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Qualitative EEG abnormalities in ASD reflect inhibition-dominated brain dynamics.

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1 match 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.

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  1. [1] § Materials and methods › Participants › EU-AIMS ↔ EU-AIMS scripts and data/utils/tables.py, lines 12–89 · score 0.76 · VABS dl, VABS soc, VABS composite, RBS, ADOS, Sums

Paper

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The authors' code

Python · 165 lines · 5.6 KB · MIT · 1 match

  1. from __future__ import annotations
  3. from typing import Any
  5. import numpy as np
  6. import pandas as pd
  7. from scipy.stats import sem
  9. from utils.stats import get_test_and_pvalue
  12. def make_table(
  13. labels: list[str],
  14. group_idxes: list[pd.Series],
  15. test_types: list[int],
  16. metadata_frame: pd.DataFrame,
  17. ) -> pd.DataFrame:
  18. subset_df1 = metadata_frame[group_idxes[0]]
  19. subset_df2 = metadata_frame[group_idxes[1]]
  21. columns = [
  22. "tests",
  23. f"{labels[0]}(n={len(subset_df1)})",
  24. f"{labels[1]}(n={len(subset_df2)})",
  25. "T (DF) / Z,W",
  26. "P-VALUE",
  27. "Effect size",
  28. ]
  29. table = pd.DataFrame(columns=columns)
  31. n_male1 = np.sum(subset_df1["t1_sex (1 Masc; -1 fem)"] == 1)
  32. n_female1 = np.sum(subset_df1["t1_sex (1 Masc; -1 fem)"] == -1)
  33. n_male2 = np.sum(subset_df2["t1_sex (1 Masc; -1 fem)"] == 1)
  34. n_female2 = np.sum(subset_df2["t1_sex (1 Masc; -1 fem)"] == -1)
  36. table.loc[0] = [
  37. "males/females",
  38. f"{n_male1}/{n_female1}",
  39. f"{n_male2}/{n_female2}",
  40. "-",
  41. "-",
  42. "-",
  43. ]
  45. test_text, pvalue_text, eff_text, _ = get_test_and_pvalue(
  46. subset_df2,
  47. subset_df1,
  48. "t1_ageyrs",
  49. test_types[0],
  50. )
  51. table.loc[1] = [
  52. "age",
  53. f"{np.min(subset_df1['t1_ageyrs'])}-{np.max(subset_df1['t1_ageyrs'])} "
  54. f"{np.round(np.nanmean(subset_df1['t1_ageyrs']), 1)}+-{np.round(np.nanstd(subset_df1['t1_ageyrs']), 1)}",
  55. f"{np.min(subset_df2['t1_ageyrs'])}-{np.max(subset_df2['t1_ageyrs'])} "
  56. f"{np.round(np.nanmean(subset_df2['t1_ageyrs']), 1)}+-{np.round(np.nanstd(subset_df2['t1_ageyrs']), 1)}",
  57. test_text,
  58. pvalue_text,
  59. eff_text,
  60. ]
  62. def add_row(row_idx: int, label: str, colname: str, test_type_idx: int) -> None:
  63. test_text, pvalue_text, eff_text, _ = get_test_and_pvalue(
  64. subset_df2,
  65. subset_df1,
  66. colname,
  67. test_types[test_type_idx],
  68. )
  70. table.loc[row_idx] = [
  71. label,
  72. f"n={np.sum(~np.isnan(subset_df1[colname]))} "
  73. f"{np.round(np.nanmean(subset_df1[colname]), 1)}+-{np.round(sem(subset_df1[colname], nan_policy='omit'), 1)}",
  74. f"n={np.sum(~np.isnan(subset_df2[colname]))} "
  75. f"{np.round(np.nanmean(subset_df2[colname]), 1)}+-{np.round(sem(subset_df2[colname], nan_policy='omit'), 1)}",
  76. test_text,
  77. pvalue_text,
  78. eff_text,
  79. ]
  81. add_row(2, "TIQ", "t1_fsiq", 1)
  82. add_row(3, "ADOS-2", "ADOS_total", 2)
  83. add_row(4, "RBS-R", "t1_rbs_total", 2)
  84. add_row(5, "VABS-composite", "t1_vabsabcabc_standard", 3)
  85. add_row(6, "VABS-com", "t1_vabsdscoresc_dss", 4)
  86. add_row(7, "VABS-dl", "t1_vabsdscoresd_dss", 5)
  87. add_row(8, "VABS-soc", "t1_vabsdscoress_dss", 6)
  89. return table
  92. def build_euaims_masks(alternative_conditions: list[dict[str, Any]]) -> dict[str, pd.Series]:
  93. asd_and_agegroup = np.array(alternative_conditions[0]["condition_column"])
  94. children_adol_mask = pd.Series(asd_and_agegroup).str.contains("Children|Adolescent")
  95. adults_mask = pd.Series(asd_and_agegroup).str.contains("Adults")
  96. asd_mask = pd.Series(asd_and_agegroup).str.contains("ASD")
  97. con_mask = pd.Series(asd_and_agegroup).str.contains("CONTROL")
  99. asd_and_nl = np.array(alternative_conditions[3]["condition_column"])
  100. nl_mask = pd.Series(asd_and_nl).str.contains("NL")
  101. abn_mask = pd.Series(asd_and_nl).str.contains("ABN")
  102. childadol_mask = pd.Series(asd_and_nl).str.contains("Child-Adol")
  104. return {
  105. "children_adol_mask": children_adol_mask,
  106. "adults_mask": adults_mask,
  107. "asd_mask": asd_mask,
  108. "con_mask": con_mask,
  109. "nl_mask": nl_mask,
  110. "abn_mask": abn_mask,
  111. "childadol_mask": childadol_mask,
  112. }
  115. def run_demographic_tables(
  116. metadata_frame_euaims: pd.DataFrame,
  117. masks: dict[str, pd.Series],
  118. ) -> dict[str, pd.DataFrame]:
  119. children_adol_mask = masks["children_adol_mask"]
  120. adults_mask = masks["adults_mask"]
  121. asd_mask = masks["asd_mask"]
  122. con_mask = masks["con_mask"]
  123. nl_mask = masks["nl_mask"]
  124. abn_mask = masks["abn_mask"]
  125. childadol_mask = masks["childadol_mask"]
  127. tables = {
  128. "Table_S1.child_adol_con_vs_asd": make_table(
  129. labels=["CON", "ASD"],
  130. group_idxes=[children_adol_mask & con_mask, children_adol_mask & asd_mask],
  131. test_types=[1, 1, 1, 1, 1, 1, 0],
  132. metadata_frame=metadata_frame_euaims,
  133. ),
  134. "Table_S1.adults_con_vs_asd": make_table(
  135. labels=["CON", "ASD"],
  136. group_idxes=[adults_mask & con_mask, adults_mask & asd_mask],
  137. test_types=[1, 1, 0, 0, 0, 0, 0],
  138. metadata_frame=metadata_frame_euaims,
  139. ),
  140. "Table_S3.child_adol_con_nl_vs_abn": make_table(
  141. labels=["CON_nl", "CON_abn"],
  142. group_idxes=[
  143. childadol_mask & con_mask & nl_mask,
  144. childadol_mask & con_mask & abn_mask,
  145. ],
  146. test_types=[1, 0, 1, 0, 0, 0, 0],
  147. metadata_frame=metadata_frame_euaims,
  148. ),
  149. "Table_S3.child_adol_asd_nl_vs_abn": make_table(
  150. labels=["ASD_nl", "ASD_abn"],
  151. group_idxes=[
  152. childadol_mask & asd_mask & nl_mask,
  153. childadol_mask & asd_mask & abn_mask,
  154. ],
  155. test_types=[1, 1, 1, 1, 1, 1, 1],
  156. metadata_frame=metadata_frame_euaims,
  157. ),
  158. }
  160. for name, table in tables.items():
  161. print(f"\n{name}")
  162. print(table)
  164. print(np.sum(con_mask & abn_mask) / np.sum(con_mask))
  165. return tables

tables.py, under MIT · at the source

Overview

Authors: Arthur-Ervin Avramiea1,2, Erika L. Juarez-Martinez1,2, Pilar Garcés3, Joerg F. Hipp3, Simon-Shlomo Poil4, Marina Diachenko1, Huibert D. Mansvelder1, Emily Jones5, Luke Mason6, Declan Murphy6,7, Eva Loth6, Bethany Oakley6, Tony Charman8, Tobias Banaschewski9, Bob Oranje10, Jan Buitelaar11, Hilgo Bruining2,12,13, Klaus Linkenkaer-Hansen1
ORCID iDs: Arthur-Ervin Avramiea, Marina Diachenko, Huibert D. Mansvelder, Luke Mason, Eva Loth, Bethany Oakley, Tony Charman, Klaus Linkenkaer-Hansen
13 affiliations
  1. Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, (CNCR), Amsterdam Neuroscience, VU Amsterdam,De Boelelaan 1100, 1081 HV Amsterdam, The Netherlands
  2. Child and Adolescent Psychiatry and Psychosocial Care, Emma Children’s Hospital, Amsterdam UMC, Vrije Universiteit Amsterdam,Meibergdreef 5, 1105 AZ Amsterdam, The Netherlands
  3. Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel,Basel, Switzerland
  4. Aspect Neuroprofiles BV, Amsterdam, The Netherlands
  5. Department of Psychological Sciences, Centre for Brain and Cognitive Development, Birkbeck, University of London,London, UK
  6. Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London,London, UK
  7. Institute for Translational Neurodevelopment/ South London and Maudsley, NHS Foundation Trust,London, UK
  8. Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King’s College London,London, UK
  9. Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, German Center for Mental Health (DZPG), Partner Site Mannheim-Heidelberg-Ulm,Mannheim, Germany
  10. Center for Neuropsychiatric Schizophrenia Research (CNSR), Psychiatric Center Glostrup, Copenhagen University Hospital,Copenhagen, Denmark
  11. Department of Medical Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboudumc,Nijmegen, The Netherlands
  12. N=You Neurodevelopmental Precision Center, Amsterdam Neuroscience, Amsterdam Reproduction and Development, Amsterdam UMC,Amsterdam, The Netherlands
  13. Levvel, Center for Child and Adolescent Psychiatry,Amsterdam, The Netherlands
Institutions: Amsterdam Neuroscience (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Roche (Switzerland) (Switzerland); University of London (United Kingdom); King's College London (United Kingdom); National Health Service (United Kingdom); Central Institute of Mental Health (Germany); Copenhagen University Hospital (Denmark); Radboud University Medical Center (Netherlands); Levvel (Netherlands)
Journal: Scientific reports, volume 16, issue 1, article 16055
Dates: received 5 March 2025; accepted 24 February 2026; published online 3 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-42120-y · PMID 41932958 · PMCID PMC13199430 · OpenAlex W7148738596
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), autism (population), developmental (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Complexity
Keywords: Autism spectrum disorder, EEG, Excitation-inhibition balance, Autism spectrum disorders, Inhibition-excitation balance
MeSH: Autism Spectrum Disorder*, Brain*, Electroencephalography*, Adolescent, Child, Female, Humans, Male (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Nederlandse Organisatie voor Wetenschappelijk Onderzoek (406-15-256, 10250022110003, 612.001.123); H2020 Society (604102); NWA (NWA.1160.18.200); Amsterdam UMC TKI grant BRAINinBALANCE (project number 31556-2012377)
Citations: cited by 1 paper (Europe PMC); 103 references in the paper

Abstract

Qualitative EEG abnormalities are common in Autism Spectrum Disorder (ASD) and hypothesized to reflect disrupted excitation/inhibition (E/I) balance. To test this, we recently introduced a functional measure of network-level E/I ratio (fE/I). Here, we applied fE/I and other EEG measures to alpha oscillations from source-reconstructed data in the EU-AIMS dataset (267 ASD, 209 controls). We analyzed these measures alongside qualitative EEG abnormalities ranging from slowing of activity to epileptiform patterns, aiming to replicate the findings from the SPACE-BAMBI study. Contrary to our previous report, we did not observe increased fE/I variability in ASD compared to controls. EEG abnormalities were rare in adults and could not be statistically assessed. ASD children-adolescents with EEG abnormalities exhibited lower relative alpha power and fE/I compared to those without. However, EEG-abnormality scoring did not stratify the behavioral heterogeneity of ASD using clinical measures. Surprisingly, several controls also exhibited qualitative EEG abnormalities with a strikingly similar anatomical distribution of reduced fE/I, reflecting inhibition-dominated network dynamics in sensory processing regions. The robustness of this association between EEG abnormalities and reduced fE/I was further supported by re-analysis of the SPACE-BAMBI study in source space. Stratification by the presence of EEG abnormalities and their effects on network activity may help understand neurodevelopmental physiological heterogeneity and the difficulties in implementing E/I targeting treatments in unselected cohorts.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-42120-y.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

figshare 31634182

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (7 files), pandas (5 files), SciPy (4 files), Matplotlib (2 files), statsmodels (2 files), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
10 files
At the source: doi.org/10.6084/m9.figshare.31634182

rhardstone/fei](https:

License: none: the authors keep all their rights
State: the link is dead, verified on 28 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link is dead
  • 28 September 2026: the link is dead
At the source: github.com/rhardstone/fEI](https:/github.com/rhardstone/fEI)

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;
  • 10 scripts, each with its path and the digest of its content;
  • 1 match 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

Due to privacy regulations of human subjects, we cannot provide the EEG files of the subjects included in our study. Analysis scripts to reproduce the figures and statistics, along with the underlying data, will be made available on figshare (https://doi.org/10.6084/m9.figshare.31634182), a website dedicated for sharing scientific data. The code for the fE/I algorithm is publicly available at [https://github.com/rhardstone/fEI](https:/github.com/rhardstone/fEI).

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 5 keywords, 8 MeSH terms, 4 funders, 93 references.

Cite

This paper

Avramiea, A.-E., Juarez-Martinez, E. L., Garcés, P., Hipp, J. F., Poil, S.-S., Diachenko, M., Mansvelder, H. D., Jones, E., Mason, L., Murphy, D., Loth, E., Oakley, B., Charman, T., Banaschewski, T., Oranje, B., Buitelaar, J., Bruining, H., & Linkenkaer-Hansen, K. (2026). Qualitative EEG abnormalities in ASD reflect inhibition-dominated brain dynamics. Scientific reports, 16(1), 16055. https://doi.org/10.1038/s41598-026-42120-y

BibTeX

@article{avramiea2026qualitative,
author = {Avramiea, Arthur-Ervin and Juarez-Martinez, Erika L. and Garcés, Pilar and Hipp, Joerg F. and Poil, Simon-Shlomo and Diachenko, Marina and Mansvelder, Huibert D. and Jones, Emily and Mason, Luke and Murphy, Declan and Loth, Eva and Oakley, Bethany and Charman, Tony and Banaschewski, Tobias and Oranje, Bob and Buitelaar, Jan and Bruining, Hilgo and Linkenkaer-Hansen, Klaus},
title = {{Qualitative EEG abnormalities in ASD reflect inhibition-dominated brain dynamics}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {16055},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-42120-y},
url = {https://doi.org/10.1038/s41598-026-42120-y},
pmid = {41932958},
pmcid = {PMC13199430}
}

RIS

TY - JOUR
AU - Avramiea, Arthur-Ervin
AU - Juarez-Martinez, Erika L.
AU - Garcés, Pilar
AU - Hipp, Joerg F.
AU - Poil, Simon-Shlomo
AU - Diachenko, Marina
AU - Mansvelder, Huibert D.
AU - Jones, Emily
AU - Mason, Luke
AU - Murphy, Declan
AU - Loth, Eva
AU - Oakley, Bethany
AU - Charman, Tony
AU - Banaschewski, Tobias
AU - Oranje, Bob
AU - Buitelaar, Jan
AU - Bruining, Hilgo
AU - Linkenkaer-Hansen, Klaus
TI - Qualitative EEG abnormalities in ASD reflect inhibition-dominated brain dynamics
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/03
VL - 16
IS - 1
SP - 16055
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-42120-y
UR - https://doi.org/10.1038/s41598-026-42120-y
LA - en
ER -

CSL-JSON

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