EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation

  • Aleksandra Miljevic
    Correspondence
    Corresponding author: A Miljevic Email: Telephone: +61 03 9805 4163 Address: Epworth Centre for Innovation in Mental Health, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
    Affiliations
    Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
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  • Neil W. Bailey
    Affiliations
    Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
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  • Fidel Vila-Rodriguez
    Affiliations
    Non-Invasive Neurostimulation Therapies Laboratory, Dept. Psychiatry, The University of British Columbia, Vancouver, BC, Canada
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  • Sally E. Herring
    Affiliations
    Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
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  • Paul B. Fitzgerald
    Affiliations
    Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
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Published:November 02, 2021DOI:https://doi.org/10.1016/j.bpsc.2021.10.017

      Abstract

      Brain connectivity can be estimated through many analyses applied to electroencephalographic (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exist. Heterogeneity in conceptualization of connectivity measures, data collection, or data pre-processing 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, artefact 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 to be made more synthesisable and comparable despite variations in the methodology underlying connectivity estimates.

      Keywords

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