OSCR

Magnetoencephalography biomarkers for assessing myelin content and neuronal function in acute optic neuritis.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › Statistical analyses › Reproducibility of the P100/M100 latencies and of harmonic counts ↔ icc_reproducibility.py, lines 55–159 · score 0.62 · retest variability, standard deviation, TRV, raters, Reproducibility
  2. [2] § Results › Associations between magnetoencephalography metrics in V1, electrophysiology and imaging parameters ↔ correlations.py, lines 29–74 · score 0.54 · macular GCL volume, Pearson correlation, P100 latency, scatter, ophthalmological, Harmonic

Paper

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The authors' code

Python · 236 lines · 8.4 KB · no license · 1 match

  1. """
  2. ICC test-retest reproducibility (Table 2).
  4. Computes intraclass correlation coefficients (ICC) for P100 latency
  5. measurements across runs, directions, raters, and modalities (VEP vs MEG).
  6. Produces scatter plots and exports results to Excel/pickle.
  8. Source: test_retest.py
  9. """
  10. import functools
  11. import logging
  12. import operator
  13. import os
  14. from math import sqrt
  15. from pathlib import Path
  16. from typing import Dict, Generator
  18. import matplotlib
  20. matplotlib.use("Agg")
  21. import matplotlib.pyplot as plt
  22. import numpy as np
  23. import pandas as pd
  24. import pingouin as pg
  26. CSV_PATH = Path(__file__).parent / "data_test_retest.csv"
  29. def df_friendly_concat(
  30. df: pd.DataFrame, columns: list, behavior_if_missing: str = "ignore"
  31. ) -> pd.Series:
  32. """Concatenate string representations of multiple columns into a single key."""
  34. def iterator_column(
  35. df: pd.DataFrame, columns: list
  36. ) -> Generator[pd.Series, None, None]:
  37. for c in columns:
  38. try:
  39. yield df[c].astype(str)
  40. except KeyError:
  41. if behavior_if_missing == "ignore":
  42. continue
  43. else:
  44. raise
  46. return functools.reduce(operator.add, iterator_column(df, columns))
  49. def compact_name(filter: Dict, raters: str) -> str:
  50. """Build a compact descriptive name from filter and raters parameters."""
  51. name_elements = {"comp": raters, **filter}
  52. return "_".join(f"{k}_{v}" for k, v in name_elements.items())
  55. def icc(df: pd.DataFrame, raters: str, filter: Dict) -> pd.DataFrame:
  56. """Compute ICC for a given comparison (raters) with data filtered by filter.
  58. Also produces a scatter plot of the two measurement occasions and computes
  59. test-retest variability (TRV) and delta statistics.
  60. """
  61. df2 = df
  62. df2 = df2[df2["angle"] == "60"]
  63. df2 = df2[df2["rater"] != "YC"]
  64. df2 = df2[df2["direction"] != "mean_right_left"]
  65. if "are_meaned_runs" not in filter:
  66. df2 = df2[~df2["ignore_test_retest"]]
  67. df2 = df2.dropna(subset=["run"], inplace=False)
  68. df2 = df2[df2["run"] <= 2]
  69. for k, v in filter.items():
  70. df2 = df2[df2[k] == v]
  71. cols = ["eye", "direction", "angle", "patient_number", "rater", "type", "run"]
  72. cols.remove(raters)
  73. df2["item_info"] = df_friendly_concat(df2, cols)
  74. unique_patient_numbers = [str(i) for i in df2["patient_number"].unique()]
  75. patient_numbers = (
  76. str(len(unique_patient_numbers)) + " : " + ",".join(unique_patient_numbers)
  77. )
  78. try:
  79. icc_df = pg.intraclass_corr(
  80. data=df2,
  81. raters=raters,
  82. targets="item_info",
  83. ratings="peak",
  84. nan_policy="omit",
  85. )
  86. except Exception:
  87. logging.exception(f"icc failed for {raters}, {filter}")
  88. return pd.DataFrame()
  90. try:
  91. df_pivot = df2.pivot_table(index="item_info", columns=raters, values="peak")
  92. except Exception:
  93. logging.exception(f"pivot_table failed for {raters}, {filter}")
  94. return pd.DataFrame()
  96. rater_values = df2[raters].unique()
  97. if len(rater_values) > 2:
  98. try:
  99. rater_values = rater_values[~np.isnan(rater_values)]
  100. except Exception:
  101. pass
  102. if len(rater_values) != 2:
  103. raise ValueError(
  104. f"Expected exactly 2 rater values, got {len(rater_values)}: {rater_values}"
  105. )
  106. df_pivot["delta"] = df_pivot[rater_values[0]] - df_pivot[rater_values[1]]
  107. icc_df["delta_standard_deviation"] = df_pivot["delta"].std()
  108. icc_df["patient_numbers"] = patient_numbers
  110. # Test-retest variability (TRV)
  111. df_pivot["delta2"] = df_pivot["delta"] ** 2
  112. sw_square = df_pivot["delta2"].mean() / 2
  113. trv = 1.96 * sqrt(sw_square)
  115. df_pivot["delta"] = df_pivot["delta"].abs()
  116. mn = df_pivot["delta"].mean()
  117. icc_df["delta"] = mn
  118. icc_df["trv"] = trv
  120. name = compact_name(filter, raters)
  122. # --- Scatter plot ---
  123. labels_colors = {"k": "healthy", "red": "affected", "green": "fellow"}
  124. f, ax = plt.subplots()
  125. labels = None
  126. for color in ["k", "red", "green"]:
  127. df2_color = df2[df2["color"] == color]
  128. runs = df2_color.pivot_table(index="item_info", columns=raters, values="peak")
  129. runs = runs.dropna(inplace=False)
  130. if runs.empty:
  131. continue
  132. columns = runs.columns
  133. run1 = list(runs[columns[0]])
  134. run2 = list(runs[columns[1]])
  135. ax.scatter(
  136. run1,
  137. run2,
  138. c=color,
  139. s=10,
  140. alpha=0.5,
  141. label=labels_colors[color],
  142. )
  143. ax.tick_params(axis="both", which="major", labelsize="large")
  144. labels = columns[0], columns[1]
  145. ax.legend()
  146. if labels is None:
  147. labels = "1 ?", "2 ?"
  148. if tuple(labels) == (1, 2):
  149. labels = "1", "2"
  150. plt.xlabel(f"{raters} {labels[0]}", fontsize="large")
  151. plt.ylabel(f"{raters} {labels[1]}", fontsize="large")
  152. plt.suptitle("peaks")
  153. ax.legend(loc="upper left", fontsize="large")
  154. ax.plot([85, 180], [85, 180], color="grey", linestyle="dashed")
  156. f.savefig(f"./figures/{name}.png")
  157. f.savefig(f"./figures/{name}.svg")
  158. plt.close()
  159. return icc_df
  162. def clone_with_augmented_filter(d: Dict, filter: Dict) -> Dict:
  163. """Copy a config dict while merging additional filter keys."""
  164. return {**d, "filter": {**d["filter"], **filter}}
  167. def iterator_all_icc_to_perform(iccs_to_perform: list) -> Generator[Dict, None, None]:
  168. """Yield all ICC configurations, expanding each base config into
  169. sub-analyses (affected/fellow eye, patient/healthy splits)."""
  170. for d in iccs_to_perform:
  171. obtention_type = d["filter"].get("type")
  172. if obtention_type == "ophtalmo":
  173. yield d
  174. yield clone_with_augmented_filter(
  175. d, {"is_patient": True, "is_eye_affected": True}
  176. )
  177. yield clone_with_augmented_filter(
  178. d, {"is_patient": True, "is_eye_affected": False}
  179. )
  180. elif obtention_type == "stc":
  181. yield d
  182. yield clone_with_augmented_filter(d, {"is_patient": False})
  183. yield clone_with_augmented_filter(
  184. d, {"is_patient": True, "is_eye_affected": True}
  185. )
  186. yield clone_with_augmented_filter(
  187. d, {"is_patient": True, "is_eye_affected": False}
  188. )
  189. elif d["raters"] == "type":
  190. yield clone_with_augmented_filter(
  191. d, {"is_patient": True, "are_meaned_runs": True}
  192. )
  193. yield clone_with_augmented_filter(
  194. d,
  195. {"is_patient": True, "is_eye_affected": False, "are_meaned_runs": True},
  196. )
  197. yield clone_with_augmented_filter(
  198. d,
  199. {"is_patient": True, "is_eye_affected": True, "are_meaned_runs": True},
  200. )
  203. def compute_iccs_and_create_figures(largest_df: pd.DataFrame) -> None:
  204. """Run all ICC analyses and save results to Excel and pickle."""
  205. os.makedirs("./figures", exist_ok=True)
  206. iccs_to_perform = [
  207. dict(raters="run", filter={"rater": "Ysoline", "type": "ophtalmo"}),
  208. dict(raters="run", filter={"rater": "Ysoline", "type": "stc"}),
  209. dict(raters="run", filter={"rater": "Celine", "type": "ophtalmo"}),
  210. dict(raters="run", filter={"rater": "Celine", "type": "stc"}),
  211. dict(raters="direction", filter={"rater": "Ysoline", "type": "stc"}),
  212. dict(raters="direction", filter={"rater": "Ysoline", "type": "ophtalmo"}),
  213. dict(raters="direction", filter={"rater": "Celine", "type": "ophtalmo"}),
  214. dict(raters="direction", filter={"rater": "Celine", "type": "stc"}),
  215. dict(raters="rater", filter={"type": "ophtalmo"}),
  216. dict(raters="rater", filter={"type": "stc"}),
  217. dict(raters="type", filter={"is_patient": True, "rater": "Ysoline"}),
  218. ]
  219. all_iccs_performed = []
  220. keys = []
  221. for icc_info in iterator_all_icc_to_perform(iccs_to_perform):
  222. all_iccs_performed.append(icc(largest_df, **icc_info))
  223. keys.append(compact_name(**icc_info))
  224. non_empty = [(k, df) for k, df in zip(keys, all_iccs_performed) if not df.empty]
  225. if non_empty:
  226. result_keys, result_dfs = zip(*non_empty)
  227. results = pd.concat(result_dfs, keys=result_keys)
  228. results.to_excel("./results_icc.xlsx")
  229. results.to_pickle("./results_icc.pkl")
  230. else:
  231. logging.warning("All ICC computations returned empty results")
  234. if __name__ == "__main__":
  235. df = pd.read_csv(CSV_PATH)
  236. compute_iccs_and_create_figures(df)

icc_reproducibility.py at commit 0a6f3f2, no license · at the source

Overview

Authors: Ysoline Beigneux1, Christophe Gitton2, Abdul Rauf Anwar2, Jean-Didier Lemarechal3, Mariem Hamzaoui4, Michel Paques5,6, Catherine Vignal7,8, Isabelle Audo9,10, Benedetta Bodini1, Bruno Stankoff1, Letizia Leocani11, Catherine Lubetzki1, Nathalie George2, Céline Louapre1
ORCID iDs: Jean-Didier Lemarechal, Bruno Stankoff, Céline Louapre
  1. Department of Neurology, CIC Neurosciences, Sorbonne Université, Paris Brain Institute—ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Hôpital de la Pitié Salpêtrière, Paris 75013, France
  2. Sorbonne Université, Institut du Cerveau—Paris Brain Institute—ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, CENIR, Centre MEG-EEG, Paris 75013, France
  3. Institut de Neurosciences de la Timone, Aix Marseille Université, Marseille 13005, France
  4. Sorbonne Université, Paris Brain Institute—ICM, Inserm, CNRS, Paris 75013, France
  5. Sorbonne Université, INSERM, CNRS, UMR_S 968, Institut de la Vision, Paris 75012, France
  6. Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts, INSERM-DHOS Clinical Investigation Center 1423, Paris 75012, France
  7. Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts, Centre de Référence Maladies Rares REFERET and INSERM-DGOS CIC 1423, Paris 75012, France
  8. Department of Neuro-Ophthalmology, Foundation Adolphe de Rothschild Hospital, Paris 75019, France
  9. Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris 75012, France
  10. Centre Hospitalier National d'Ophtalmologie des Quinze-Vingts, Unité d’électrophysiologie Centre de Référence Maladies Rares REFERET and INSERM-DGOS CIC 1423, Paris 75012, France
  11. Experimental Neurophysiology Unit, Institute of Experimental Neurology-INSPE, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy
Institutions: Centre National de la Recherche Scientifique (France); Inserm (France); Sorbonne Université (France); Assistance Publique – Hôpitaux de Paris (France); Pitié-Salpêtrière Hospital (France); Institut du Cerveau (France); Aix-Marseille Université (France); Institut de Neurosciences de la Timone (France); Institut de la Vision (France); Centre hospitalier national d'ophtalmologie des Quinze-Vingts (France); Hôpital Rothschild (France); Fondation Ophtalmologique Adolphe de Rothschild (France); Fondation de Rothschild (France); IRCCS Ospedale San Raffaele (Italy)
Journal: Brain communications, volume 8, issue 3, article fcag218
Dates: received 9 May 2025; accepted 8 June 2026; published online 10 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/braincomms/fcag218 · PMID 42344673 · PMCID PMC13289812 · OpenAlex W7164181533
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), MEG (modality), human (organism), multiple sclerosis (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Spectral & time-frequency, Evoked potentials, Connectivity
Keywords: MEG, VEP, OCT, demyelination, neurodegeneration
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: Fondation FRANCE Sclérose en Plaques; Neuroscience Translational Research Infrastructure; Fondation pour la Recherche Médicale (FDM201906008575); Hôpital National des 15–20
Citations: not cited yet (Europe PMC); 33 references in the paper

Abstract

The visual pathway is an important model system for remyelination and neuroprotection trials in multiple sclerosis, due to its accessibility and the availability of validated methods including visual evoked potential and optical coherence tomography. However, visual evoked potentials are sometimes undetectable and demonstrate limited reliability after acute optic neuritis. This study aims to investigate novel magnetoencephalography markers for assessing myelin content and neuronal dysfunction in the early phase of optic neuritis and describes their inter-run reproducibility (‘over a single visit’) and association with short-term visual outcomes. Patients with unilateral acute optic neuritis were recruited and underwent ophthalmological assessments, brain MRI and magnetoencephalography. Magnetoencephalography data were acquired during visual stimulation with an alternating checkerboard pattern. We used source localization to reconstruct brain activity in the primary visual cortex (V1) and analysed it in the temporal and frequency domains. In the temporal domain, we focused on M100 latency—the magnetic counterpart of P100 latency. In the frequency domain, we assessed the spectral richness of the steady-state evoked field response by harmonic count, which reflects the diversity of frequency components present in the brain signal. Thirty-two patients were included at a median of 54 days [interquartile range = (37.5–78)] post-symptom onset of optic neuritis. Among patients with optic neuritis, visual evoked field recordings were detectable in 77% of cases, compared with 66% for visual evoked potential recordings. M100 latency demonstrated an excellent inter-run reproducibility for both fellow and affected eyes [intra-class correlation coefficient (ICC) >0.8, mean absolute inter-run difference of 2.99 ± 6.53 and 3.76 ± 7.53 ms, respectively]. By comparison, the reproducibility of P100 latency was good for fellow eye (ICC = 0.7, mean absolute inter-run difference of 3.9 ± 6.2 ms) but moderate for affected eye (ICC = 0.6, mean absolute inter-run difference of 9.1 ± 21.8 ms). In the frequency domain, the harmonic count correlated strongly with ganglion cell layer volume (r = 0.68, P = 0.0001), likely reflecting functional consequences of neuronal loss. Measures reflecting demyelination (P100 and M100 latencies) correlated with measures of neuronal damage (ganglion cell layer volume and harmonic count) from both conventional and magnetoencephalography assessments. Visual impairment was associated with neuronal damage (parameter estimates: β = 0.49, P = 0.017 for ganglion cell layer volume, β = 0.57, P = 0.003 for harmonic count) but not with demyelination measures. Our results highlight magnetoencephalography as a reproducible and comprehensive tool to study both myelin content and neuronal dysfunction shortly after optic neuritis and suggest that, at this early stage, neuronal damage is already the main driver of visual outcome.

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 2 matches between paragraphs and lines of code.

y-bgx/optic-neuritis-meg-paper

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0a6f3f24591808bd5cb3b05cecc458249c2ac6b6, 28 March 2026
Languages: Python (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, environment (requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: pandas (4 files), Matplotlib (3 files), NumPy (2 files), SciPy (2 files), seaborn (2 files), statsmodels (2 files), Pingouin (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/y-bgx/optic-neuritis-meg-paper

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

Data not provided in the article because of space limitations may be shared (anonymized) at the request of any qualified investigator for purposes of replicating procedures and results. The code used for data analysis in this study is available in a public repository at: https://github.com/y-bgx/optic-neuritis-meg-paper.

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, issue, pages, dates, 14 authors, 5 keywords, 4 funders, 33 references.

Cite

This paper

Beigneux, Y., Gitton, C., Anwar, A. R., Lemarechal, J.-D., Hamzaoui, M., Paques, M., Vignal, C., Audo, I., Bodini, B., Stankoff, B., Leocani, L., Lubetzki, C., George, N., & Louapre, C. (2026). Magnetoencephalography biomarkers for assessing myelin content and neuronal function in acute optic neuritis. Brain communications, 8(3), fcag218. https://doi.org/10.1093/braincomms/fcag218

BibTeX

@article{beigneux2026magnetoencephalograp,
author = {Beigneux, Ysoline and Gitton, Christophe and Anwar, Abdul Rauf and Lemarechal, Jean-Didier and Hamzaoui, Mariem and Paques, Michel and Vignal, Catherine and Audo, Isabelle and Bodini, Benedetta and Stankoff, Bruno and Leocani, Letizia and Lubetzki, Catherine and George, Nathalie and Louapre, Céline},
title = {{Magnetoencephalography biomarkers for assessing myelin content and neuronal function in acute optic neuritis}},
journal = {Brain communications},
year = {2026},
month = jun,
volume = {8},
number = {3},
pages = {fcag218},
publisher = {Oxford University Press},
issn = {2632-1297},
doi = {10.1093/braincomms/fcag218},
url = {https://doi.org/10.1093/braincomms/fcag218},
pmid = {42344673},
pmcid = {PMC13289812}
}

RIS

TY - JOUR
AU - Beigneux, Ysoline
AU - Gitton, Christophe
AU - Anwar, Abdul Rauf
AU - Lemarechal, Jean-Didier
AU - Hamzaoui, Mariem
AU - Paques, Michel
AU - Vignal, Catherine
AU - Audo, Isabelle
AU - Bodini, Benedetta
AU - Stankoff, Bruno
AU - Leocani, Letizia
AU - Lubetzki, Catherine
AU - George, Nathalie
AU - Louapre, Céline
TI - Magnetoencephalography biomarkers for assessing myelin content and neuronal function in acute optic neuritis
T2 - Brain communications
J2 - Brain Commun
PY - 2026
DA - 2026/06/10
VL - 8
IS - 3
SP - fcag218
SN - 2632-1297
PB - Oxford University Press
DO - 10.1093/braincomms/fcag218
UR - https://doi.org/10.1093/braincomms/fcag218
LA - en
ER -

CSL-JSON

{
"id": "10.1093/braincomms/fcag218",
"type": "article-journal",
"title": "Magnetoencephalography biomarkers for assessing myelin content and neuronal function in acute optic neuritis",
"container-title": "Brain communications",
"author": [
{
"family": "Beigneux",
"given": "Ysoline"
},
{
"family": "Gitton",
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{
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{
"family": "Hamzaoui",
"given": "Mariem"
},
{
"family": "Paques",
"given": "Michel"
},
{
"family": "Vignal",
"given": "Catherine"
},
{
"family": "Audo",
"given": "Isabelle"
},
{
"family": "Bodini",
"given": "Benedetta"
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{
"family": "Stankoff",
"given": "Bruno"
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{
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{
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{
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{
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}
],
"container-title-short": "Brain Commun",
"volume": "8",
"issue": "3",
"page": "fcag218",
"DOI": "10.1093/braincomms/fcag218",
"PMID": "42344673",
"PMCID": "PMC13289812",
"ISSN": "2632-1297",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/braincomms/fcag218",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
10
]
]
}
}

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In common: Pingouin, statsmodels, seaborn, 4 other tools, MEG, EEG
[9] doi:10.7554/elife.108673 [code]
Adaptive behavior is guided by integrated representations of controlled and non-controlled information.
Journal: eLife
In common: Pingouin, statsmodels, seaborn, 5 other tools, EEG
[10] doi:10.1093/cercor/bhag077 [code]
The longitudinal development of intrinsic timescales in infancy and their relation to alpha brain rhythm.
Journal: Cerebral cortex (New York, N.Y. : 1991)
In common: Pingouin, statsmodels, seaborn, 5 other tools, EEG

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