Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation (2024)

Miljevic, A., Bailey, N. W., Vila-Rodriguez, F., Herring, S. E., & Fitzgerald, P. B. (2022). Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 7(6), 546-554. https://doi.org/10.1016/j.bpsc.2021.10.017

Miljevic, Aleksandra ; Bailey, Neil W. ; Vila-Rodriguez, Fidel et al. / Electroencephalographic Connectivity : A Fundamental Guide and Checklist for Optimal Study Design and Evaluation. In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2022 ; Vol. 7, No. 6. pp. 546-554.

@article{472523f90f5c4ac08a54e016e6d73b71,

title = "Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation",

abstract = "Brain connectivity can be estimated through many analyses applied to electroencephalography (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exists. Heterogeneity in conceptualization of connectivity measures, data collection, or data preprocessing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artifact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies being made more synthesizable and comparable despite variations in the methodology underlying connectivity estimates.",

keywords = "Brain, Connectivity, Connectivity metrics, EEG, EEG analysis, EEG connectivity, EEG processing, Electroencephalography, Methodology",

author = "Aleksandra Miljevic and Bailey, {Neil W.} and Fidel Vila-Rodriguez and Herring, {Sally E.} and Fitzgerald, {Paul B.}",

note = "Publisher Copyright: {\textcopyright} 2021",

year = "2022",

month = jun,

doi = "10.1016/j.bpsc.2021.10.017",

language = "English",

volume = "7",

pages = "546--554",

journal = "Biological Psychiatry: Cognitive Neuroscience and Neuroimaging",

issn = "2451-9022",

publisher = "Elsevier Inc.",

number = "6",

}

Miljevic, A, Bailey, NW, Vila-Rodriguez, F, Herring, SE & Fitzgerald, PB 2022, 'Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation', Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 7, no. 6, pp. 546-554. https://doi.org/10.1016/j.bpsc.2021.10.017

Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation. / Miljevic, Aleksandra; Bailey, Neil W.; Vila-Rodriguez, Fidel et al.
In: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, Vol. 7, No. 6, 06.2022, p. 546-554.

Research output: Contribution to journal › Review article › peer-review

TY - JOUR

T1 - Electroencephalographic Connectivity

T2 - A Fundamental Guide and Checklist for Optimal Study Design and Evaluation

AU - Miljevic, Aleksandra

AU - Bailey, Neil W.

AU - Vila-Rodriguez, Fidel

AU - Herring, Sally E.

AU - Fitzgerald, Paul B.

N1 - Publisher Copyright:© 2021

PY - 2022/6

Y1 - 2022/6

N2 - Brain connectivity can be estimated through many analyses applied to electroencephalography (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exists. Heterogeneity in conceptualization of connectivity measures, data collection, or data preprocessing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artifact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies being made more synthesizable and comparable despite variations in the methodology underlying connectivity estimates.

AB - Brain connectivity can be estimated through many analyses applied to electroencephalography (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exists. Heterogeneity in conceptualization of connectivity measures, data collection, or data preprocessing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artifact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies being made more synthesizable and comparable despite variations in the methodology underlying connectivity estimates.

KW - Brain

KW - Connectivity

KW - Connectivity metrics

KW - EEG

KW - EEG analysis

KW - EEG connectivity

KW - EEG processing

KW - Electroencephalography

KW - Methodology

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U2 - 10.1016/j.bpsc.2021.10.017

DO - 10.1016/j.bpsc.2021.10.017

M3 - Review article

SN - 2451-9022

VL - 7

SP - 546

EP - 554

JO - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

JF - Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

IS - 6

ER -

Miljevic A, Bailey NW, Vila-Rodriguez F, Herring SE, Fitzgerald PB. Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2022 Jun;7(6):546-554. doi: 10.1016/j.bpsc.2021.10.017