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Diazepam alters the shape of alpha oscillations recorded from human cortex using EEG.

Code ↔ Paper

13 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 13 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Data analysis › Spatial filtering & component identification › Spectral analysis and parameterisation ↔ code/spect_1_get_plot_pwr.py, lines 17–72 · score 0.83 · peak width limits, 2–40 Hz, peak height, epochs, specparam, r2
  2. [2] § Methods › Data analysis › Spatial filtering & component identification › Spectral analysis and parameterisation ↔ code/base/helper.py, lines 372–420 · score 0.81 · peak width limits, 2–40 Hz, peak height, epochs, spectra, aperiodic
  3. [3] § Methods › Data analysis › Waveform shape analysis ↔ code/emd_1_run_inspect.py, lines 54–119 · score 0.74 · mask_sift, masking frequencies, transform, amplitude, IMFs, EMD
  4. [4] § Methods › Data analysis › EEG preprocessing ↔ code/preproc/preprocessing.py, lines 16–63 · score 0.72 · 1–100 Hz, bad channels, EEGLAB, MNE, preprocessing, raw
  5. [5] § Methods › Data analysis › Spatial filtering & component identification › Template matching of SSD components ↔ code/ssd_4_ssd_srcs.py, lines 44–93 · score 0.72 · absolute cosine distance, forward model, MNE, location, position, lobe
  6. [6] § Methods › Data analysis › Waveform shape analysis ↔ code/emd_1_run_inspect.py, lines 1–52 · score 0.65 · phase aligned instantaneous, SSD component, IMF, EMD, band, cycles
  7. [7] § Methods › Statistical analysis › Spectral data ↔ code/stats/spect/GLMM_spect.R, the whole file · a weak match · score 0.61 · skew normal, imputed, mice, chained, GLMM, models
  8. [8] § Methods › Statistical analysis › Bayesian estimation ↔ code/stats/PC/emmeans_analysis.R, lines 1–47 · score 0.58 · Chain convergence, brms, emmeans, ROPE, GLMMs, models
  9. [9] § Methods › Data analysis › Waveform shape analysis ↔ code/Fig_S1.py, lines 39–51 · score 0.57 · split half, principal component, validate, S1
  10. [10] § Methods › Statistical analysis › Shape data ↔ code/Fig_S3_4.py, lines 64–146 · score 0.57 · von Mises, PC scores, lobes, Post, Pre
  11. [11] § Methods › Data analysis › Spatial filtering & component identification › Spatio-spectral decomposition (SSD) ↔ code/ssd_4_ssd_srcs.py, lines 95–149 · score 0.57 · template matching, forward model, SNR, filters, SSD, band
  12. [12] § Methods › Statistical analysis › Bayesian estimation ↔ code/stats/spect/emmeans_analysis_spect.R, the whole file · a weak match · score 0.56 · brms, emmeans, HDI, Posterior, ROPE, models
  13. [13] § Methods › Data analysis › EEG preprocessing ↔ code/emd_1_run_inspect.py, lines 1–52 · score 0.56 · phase aligned instantaneous, alpha band, EMD, cycle, EEG, SSD

Paper

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

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The authors' code

Python · 124 lines · 4.6 KB · no license · 3 matches

  1. # -*- coding: utf-8 -*-
  2. """
  3. Created on Wed Sep 25 09:04:47 2024
  5. @author: a1147969
  7. script cycles through ssd components, runs EMD, displays outcome for alpha band by plotting IMF relative to
  8. SSD timeseries, allowing visual inspection. Well fit data are then subject to cycle anlysis and extraction
  9. of phase-aligned instantaneous frequency
  11. """
  13. import os
  14. import numpy as np
  15. import emd
  16. import pickle
  17. import mne
  18. import matplotlib.pyplot as plt
  20. from base import ssd
  21. from base.params import mne_datIn, srate, emd_mask_frq, ID, time, imf_pk
  23. for sub in range(len(ID)):
  25. subOut = mne_datIn + ID[sub]
  26. os.chdir(subOut)
  28. ssdDat = pickle.load(open('emd/cmp_dat.pkl', 'rb'))
  30. comp_kys = list(ssdDat.keys())
  32. emd_out = {}
  34. # load filters
  35. ssd_out = pickle.load(open(f'ssd/{ID[sub]}_ssd_out.pkl', 'rb'))
  37. eeg_dat = {}
  39. for cmp_id, cmp in enumerate(comp_kys):
  41. kys_segs = comp_kys[cmp_id].split('_')
  43. cnd = kys_segs[0]
  44. frq = kys_segs[1]
  45. ssd_n = kys_segs[-1]
  47. if frq == 'a':
  48. # get filter
  49. filt = ssd_out[f'{cnd}_{frq}_frq']['filt']
  51. imf_out = {}
  52. emd_dat = {}
  54. # run emd on pre-post data
  55. for tm in ('PRE', 'POS'):
  57. # load eeg data
  58. raw = mne.io.read_raw_fif(f'pre_proc/{ID[sub]}_{cnd}{tm}_filtCh_raw.fif', preload=True)
  60. # get filtered data
  61. raw_filt = ssd.apply_filters(raw, filt, prefix="ssd")
  63. emd_in = raw_filt.get_data(picks=ssd_n)[0]
  65. #run EMD
  66. imf = emd.sift.mask_sift(emd_in,
  67. mask_amp = 2,
  68. # mask_amp_mode='ratio_sig',
  69. mask_freqs = np.array(emd_mask_frq[frq])/srate,
  70. max_imfs = 6,
  71. envelope_opts = {"interp_method":"mono_pchip"})
  73. imf_out[tm] = imf
  74. emd_dat[tm] = emd_in
  76. # plot imfs against ssd time course
  77. for im_d in imf_out:
  79. fig, ax = plt.subplots()
  81. plt.ion()
  82. plt.plot(emd_dat[im_d])
  83. plt.plot(imf_out[im_d][:,imf_pk[frq]])
  84. plt.grid(visible=True, axis='x')
  85. plt.xlim(0, 1800)
  86. plt.title(f'freq = {frq}_{im_d}')
  88. plt.show(block=True)
  90. # is the fit/data good to keep?
  91. keep = input('Keep component (Y/N)?')
  93. # if its good, extract cycle and paif info
  94. if keep == 'Y':
  96. for n, im_d in enumerate(imf_out):
  97. # calculate inst. phase/amp
  98. IP, IF, IA = emd.spectra.frequency_transform(imf_out[im_d], srate, 'nht')
  100. # select good/thresholded cycles
  101. amp_thr = np.percentile(IA[:, imf_pk[frq]], 75) #cut-off set at 75th percentile
  102. mask = IA[:, imf_pk[frq]] > amp_thr
  103. cycles = emd.cycles.get_cycle_vector(IP[:, imf_pk[frq]],
  104. return_good=True,
  105. mask=mask)
  107. # phase align
  108. pa_if, _ = emd.cycles.phase_align(IP[:, imf_pk[frq]],
  109. IF[:, imf_pk[frq]],
  110. cycles=cycles[:, 0])
  112. emd_out[f'{cnd}_{ssd_n}_{time[n]}'] = dict(imfs=imf_out[im_d],
  113. IA=IA[:,imf_pk[frq]],
  114. IP=IP[:,imf_pk[frq]],
  115. IF=IF[:,imf_pk[frq]],
  116. paif=pa_if,
  117. mask=mask)
  119. pickle.dump(emd_out, open('emd/emd_out.pkl', 'wb'))

emd_1_run_inspect.py at commit ed0c6ce, no license · at the source

Overview

Authors: George M Opie1, Natalie Schaworonkow2, Pedro C Gordon3,4, Dania Humaidan3,4, Ulf Ziemann3,4
ORCID iDs: George M Opie
  1. Discipline of Physiology, School of Biomedicine, The University of Adelaide, Adelaide, Australia
  2. Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
  3. Department of Neurology & Stroke, Eberhard Karls University of Tübingen, Tübingen, Germany
  4. Hertie-Institute for Clinical Brain Research, Eberhard Karls University of Tübingen, Tübingen, Germany
Institutions: The University of Adelaide (Australia); Ernst Strüngmann Institute for Neuroscience (Germany); Hertie Institute for Clinical Brain Research (Germany); University of Tübingen (Germany)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1169
Dates: received 27 October 2025; accepted 14 February 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1169 · PMID 41836918 · PMCID PMC12983582 · OpenAlex W7130677388
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Physiology & signal measures, Machine learning, fMRI & imaging
Keywords: electroencephalography, alpha oscillation, waveform shape, diazepam, GABAA receptor, pharmaco-EEG
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Australian Research Council (DE230100022)
Citations: not cited yet (Europe PMC); 73 references in the paper

Abstract

While neural oscillations are conventionally assessed via their frequency, power and phase, developing literature suggests that their shape also provides neurophysiological and functional information. However, the extent to which the shape of oscillations recorded non-invasively in humans index specific brain processes remains unclear. This study implemented a pharmaco-EEG approach to begin addressing this limitation. Resting-state EEG data were collected before and after placebo or diazepam, a positive allosteric modulator of type A γ-aminobutyric acid (GABAA) receptors, with 15 participants included in the main analysis. The shape of individual cycles in the alpha band was then derived using empirical mode decomposition, followed by extraction of principal components (PCs) describing specific facets of alpha shape. Results of this approach show that all shape features were unchanged following placebo. In contrast, diazepam was associated with complex changes in several shape features, including peak-trough shape and edge speed. While changes in shape were apparent in all cortical lobes, the strongest alterations were specific to sensorimotor and parietal cortices. Taken together, our results support the neurophysiological utility of waveform shape, particularly with respect to non-invasive human recordings. Furthermore, the regional specificity of effects highlights the need for more granular exploration of waveform diversity.

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 13 matches between paragraphs and lines of code.

g-opie/Diazepam-and-alpha-shape

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: ed0c6ce5bb896509c1148ec3c4a294b385329dc6, 21 January 2026
Languages: Python (23), R (6), MATLAB (2)
Size: 45 files, 31 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 (17 files), pandas (14 files), Matplotlib (13 files), MNE-Python (11 files), specparam (formerly FOOOF) (9 files), brms (6 files), tidyverse (6 files), SciPy (5 files), car (4 files), easystats (4 files), emmeans (4 files), ggplot2 (4 files), ggpubr (4 files), EEGLAB (2 files), seaborn (2 files), PyPREP (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
32 files
At the source: github.com/g-opie/Diazepam-and-alpha-shape

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

Tracing map

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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;
  • 31 scripts, each with its path and the digest of its content;
  • 13 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

No dataset and no data link were found in the paper.

Data and Code Availability

Due to the nature of ethical consent obtained, data cannot be shared. All codes to replicate analyses and generate figures from this article can be found at: https://github.com/g-opie/Diazepam-and-alpha-shape

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, 6 keywords, 1 funder, 72 references.

Cite

This paper

Opie, G. M., Schaworonkow, N., Gordon, P. C., Humaidan, D., & Ziemann, U. (2026). Diazepam alters the shape of alpha oscillations recorded from human cortex using EEG. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1169. https://doi.org/10.1162/imag.a.1169

BibTeX

@article{opie2026diazepam,
author = {Opie, George M and Schaworonkow, Natalie and Gordon, Pedro C and Humaidan, Dania and Ziemann, Ulf},
title = {{Diazepam alters the shape of alpha oscillations recorded from human cortex using EEG}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {4},
pages = {IMAG.a.1169},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1169},
url = {https://doi.org/10.1162/imag.a.1169},
pmid = {41836918},
pmcid = {PMC12983582}
}

RIS

TY - JOUR
AU - Opie, George M
AU - Schaworonkow, Natalie
AU - Gordon, Pedro C
AU - Humaidan, Dania
AU - Ziemann, Ulf
TI - Diazepam alters the shape of alpha oscillations recorded from human cortex using EEG
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/03/12
VL - 4
SP - IMAG.a.1169
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1169
UR - https://doi.org/10.1162/imag.a.1169
LA - en
ER -

CSL-JSON

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