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DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-c...

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Autores principales: Gil Ávila, Cristina, Bott, Felix S., Tiemann, Laura, Hohn, Vanessa D., May, Elisabeth S., Nickel, Moritz M., Zebhauser, Paul Theo, Gross, Joachim, Ploner, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495446/
https://www.ncbi.nlm.nih.gov/pubmed/37696851
http://dx.doi.org/10.1038/s41597-023-02525-0
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author Gil Ávila, Cristina
Bott, Felix S.
Tiemann, Laura
Hohn, Vanessa D.
May, Elisabeth S.
Nickel, Moritz M.
Zebhauser, Paul Theo
Gross, Joachim
Ploner, Markus
author_facet Gil Ávila, Cristina
Bott, Felix S.
Tiemann, Laura
Hohn, Vanessa D.
May, Elisabeth S.
Nickel, Moritz M.
Zebhauser, Paul Theo
Gross, Joachim
Ploner, Markus
author_sort Gil Ávila, Cristina
collection PubMed
description Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.
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spelling pubmed-104954462023-09-13 DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience Gil Ávila, Cristina Bott, Felix S. Tiemann, Laura Hohn, Vanessa D. May, Elisabeth S. Nickel, Moritz M. Zebhauser, Paul Theo Gross, Joachim Ploner, Markus Sci Data Analysis Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function. Nature Publishing Group UK 2023-09-11 /pmc/articles/PMC10495446/ /pubmed/37696851 http://dx.doi.org/10.1038/s41597-023-02525-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Analysis
Gil Ávila, Cristina
Bott, Felix S.
Tiemann, Laura
Hohn, Vanessa D.
May, Elisabeth S.
Nickel, Moritz M.
Zebhauser, Paul Theo
Gross, Joachim
Ploner, Markus
DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience
title DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience
title_full DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience
title_fullStr DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience
title_full_unstemmed DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience
title_short DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience
title_sort discover-eeg: an open, fully automated eeg pipeline for biomarker discovery in clinical neuroscience
topic Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10495446/
https://www.ncbi.nlm.nih.gov/pubmed/37696851
http://dx.doi.org/10.1038/s41597-023-02525-0
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