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Brainwaves under medication: revealing class-specific neural signatures of psychotropic medication from 24,000 EEGs.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › Medicine groups ↔ Brainwaves_Under_Medication_Visualisation_and_PCA_analysis_code_clean.ipynb, lines 172–219 · score 0.83 · sedative hypnotic, NaSSA, AChE, Anticholinergic, opioid, SNRIs
  2. [2] § Methods › Preprocessing ↔ preprocessing_REST_ASR_flexible_commented.m, lines 64–200 · score 0.80 · high pass filter, window criterion, channel interpolation, ASR, pipeline, FASTER
  3. [3] § Methods › Preprocessing ↔ preprocessing_REST_ASR_flexible_commented.m, lines 1–62 · score 0.71 · CleanLine, infinity, kurtosis, EEGLAB, plugin, noise
  4. [4] § Results › Data ↔ Brainwaves_Under_Medication_Visualisation_and_PCA_analysis_code_clean.ipynb, lines 172–219 · score 0.67 · AED Ca, AED Na, NaSSA, SARI, AP, atypical
  5. [5] § Methods › Dimensionality reduction ↔ Brainwaves_Under_Medication_code_commented.m, lines 253–282 · score 0.62 · confidence interval, Principal Component, uncorrelated, coefficients, variance, PCA
  6. [6] § Methods › EEG signal features ↔ Brainwaves_Under_Medication_code_commented.m, lines 37–58 · score 0.62 · feature exceeded, standard deviations, VAR, outliers, scored, patients
  7. [7] § Methods › Medicine groups ↔ balance_groups_meds_DN.m, lines 1–55 · score 0.60 · Chi squared, classified, medication classes, binary, psychotropic, diagnosis
  8. [8] § Results › Data ↔ Brainwaves_Under_Medication_Visualisation_and_PCA_analysis_code_clean.ipynb, lines 938–1070 · score 0.56 · hierarchical regression model, Holm, mixed, dimensionality, PCA, matched

Paper

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

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The authors' code

Jupyter notebook · 1,338 lines · 16 MB · no license · 3 matches

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It can be read at the source: Brainwaves_Under_Medication_Visualisation_and_PCA_analysis_code_clean.ipynb.

Overview

Authors: Magdalena Szponar1, Patrycja Dzianok1,2, Bartłomiej Gmaj3, Wojciech Jernajczyk4, Jan Kamiński5,1
ORCID iDs: Bartłomiej Gmaj
  1. Laboratory of Neurophysiology of Mind, Nencki Institute of Experimental Biology, Warsaw, Poland
  2. International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
  3. Department of Psychiatry, Medical University of Warsaw, Warsaw, Poland
  4. Department of Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warsaw, Poland
  5. Department of Neurosurgery, SUNY Upstate Medical University, Syracuse, NY, USA
Institutions: Instytut Biologii Doświadczalnej im. Marcelego Nenckiego (Poland); International Institute of Molecular and Cell Biology (Poland); Medical University of Warsaw (Poland); Institute of Psychiatry and Neurology (Poland); SUNY Upstate Medical University (United States)
Journal: EBioMedicine, volume 130, article 106375
Dates: received 8 December 2025; accepted 23 June 2026; published online 9 July 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.ebiom.2026.106375 · PMID 42424703 · PMCID PMC13380497 · OpenAlex W7167808942
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), clinical / translational (subfield)
Methods: Connectivity, Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Complexity, Physiology & signal measures
Keywords: Psychotropic drugs, Psychiatry, Pharmacotherapy, Mental disorders, Electroencephalography (EEG), PharmacoEEG
MeSH: Brain*, Brain Waves*, Electroencephalography*, Mental Disorders*, Psychotropic Drugs*, Cross-Sectional Studies, Female, Humans, Male (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Funds for Smart Economy; This publication was financially supported by the Foundation for Polish Science (FNP) within the BRAINCITY IRAP project, funded by the EU under the European Funds for Smart Economy programme
Citations: cited by 1 paper (Europe PMC); 99 references in the paper

Abstract

Background: Psychotropic medications remain foundational in psychiatric care, yet the neurophysiological mechanisms through which they exert therapeutic and adverse effects are still poorly characterised, limiting the field's ability to optimise treatment selection and monitoring. Electroencephalography (EEG) offers a non-invasive, real-time window into brain function that could support more precise, mechanism-informed prescribing; however, progress has been constrained by the absence of sufficiently large and systematically analysed pharmaco-EEG datasets.

Methods: In this cross-sectional observational study, we analysed over 24,000 clinical EEG recordings (∼6000 h of data) obtained across a wide range of psychiatric diagnoses and medication regimens. We compared more than 75,000 spectral, connectivity, and nonlinear EEG features across major drug classes, including benzodiazepines, SSRIs, antipsychotics, and anticonvulsants.

Findings: Dimensionality-reduced analyses revealed robust, class-specific neurophysiological signatures that can be linked to psychotropic drugs' mechanisms of action: benzodiazepines increased beta and decreased theta–alpha power; SSRIs enhanced gamma-band coherence; and antipsychotics and anticonvulsants produced marked slow-wave amplification and reductions in signal complexity. All results are made publicly accessible through an interactive resource (BrainwavesRX), enabling clinicians and researchers to explore medication-specific EEG effects at multiple levels of granularity.

Interpretation: By establishing a population-level reference atlas of psychotropic medication effects on human neural dynamics, this study provides an important foundation for future studies leveraging EEG to predict treatment response, detect insufficient or excessive pharmacological effects, and ultimately advance the development of individualised, data-driven psychiatric care.

Funding: The publication was prepared as part of Foundation of Polish Science's Proof of Concept (FENG.02.01-IP.05-0010/24) and BRAINCITY IRAP (FENG.02.07-IP.05-0179/23) projects.

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 8 matches between paragraphs and lines of code.

labianca/EEG-psychotropic-medications

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 99898aadd758379da6d9f23b98763479c731c631, 7 July 2026
Languages: MATLAB (5), Jupyter (1)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: “Data sharing statement”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (1 file), h5py (1 file), ICLabel (1 file), Statistics and Machine Learning Toolbox (1 file), Matplotlib (1 file), MNE-Python (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files, not copied: shown from their source

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At the source: github.com/labianca/EEG-psychotropic-medications

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;
  • 6 scripts, each with its path and the digest of its content;
  • 8 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 sharing statement

The results from all comparisons between all drug classes are available on the interactive website, at https://brainwavesrx.nencki.edu.pl/.

The code used for data preprocessing and statistical analysis is available on GitHub at https://github.com/labianca/EEG-psychotropic-medications.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 6 keywords, 9 MeSH terms, 2 funders, 91 references.

Cite

This paper

Szponar, M., Dzianok, P., Gmaj, B., Jernajczyk, W., & Kamiński, J. (2026). Brainwaves under medication: revealing class-specific neural signatures of psychotropic medication from 24,000 EEGs. EBioMedicine, 130, 106375. https://doi.org/10.1016/j.ebiom.2026.106375

BibTeX

@article{szponar2026brainwaves,
author = {Szponar, Magdalena and Dzianok, Patrycja and Gmaj, Bartłomiej and Jernajczyk, Wojciech and Kamiński, Jan},
title = {{Brainwaves under medication: revealing class-specific neural signatures of psychotropic medication from 24,000 EEGs}},
journal = {EBioMedicine},
year = {2026},
month = jul,
volume = {130},
pages = {106375},
publisher = {Elsevier},
issn = {2352-3964},
doi = {10.1016/j.ebiom.2026.106375},
url = {https://doi.org/10.1016/j.ebiom.2026.106375},
pmid = {42424703},
pmcid = {PMC13380497}
}

RIS

TY - JOUR
AU - Szponar, Magdalena
AU - Dzianok, Patrycja
AU - Gmaj, Bartłomiej
AU - Jernajczyk, Wojciech
AU - Kamiński, Jan
TI - Brainwaves under medication: revealing class-specific neural signatures of psychotropic medication from 24,000 EEGs
T2 - EBioMedicine
J2 - eBioMedicine
PY - 2026
DA - 2026/07/09
VL - 130
SP - 106375
SN - 2352-3964
PB - Elsevier
DO - 10.1016/j.ebiom.2026.106375
UR - https://doi.org/10.1016/j.ebiom.2026.106375
LA - en
ER -

CSL-JSON

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