Reliability of Resting-State Microstate Features in Electroencephalography
Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use...
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| Published in | PloS one Vol. 9; no. 12; p. e114163 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Public Library of Science
05.12.2014
Public Library of Science (PLoS) |
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| Abstract | Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis.
We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes.
The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's α > 0.8, SEM ≈ 10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α > 0.9). All features had high test-retest reliability with 19 and 8 electrodes.
High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health. |
|---|---|
| AbstractList | Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's [alpha] and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's [alpha]>0.8, SEM [almost equal to]10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's [alpha]>0.9). All features had high test-retest reliability with 19 and 8 electrodes. High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health. Background Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states (“microstates”) that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. Methods We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k -means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. Results The approach of identifying a single set of “global” microstate maps showed the highest reliability (mean Cronbach's α>0.8, SEM ≈10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α>0.9). All features had high test-retest reliability with 19 and 8 electrodes. Conclusions High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health. Background Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states (“microstates”) that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. Methods We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. Results The approach of identifying a single set of “global” microstate maps showed the highest reliability (mean Cronbach's α>0.8, SEM ≈10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α>0.9). All features had high test-retest reliability with 19 and 8 electrodes. Conclusions High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health. Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's α > 0.8, SEM ≈ 10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α > 0.9). All features had high test-retest reliability with 19 and 8 electrodes. High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health. Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis.BACKGROUNDElectroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis.We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes.METHODSWe analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes.The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's α > 0.8, SEM ≈ 10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α > 0.9). All features had high test-retest reliability with 19 and 8 electrodes.RESULTSThe approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's α > 0.8, SEM ≈ 10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α > 0.9). All features had high test-retest reliability with 19 and 8 electrodes.High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health.CONCLUSIONSHigh test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health. Background Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. Methods We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's [alpha] and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. Results The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's [alpha]>0.8, SEM [almost equal to]10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's [alpha]>0.9). All features had high test-retest reliability with 19 and 8 electrodes. Conclusions High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health. Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis.We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes.The approach of identifying a single set of "global" microstate maps showed the highest reliability (mean Cronbach's α > 0.8, SEM ≈ 10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α > 0.9). All features had high test-retest reliability with 19 and 8 electrodes.High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health. |
| Audience | Academic |
| Author | Khanna, Arjun Pascual-Leone, Alvaro Farzan, Faranak |
| AuthorAffiliation | University of Bern, Switzerland 1 Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America 2 Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada |
| AuthorAffiliation_xml | – name: 2 Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada – name: 1 Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America – name: University of Bern, Switzerland |
| Author_xml | – sequence: 1 givenname: Arjun surname: Khanna fullname: Khanna, Arjun – sequence: 2 givenname: Alvaro surname: Pascual-Leone fullname: Pascual-Leone, Alvaro – sequence: 3 givenname: Faranak surname: Farzan fullname: Farzan, Faranak |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25479614$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2014 Public Library of Science 2014 Khanna et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2014 Khanna et al 2014 Khanna et al |
| Copyright_xml | – notice: COPYRIGHT 2014 Public Library of Science – notice: 2014 Khanna et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2014 Khanna et al 2014 Khanna et al |
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| DOI | 10.1371/journal.pone.0114163 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Conceived and designed the experiments: FF APL. Performed the experiments: FF. Analyzed the data: FF AK. Contributed reagents/materials/analysis tools: APL. Wrote the paper: AK FF. Competing Interests: AK and FF have no conflict of interest to disclose. APL serves on the scientific advisory boards for Nexstim, Neuronix, starlab Neuroscience, Neosync, and Novavision, and is an inventor on patents and patent applications related to noninvasive brain stimulation and real-time integration of TMS with EEG and fMRI. |
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| Snippet | Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered in a number... Background Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states ("microstates") that are altered... Background Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states (“microstates”) that are altered... Background Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states (“microstates”) that are altered... |
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| SubjectTerms | Adult Algorithms Bioindicators Biomarkers Brain Brain Mapping Cluster analysis Clustering Consistency EEG Electrodes Electroencephalography Electroencephalography - methods Error analysis Feature extraction Female Healthy Volunteers Humans Mathematical analysis Medicine and Health Sciences Mental disorders Nervous system diseases Neurophysiology Recording Reliability analysis Rest - physiology Standard error Vector quantization |
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| Title | Reliability of Resting-State Microstate Features in Electroencephalography |
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