OSCR

A computational pipeline for a neurotransmitter-centric analysis of the effects of psychiatric medication on EEG spectral power.

Code ↔ Paper

5 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 5 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Computational pipeline workflow ↔ fitMixedModel.m, lines 1–62 · score 0.93 · TotalAcetylcholine, TotalDopamine, TotalHistamine, TotalNorepinephrine, TotalSerotonin, ElectrodeLocation
  2. [2] § Methods › Computational pipeline workflow ↔ visualizeEEGData.m, lines 160–220 · score 0.72 · clean_artifacts, power spectral density, EEGLAB, overlapping, channel, gamma
  3. [3] § Methods › Computational pipeline workflow ↔ functionsForEDFFiles.m, lines 79–165 · score 0.65 · power spectral density, EEGLAB, amplitude, frequency bands, epochs, 100 Hz
  4. [4] § Methods › Computational pipeline workflow ↔ fitMixedModel.m, lines 1–62 · score 0.52 · electrode location, fitted, frequency band, acetylcholine, norepinephrine, histamine
  5. [5] § Methods › Psychopharmacological data extraction and coding ↔ CreateTableWithReceptorProperties.m, the whole file · a weak match · score 0.51 · Drug receptor properties, Mirtazapine, profile, mixed, modeling

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 94 lines · 4.6 KB · BSD-2-Clause · 2 matches

  1. %fits different mixed model
  2. clear;
  4. file="D:/Results/FullTableMixedModelNormal.csv";
  5. table = readtable(file);
  7. table=table(:,{'PatientNr', 'FrequencyBand', 'ElectrodeLocation', 'Power', 'Antidepressant', 'Antipsychotic', 'TotalSerotonin', 'TotalDopamine', 'TotalNorepinephrine', 'TotalHistamine', 'TotalAcetylcholine' });
  9. %set if columns are categorical
  10. table.PatientNr = categorical(table.PatientNr);
  11. table.Antidepressant = categorical(table.Antidepressant);
  12. table.Antipsychotic = categorical(table.Antipsychotic);
  13. table.ElectrodeLocation = categorical(table.ElectrodeLocation);
  14. table.FrequencyBand = categorical(table.FrequencyBand);
  15. table.TotalSerotonin = categorical(table.TotalSerotonin);
  16. table.TotalDopamine = categorical(table.TotalDopamine);
  17. table.TotalNorepinephrine = categorical(table.TotalNorepinephrine);
  18. table.TotalHistamine = categorical(table.TotalHistamine);
  19. table.TotalAcetylcholine = categorical(table.TotalAcetylcholine);
  21. %fit different mixed models
  22. %lme = fitlme(table,"Power ~ FrequencyBand*ElectrodeLocation*Antipsychotic*Antidepressant+ (1|SessionNr) + (1|ElectrodeLocation:Side:Electrode)");
  24. %lme = fitlm(table,"Power ~ Antidepressant + ElectrodeLocation + FrequencyBand + PatientNr");
  26. %lme = fitlm(table,"Power ~ Risperidone*Olanzapine*Quetiapine*Aripiprazole*Ziprasidone*Haloperidol*Fluphenazine*Perphenazine*Clozapin*Citalopram*Escitalopram*Sertraline*Paroxetine*Fluoxetine*Bupropion*Venlafaxine*Mirtazapine*Trazodone*Amitriptyline*Clomipramin*Doxepin*Duloxetin*Nortriptylin*Lithium + ElectrodeLocation + FrequencyBand + PatientNr");
  28. %lme = fitlme(table,"Power ~ (Risperidone+Olanzapine+Quetiapine+Aripiprazole+Ziprasidone+Haloperidol+Fluphenazine+Perphenazine+Clozapin+Citalopram+Escitalopram+Sertraline+Paroxetine+Fluoxetine+Bupropion+Venlafaxine+Mirtazapine+Trazodone+Amitriptyline+Clomipramin+Doxepin+Duloxetin+Nortriptylin+Lithium|Antidepressant) + ElectrodeLocation + FrequencyBand + PatientNr");
  30. %lme = fitlme(table,"Power ~ Risperidone + Olanzapine + ElectrodeLocation + FrequencyBand + PatientNr + (1|Antidepressant)");
  32. %Model 1
  33. %lme = fitlme(table,"Power ~ Antidepressant + Side + FrequencyBand + (1|PatientNr)");
  35. %lme = fitlme(table,"Power ~ Antidepressant + Side + FrequencyBand + (1+Antidepressant + ElectrodeLocation + FrequencyBand|PatientNr)");
  37. %Model 2
  38. %lme = fitlme(table,"Power ~ Antidepressant + Side + FrequencyBand + (1|PatientNr) + (1|PatientNr:SessionNr)");
  40. %lme = fitlme(table,"Power ~ Antidepressant + Side + FrequencyBand + (1|PatientNr) + (1|PatientNr:SessionNr)+ (1|PatientNr:SessionNr:FileNr)");%Model 6
  42. %Model 3
  43. %lme = fitlme(table,"Power ~ Side + FrequencyBand + TotalSerotonin + TotalDopamine + TotalNorepinephrine + TotalHistamine + TotalAcetylcholine + (1|PatientNr)");
  45. %Model 4: did not work, not enough data for this complexitiy
  46. %lme2 = fitlme(table,"Power ~ Side + FrequencyBand * TotalSerotonin * TotalDopamine * TotalNorepinephrine * TotalHistamine * TotalAcetylcholine + (1|PatientNr)");
  48. %Model 5
  49. %lme = fitlme(table,"Power ~ Side + FrequencyBand*TotalSerotonin + FrequencyBand*TotalDopamine + FrequencyBand*TotalNorepinephrine + FrequencyBand*TotalHistamine + FrequencyBand*TotalAcetylcholine + (1|PatientNr)");
  51. %Model 6
  52. %lme = fitlme(table,"Power ~ ElectrodeLocation + FrequencyBand*TotalSerotonin + FrequencyBand*TotalDopamine + FrequencyBand*TotalNorepinephrine + FrequencyBand*TotalHistamine + FrequencyBand*TotalAcetylcholine + (1|PatientNr)");
  54. %Model 7
  55. %lme2 = fitlme(table,"Power ~ ElectrodeLocation*FrequencyBand*TotalSerotonin + ElectrodeLocation*FrequencyBand*TotalDopamine + ElectrodeLocation*FrequencyBand*TotalNorepinephrine + ElectrodeLocation*FrequencyBand*TotalHistamine + ElectrodeLocation*FrequencyBand*TotalAcetylcholine + (1|PatientNr)");
  58. %Model 8
  59. lme = fitlme(table,"Power ~ ElectrodeLocation*FrequencyBand*Antidepressant + ElectrodeLocation*FrequencyBand*Antipsychotic + (1|PatientNr)");
  62. lme
  65. %%%%%%%%%%%%%%%%%%%%%compare models%%%%%%%%%%%%%%%%%%%%%%%%%%
  66. %compare(lme, lme2)
  68. %%%%%%%%%%%%%%%%%%%%F-test%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  69. %pVal = coefTest(lme);
  70. %pVal
  72. %anova(lme)
  74. %%%%%%%%%%%%%%%%%%%estimated marginal means%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  75. %done in R
  77. %%%%%%%%%%%%%%%%%%Plot fitted response vs observed response%%%%%%%%%%%%%%%%
  78. % F = fitted(lme);
  79. % R = response(lme);
  80. % figure();
  81. % plot(R,F,'rx')
  82. % xlabel('Response')
  83. % ylabel('Fitted')
  85. %%%%%%%%%%% plot residuals %%%%%%%%%%%%%%%%%%%%%%%%%
  86. % figure();
  87. % plotResiduals(lme,'fitted')
  90. %%%%%%%%%%%%%%save output in file %%%%%%%%%%%%%%%%%%%%%%%
  91. % mdlOutput = evalc('disp(lme2)');
  92. % fid = fopen("Results\Model6.txt",'wt');
  93. % fprintf(fid,'%s',mdlOutput);
  94. % fclose(fid);

fitMixedModel.m at commit f216ea9, under BSD-2-Clause · at the source

Overview

Authors: Samar Samy Zekerallah1, Anna Alexandra Maxion2,3, Jana Zweerings1, Paula Teucher1,4, Klaus Mathiak1, Ekaterina Kutafina5, Arnim Johannes Gaebler1,6
  1. Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
  2. Joint Research Center for Computational Biomedicine, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
  3. Scientific Center for Neuropathic Pain Aachen, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
  4. Center for Human Genetics and Genomic Medicine, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
  5. Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
  6. Institute for Neurophysiology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
Institutions: RWTH Aachen University (Germany); University of Cologne (Germany); University Hospital Cologne (Germany)
Journal: Frontiers in psychiatry, volume 17, article 1737357
Dates: received 1 November 2025; accepted 25 May 2026; published online 19 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fpsyt.2026.1737357 · PMID 42404718 · PMCID PMC13328094 · OpenAlex W7165173557
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Statistics, Preprocessing, Spectral & time-frequency
Keywords: biomarkers, linear mixed-effects models, neurotransmitter systems, personalized psychiatry, pharmaco-EEG, psychotropic medications, receptor profiles, spectral power
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: RWTH Aachen University
Citations: not cited yet (Europe PMC); 79 references in the paper

Abstract

Introduction: Traditional pharmaco-electroencephalography (EEG) studies have mainly examined the effects of psychotropic medications at the level of individual drugs or broad drug classes, limiting biological specificity and clinical translation. This study aimed to determine whether modeling EEG spectral power changes according to the engagement of distinct neurotransmitter systems provides a more mechanistic understanding of psychotropic drug effects in a real-world clinical population.

Methods: We analyzed 4,128 EEG sessions from 2,083 patients in the Temple University Hospital EEG Corpus, a large heterogeneous dataset. EEG data were preprocessed and segmented into canonical frequency bands (delta, theta, alpha, beta, and gamma). Psychotropic medication data were systematically extracted and coded at the receptor level for serotonin, dopamine, norepinephrine, histamine, and acetylcholine systems using the Neuroscience-based Nomenclature framework. Receptor profiles were summarized to represent each patient’s overall neurotransmitter engagement (agonistic, neutral, antagonistic, or mixed). Linear mixed-effects models were applied to assess relationships between neurotransmitter profiles and log-transformed spectral power while controlling for electrode location and patient-level variability.

Results: Frequency- and region-specific EEG patterns were identified across neurotransmitter systems. Dopamine antagonists were associated with higher delta and theta power at central electrode locations and lower alpha power at occipital and temporal locations, whereas dopamine agonists were associated with higher delta activity at occipital locations and increased frontal gamma power. Serotonin antagonists showed associations with elevated slow-wave and alpha power, while serotonin agonists were linked to increased frontal alpha, decreased occipital alpha, and enhanced temporal gamma power. Both norepinephrine antagonists and agonists showed positive relationships with delta power, with a broader topographical pattern for antagonists. Theta power was positively associated with norepinephrine antagonists and negatively associated with norepinephrine agonists. Norepinephrine antagonists were related to lower temporal alpha and higher frontal and parietal gamma power. Histamine antagonists and mixed histaminergic agents were associated with lower delta, theta, and alpha power. Acetylcholine antagonists were linked to higher delta, theta, and alpha power across electrode locations.

Discussion: Modeling psychotropic medication effects on EEG at the neurotransmitter receptor level offers a biologically grounded and clinically relevant improvement over traditional drug class-based approaches. This neurotransmitter-centric framework enhances mechanistic interpretability and may support the development of EEG biomarkers for personalized, mechanism-based psychiatric care.

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 5 matches between paragraphs and lines of code.

rwth-imi/Drug_induced_spectral_changes_TUH

License: BSD-2-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f216ea90f696bc2cf6740d8a49651927b5ee55b4, 12 July 2024
Languages: MATLAB (33)
Size: 36 files, 33 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (9 files), Signal Processing Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files), Deep Learning Toolbox (1 file), Wavelet Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
35 files
At the source: github.com/rwth-imi/Drug_induced_spectral_changes_TUH

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;
  • 33 scripts, each with its path and the digest of its content;
  • 5 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 statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

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

Data Availability Statement

All code used for data analysis can be retrieved from https://github.com/rwth-imi/Drug_induced_spectral_changes_TUH.

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

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 2, 28 September 2026

  • Funding: added RWTH Aachen University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 8 keywords, 78 references.

Cite

This paper

Zekerallah, S. S., Maxion, A. A., Zweerings, J., Teucher, P., Mathiak, K., Kutafina, E., & Gaebler, A. J. (2026). A computational pipeline for a neurotransmitter-centric analysis of the effects of psychiatric medication on EEG spectral power. Frontiers in psychiatry, 17, 1737357. https://doi.org/10.3389/fpsyt.2026.1737357

BibTeX

@article{zekerallah2026computational,
author = {Zekerallah, Samar Samy and Maxion, Anna Alexandra and Zweerings, Jana and Teucher, Paula and Mathiak, Klaus and Kutafina, Ekaterina and Gaebler, Arnim Johannes},
title = {{A computational pipeline for a neurotransmitter-centric analysis of the effects of psychiatric medication on EEG spectral power}},
journal = {Frontiers in psychiatry},
year = {2026},
month = jun,
volume = {17},
pages = {1737357},
publisher = {Frontiers Media SA},
issn = {1664-0640},
doi = {10.3389/fpsyt.2026.1737357},
url = {https://doi.org/10.3389/fpsyt.2026.1737357},
pmid = {42404718},
pmcid = {PMC13328094}
}

RIS

TY - JOUR
AU - Zekerallah, Samar Samy
AU - Maxion, Anna Alexandra
AU - Zweerings, Jana
AU - Teucher, Paula
AU - Mathiak, Klaus
AU - Kutafina, Ekaterina
AU - Gaebler, Arnim Johannes
TI - A computational pipeline for a neurotransmitter-centric analysis of the effects of psychiatric medication on EEG spectral power
T2 - Frontiers in psychiatry
J2 - Front Psychiatry
PY - 2026
DA - 2026/06/19
VL - 17
SP - 1737357
SN - 1664-0640
PB - Frontiers Media SA
DO - 10.3389/fpsyt.2026.1737357
UR - https://doi.org/10.3389/fpsyt.2026.1737357
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fpsyt.2026.1737357",
"type": "article-journal",
"title": "A computational pipeline for a neurotransmitter-centric analysis of the effects of psychiatric medication on EEG spectral power",
"container-title": "Frontiers in psychiatry",
"author": [
{
"family": "Zekerallah",
"given": "Samar Samy"
},
{
"family": "Maxion",
"given": "Anna Alexandra"
},
{
"family": "Zweerings",
"given": "Jana"
},
{
"family": "Teucher",
"given": "Paula"
},
{
"family": "Mathiak",
"given": "Klaus"
},
{
"family": "Kutafina",
"given": "Ekaterina"
},
{
"family": "Gaebler",
"given": "Arnim Johannes"
}
],
"container-title-short": "Front Psychiatry",
"volume": "17",
"page": "1737357",
"DOI": "10.3389/fpsyt.2026.1737357",
"PMID": "42404718",
"PMCID": "PMC13328094",
"ISSN": "1664-0640",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fpsyt.2026.1737357",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
19
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1016/j.ebiom.2026.106375 [code]
Brainwaves under medication: revealing class-specific neural signatures of psychotropic medication from 24,000 EEGs.
Journal: EBioMedicine
In common: EEGLAB, Statistics and Machine Learning Toolbox, EEG, 6 references
[2] doi:10.1038/s41398-026-04055-w [code]
Diminished variability of alpha and beta band-limited power as a neural signature in schizophrenia.
Journal: Translational psychiatry
In common: EEGLAB, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, EEG, 2 references
[3] doi:10.1038/s41467-026-71458-0 [code]
Early differential impact of MeCP2 mutations on functional networks in Rett syndrome patient-derived human cortical organoids.
Journal: Nature communications
In common: Deep Learning Toolbox, Wavelet Toolbox, Signal Processing Toolbox, 1 other tool
[4] doi:10.1371/journal.pbio.3003938 [code]
Theta oscillations tag episodic memories for sleep-dependent consolidation.
Journal: PLoS biology
In common: Wavelet Toolbox, EEGLAB, Signal Processing Toolbox, 1 other tool, EEG
[5] doi:10.7554/elife.107088 [code]
Development of auditory and spontaneous movement responses to music over the first postnatal year.
Journal: eLife
In common: Wavelet Toolbox, EEGLAB, Signal Processing Toolbox, 1 other tool, EEG
[6] doi:10.1162/imag.a.1229 [code]
40 Hz audiovisual stimulation improves sustained attention and related brain oscillations.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Wavelet Toolbox, EEGLAB, Signal Processing Toolbox, 1 other tool, EEG
[7] doi:10.1111/psyp.70265 [code]
Neurocognitive Dynamics of Translating Information From a Spatial Map Into Action.
Journal: Psychophysiology
In common: EEGLAB, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, EEG, 2 references
[8] doi:10.1523/eneuro.0032-26.2026 [code]
Deep Learning Discriminates Seizures from Normal Brain Oscillations in the Electroencephalogram of a Rat Model of Post-traumatic Epilepsy.
Journal: eNeuro
In common: Deep Learning Toolbox, Wavelet Toolbox, Signal Processing Toolbox, EEG
[9] doi:10.1162/imag.a.105 [code]
Right posterior theta reflects human parahippocampal phase resetting by salient cues during goal-directed navigation
Journal: n/a
In common: Wavelet Toolbox, EEGLAB, Statistics and Machine Learning Toolbox, EEG, 1 reference
[10] doi:10.1038/s41598-026-49900-6 [code]
Global neural oscillations underlie performance variability and attentional state fluctuations in humans.
Journal: Scientific reports
In common: Wavelet Toolbox, EEGLAB, Signal Processing Toolbox, 1 other tool

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.