Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis
•Increased generalised theta with posterior pre-alpha relative power are reproducible findings in Parkinson’s Disease.•Functional Data Analysis is a potential tool to overcome potential limitations of epochs averaging in rs-EEG spectral analysis.•Functional Data Analysis constitutes a reliable metho...
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Published in | Clinical neurophysiology Vol. 151; pp. 28 - 40 |
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Main Authors | , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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Netherlands
Elsevier B.V
01.07.2023
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Abstract | •Increased generalised theta with posterior pre-alpha relative power are reproducible findings in Parkinson’s Disease.•Functional Data Analysis is a potential tool to overcome potential limitations of epochs averaging in rs-EEG spectral analysis.•Functional Data Analysis constitutes a reliable method to analyse epoch-to-epoch variability of the rs-EEG.
This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach.
We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature.
For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs.
Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD.
Rs-EEG theta and pre-alpha findings are generalisable in PD.
FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG. |
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AbstractList | •Increased generalised theta with posterior pre-alpha relative power are reproducible findings in Parkinson’s Disease.•Functional Data Analysis is a potential tool to overcome potential limitations of epochs averaging in rs-EEG spectral analysis.•Functional Data Analysis constitutes a reliable method to analyse epoch-to-epoch variability of the rs-EEG.
This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach.
We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature.
For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs.
Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD.
Rs-EEG theta and pre-alpha findings are generalisable in PD.
FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG. This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD. Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG. OBJECTIVEThis study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach.METHODSWe included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature.RESULTSFor averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs.CONCLUSIONSIncreased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD.SIGNIFICANCERs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG. |
Author | Aarsland, Dag Bonanni, Laura Lopera, Francisco Suarez-Revelo, Jazmin Ximena Brønnick, Kolbjørn Loaiza-López, Daniel Pineda-Salazar, David Antonio Zapata Saldarriaga, Luisa María Henao Isaza, Verónica Borda, Miguel Germán Ochoa-Gomez, John Fredy Jaramillo-Jimenez, Alberto Cadavid Castro, Valeria Tobón Quintero, Carlos Andrés Tovar-Rios, Diego A. Mantilla-Ramos, Yorguin-Jose Bocanegra, Yamile Ospina, Johann Alexis |
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Keywords | Functional Data Analysis Alpha rhythm Electroencephalography Theta rhythm Parkinson's Disease |
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Snippet | •Increased generalised theta with posterior pre-alpha relative power are reproducible findings in Parkinson’s Disease.•Functional Data Analysis is a potential... This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects... OBJECTIVEThis study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy... |
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SubjectTerms | Alpha rhythm Electroencephalography Functional Data Analysis Humans Medicin och hälsovetenskap Parkinson Disease - diagnosis Parkinson's Disease Reproducibility of Results Theta rhythm |
Title | Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis |
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