2: Simultaneous EEG-fMRI › Results › Single-trial right PHG activation is predicted by RPT power ↔ experiment2/data_fusion/eegfmri_st_lme_phg.R, lines 197–266 · score 0.77 · pPHG2, aPHG2, pPHG1, beta weights, intercept, 5–6 Hz
  3. [3] § Experiment 2: Simultaneous EEG-fMRI › Methods › Integrated EEG-fMRI analysis ↔ experiment2/data_fusion/eegfmri_st_lme_phg.R, lines 268–297 · score 0.69 · beta weight, 11–12 Hz, right PHG, intercept, 1–4, 9–10
  4. [4] § Experiment 1: Asynchronous EEG-MEG › Methods › EEG and MEG intertrial coherence and phase analysis ↔ experiment1/meeg/tf_phase/itc_phase_analysis.m, lines 1–121 · score 0.67 · pop_newtimef, EEGLAB, pre, ITC, baseline, post
  5. [5] § Experiment 1: Asynchronous EEG-MEG › Results › Salient events align EEG and MEG phases across trials ↔ experiment1/figures/figure4_phase_distributions.py, lines 1–29 · score 0.66 · phase distribution, pre feedback, post feedback, circular, angles, ITC
  6. [6] § Experiment 2: Simultaneous EEG-fMRI › Methods › EEG data preprocessing and analysis ↔ experiment2/eeg/statistics/create_stats_df_eeg.py, lines 1–60 · score 0.63 · 50–350 ms, Maze, EEG
  7. [7] § Experiment 1: Asynchronous EEG-MEG › Methods › EEG and MEG intertrial coherence and phase analysis ↔ experiment1/figures/figure4_phase_distributions.py, lines 60–92 · score 0.62 · phase distribution, pre feedback, post feedback, angles, ITC, window
  8. [8] § Experiment 1: Asynchronous EEG-MEG › Methods › EEG and MEG data preprocessing ↔ experiment1/meeg/preprocessing/preprocessing_meg.py, lines 61–63 · score 0.61 · high pass filtered, high amplitude, copy, ICA, artifacts, MEG
  9. [9] § Experiment 2: Simultaneous EEG-fMRI › Methods › EEG data preprocessing and analysis ↔ experiment2/eeg/preprocessing/preprocessing.py, lines 12–15 · score 0.61 · ballistocardiac artifacts, Gradient artifacts, MNE, preprocessing, EEG
  10. [10] § Experiment 1: Asynchronous EEG-MEG › Methods › EEG and MEG data preprocessing ↔ experiment2/eeg/preprocessing/preprocessing.py, lines 62–65 · score 0.61 · high pass filtered, high amplitude, copy, ICA, artifacts, preprocessing
  11. [11] § Experiment 1: Asynchronous EEG-MEG › Results › The RPT effect is observable in EEG and MEG ↔ experiment1/meeg/statistics/create_stats_df_meg.py, lines 10–85 · score 0.61 · 100–250 ms, MEG, 100 ms, theta, peaking, channel
  12. [12] § Experiment 1: Asynchronous EEG-MEG › Results › The RPT effect is observable in EEG and MEG ↔ experiment1/figures/figure2_spectro_topo.py, lines 1–57 · score 0.60 · power change, 175 ms, 225 ms, Spectrograms, 600 ms, baseline
  13. [13] § Experiment 1: Asynchronous EEG-MEG › Methods › EEG and MEG intertrial coherence and phase analysis ↔ experiment1/figures/figure4_phase_stats.py, lines 57–87 · score 0.58 · resultant vector length, RVL, pre, Wilcoxon, angles, phase
  14. [14] § Experiment 1: Asynchronous EEG-MEG › Methods › EEG and MEG intertrial coherence and phase analysis ↔ experiment1/meeg/tf_phase/itc_phase_analysis.m, lines 1–121 · score 0.58 · CircStat, EEGLAB, MATLAB, circular, pre, post
  15. [15] § Experiment 2: Simultaneous EEG-fMRI › Methods › Integrated EEG-fMRI analysis ↔ experiment1/meeg/statistics/create_stats_df_meg.py, lines 10–85 · score 0.57 · 50–250 ms, beta, evoked, power, alleys
  16. [16] § Experiment 2: Simultaneous EEG-fMRI › Methods › Integrated EEG-fMRI analysis ↔ experiment2/data_fusion/eegfmri_st_lme_controlROIs.R, lines 113–182 · score 0.56 · 11–12 Hz, intercept, 1–4, 9–10, 5–6, 7–8
  17. [17] § Experiment 2: Simultaneous EEG-fMRI › Methods › EEG data preprocessing and analysis ↔ experiment2/eeg/preprocessing/preprocessing.py, lines 76–80 · score 0.52 · ICA components, rejected, EKG, correlation, signals, filtered
  18. [18] § Experiment 2: Simultaneous EEG-fMRI › Results › Electrophysiological evidence of the RPT effect in EEG-fMRI ↔ experiment2/behavior/behavior_stats.py, the whole file · a weak match · score 0.51 · Bonferroni Holm corrected, SD, ANOVA, Alley
  19. [19] § Experiment 1: Asynchronous EEG-MEG › Results › Salient events align EEG and MEG phases across trials ↔ experiment1/figures/figure4_phase_stats.py, lines 57–87 · score 0.51 · resultant vector, pre feedback, angles, Figure 4, phase, EEG
  20. [20] § Experiment 1: Asynchronous EEG-MEG › Results › Salient events align EEG and MEG phases across trials ↔ experiment1/figures/figure4_phase_distributions.py, lines 60–92 · score 0.51 · pre feedback, post feedback, Bar, Circular, phase, angles
  21. [21] § Experiment 2: Simultaneous EEG-fMRI › Methods › fMRI data preprocessing and analysis ↔ experiment2/fmri/preprocessing/preprocess.m, the whole file · a weak match · score 0.51 · SPM, preprocessing, slice, template, fMRI
  22. [22] § Experiment 1: Asynchronous EEG-MEG › Results › Salient events align EEG and MEG phases across trials ↔ experiment1/figures/figure4_phase_distributions.py, lines 1–29 · score 0.51 · pre feedback, post feedback, phase, angle, window, Figure 4

Paper

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

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The authors' code

Python · 104 lines · 4.3 KB · no license · 4 matches

  1. import pandas as pd
  2. from scipy.io import loadmat
  3. import seaborn as sns
  4. import numpy as np
  5. import matplotlib
  7. matplotlib.use('Qt5Agg')
  9. method = 'EEG'
  10. chan = 'PO8'
  11. measure = 2
  12. win = 2
  13. freq = 8
  15. path = 'file_directory'
  17. pal1 = sns.cubehelix_palette(10, rot=-.15, light=.7)
  18. pal2 = sns.cubehelix_palette(10, start=.4, rot=.15, light=.7)
  20. phaseStats = loadmat(path + '/phase_ITC_analysis/phaseResults_' + method + '_' + chan + '.mat')['allAngleData']
  21. circMean = loadmat(path + '/phase_ITC_analysis/circularMean_' + method + '_' + chan + '.mat')['All_Subject_Left_Right_Data8Hz']
  23. phaseStats = pd.DataFrame(phaseStats, columns=['subject', 'condition', 'frequency', 'window', 'angle'])
  24. phaseStats['window'][phaseStats['window'] == 1] = 'pre-feedback'
  25. phaseStats['window'][phaseStats['window'] == 2] = 'post-feedback'
  26. phaseStats = phaseStats.astype({'subject': int, 'condition': int, 'frequency': int, 'window': str, 'angle': float})
  28. circMean = pd.DataFrame(circMean, columns=['subject', 'condition', 'frequency', 'window', 'measure', 'angle'])
  29. circMean = circMean.astype({'subject': int, 'condition': int, 'frequency': int, 'window': int, 'measure': int, 'angle': float})
  31. ######
  33. font = {'family' : 'Arial',
  34. 'weight' : 'normal',
  35. 'size' : 13}
  37. matplotlib.rc('font', **font)
  40. for sub in np.arange(10,11):
  41. # Set up a grid of axes with a polar projection
  42. g = sns.FacetGrid(phaseStats[phaseStats['subject']==sub][phaseStats['frequency']==freq], col="window",
  43. hue="condition", subplot_kws=dict(projection='polar'), height=3.5,
  44. sharex=False, sharey=False, despine=False, palette=[pal2[4], pal1[4]])
  45. g.map(sns.distplot, "angle", rug=False, hist=True, kde=False, norm_hist=False, bins=20,
  46. hist_kws={"histtype": "bar", "fill": True, "alpha": .3})
  47. ax = g.axes
  48. ax[0][0].set_xlabel('')
  49. ax[0][1].set_xlabel('')
  50. ax[0][0].set_ylim([0,14])
  51. ax[0][1].set_ylim([0,25])
  52. #ax[0][0].legend(handles=legend_elements, loc='upper right')
  53. g.set_titles(col_template="{col_name} (EEG)", size=15)
  55. g.savefig(path + '/plots/figure4/polarplot_angle_sub' + str(sub) + '_' + str(freq) + 'Hz_' + chan +
  56. '.pdf', bbox_inches='tight')
  58. ######
  60. method = 'MEG'
  61. chan = 'poc'
  62. measure = 2
  63. win = 2
  64. freq = 8
  66. phaseStats = loadmat('H:\\MEGEEG_project\\phase_ITC_analysis\\phaseResults_' + method + '_' + chan + '.mat')['allAngleData']
  67. circMean = loadmat('H:\\MEGEEG_project\\phase_ITC_analysis\\circularMean_' + method + '_' + chan + '.mat')['All_Subject_Left_Right_Data8Hz']
  69. phaseStats = pd.DataFrame(phaseStats, columns=['subject', 'condition', 'frequency', 'window', 'angle'])
  70. phaseStats['window'][phaseStats['window'] == 1] = 'pre-feedback'
  71. phaseStats['window'][phaseStats['window'] == 2] = 'post-feedback'
  72. phaseStats = phaseStats.astype({'subject': int, 'condition': int, 'frequency': int, 'window': str, 'angle': float})
  74. circMean = pd.DataFrame(circMean, columns=['subject', 'condition', 'frequency', 'window', 'measure', 'angle'])
  75. circMean = circMean.astype({'subject': int, 'condition': int, 'frequency': int, 'window': int, 'measure': int, 'angle': float})
  77. for sub in np.arange(10,11):
  78. # Set up a grid of axes with a polar projection
  79. g = sns.FacetGrid(phaseStats[phaseStats['subject']==sub][phaseStats['frequency']==freq], col="window",
  80. hue="condition", subplot_kws=dict(projection='polar'), height=3.5,
  81. sharex=False, sharey=False, despine=False, palette=[pal2[4], pal1[4]])
  82. g.map(sns.distplot, "angle", rug=False, hist=True, kde=False, norm_hist=False, bins=20,
  83. hist_kws={"histtype": "bar", "fill": True, "alpha": .3})
  84. ax = g.axes
  85. ax[0][0].set_xlabel('')
  86. ax[0][1].set_xlabel('')
  87. ax[0][0].set_ylim([0,14])
  88. ax[0][1].set_ylim([0,20])
  89. g.set_titles(col_template="{col_name} (MEG)", size=15)
  91. g.savefig(path + '/plots/figure4/polarplot_angle_sub' + str(sub) + '_' + str(freq) + 'Hz_' + chan +
  92. '.pdf', bbox_inches='tight')
  94. ####
  96. from matplotlib.lines import Line2D
  97. import matplotlib.pyplot as plt
  99. legend_elements = [Line2D([0], [0], color=pal2[3], lw=7, label='left alley'),
  100. Line2D([0], [0], color=pal1[3], lw=7, label='right alley')]
  102. fig, ax = plt.subplots()
  103. ax.legend(legend_elements, ['left alley', 'right alley'])
  104. fig.savefig(path + '/plots/figure4/legend.pdf', bbox_inches='tight')

figure4_phase_distributions.py at commit b318701, no license · at the source

Overview

Authors: Malte R. Güth1,2, Andrew Reid3, Yu Zhang4, Sonja C. Huntgeburth5, Ravi D. Mill1, Alain Dagher5, Kim Kerns6, Clay B. Holroyd7, Michael Petrides5, Michael W. Cole1, Travis E. Baker1
ORCID iDs: Malte R. Güth, Andrew Reid, Yu Zhang, Ravi D. Mill, Alain Dagher, Clay B. Holroyd, Michael W. Cole, Travis E. Baker
  1. Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States
  2. Graduate Program in Neuroscience, Rutgers University, Newark, NJ, United States
  3. Department of Psychology, University of Tilburg, Tilburg, Netherlands
  4. Zhejiang Lab, Hangzhou, China
  5. Montréal Neurological Institute, McGill University, Montréal, Québec, Canada
  6. Department of Psychology, University of Victoria, Victoria, BC, Canada
  7. Department of Experimental Psychology, Ghent University, Ghent, Belgium
Institutions: Rutgers, The State University of New Jersey (United States); Tilburg University (Netherlands); Zhejiang Lab (China); Montreal Neurological Institute and Hospital (Canada); McGill University (Canada); University of Victoria (Canada); Ghent University (Belgium)
Journal: n/a, volume 3, article IMAG.a.105
Dates: received 11 February 2025; accepted 25 June 2025; published online 8 September 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1162/imag.a.105 · PMCID PMC12418113 · OpenAlex W4412717531
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), fMRI (modality), MEG (modality), human (organism), cognitive (subfield)
Keywords: spatial navigation, parahippocampal gyrus, theta oscillations, phase resetting, simultaneous EEG-fMRI
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Canadian Michael Smith Foundation; Rutgers Core Facility Utilization Grant; Rutgers Start-up; Graduate Program in Neuroscience
Citations: cited by 2 papers (Europe PMC); 138 references in the paper

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.

Repository

Its files are read in the Code ↔ Paper reader above, with 22 matches between paragraphs and lines of code.

BakerlabRutgers/phg_phase_resetting

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: b31870181c7801739170f3a7c9a740253325f58b, 24 April 2025
Languages: Python (35), MATLAB (12), R (7)
Size: 67 files, 54 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (30 files), pandas (20 files), Matplotlib (16 files), MNE-Python (15 files), SciPy (15 files), seaborn (10 files), reshape2 (6 files), SPM (6 files), tidyverse (6 files), statannotations (4 files), statsmodels (4 files), car (3 files), lme4 (3 files), Wavelet Toolbox (3 files), Nilearn (3 files), Pingouin (3 files), psych (3 files), ggplot2 (2 files), lavaan (2 files), NiBabel (2 files), CircStat (1 file), EEGLAB (1 file), lmerTest (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
55 files
At the source: github.com/BakerlabRutgers/phg_phase_resetting

The paper's code and data availability statement is in the Data section.

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  • 54 scripts, each with its path and the digest of its content;
  • 22 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1162/imag.a.105.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 11 authors, 5 keywords, 4 funders, 134 references.

Cite

This paper

Güth, M. R., Reid, A., Zhang, Y., Huntgeburth, S. C., Mill, R. D., Dagher, A., Kerns, K., Holroyd, C. B., Petrides, M., Cole, M. W., & Baker, T. E. (2025). Right posterior theta reflects human parahippocampal phase resetting by salient cues during goal-directed navigation. Imaging Neuroscience, 3, IMAG.a.105. https://doi.org/10.1162/imag.a.105

BibTeX

@article{guth2025right,
author = {Güth, Malte R. and Reid, Andrew and Zhang, Yu and Huntgeburth, Sonja C. and Mill, Ravi D. and Dagher, Alain and Kerns, Kim and Holroyd, Clay B. and Petrides, Michael and Cole, Michael W. and Baker, Travis E.},
title = {{Right posterior theta reflects human parahippocampal phase resetting by salient cues during goal-directed navigation}},
journal = {Imaging Neuroscience},
year = {2025},
volume = {3},
pages = {IMAG.a.105},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.105},
url = {https://doi.org/10.1162/imag.a.105},
pmcid = {PMC12418113}
}

RIS

TY - JOUR
AU - Güth, Malte R.
AU - Reid, Andrew
AU - Zhang, Yu
AU - Huntgeburth, Sonja C.
AU - Mill, Ravi D.
AU - Dagher, Alain
AU - Kerns, Kim
AU - Holroyd, Clay B.
AU - Petrides, Michael
AU - Cole, Michael W.
AU - Baker, Travis E.
TI - Right posterior theta reflects human parahippocampal phase resetting by salient cues during goal-directed navigation
T2 - Imaging Neuroscience
J2 - Imaging Neurosci (Camb)
PY - 2025
DA - 2025
VL - 3
SP - IMAG.a.105
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.105
UR - https://doi.org/10.1162/imag.a.105
LA - en
ER -

CSL-JSON

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