Cargando…

Racial disparities in EEG research and their implications for our understanding of the maternal brain

Racial disparities in maternal health are alarming and persistent. Use of electroencephalography (EEG) and event-related potentials (ERPs) to understand the maternal brain can improve our knowledge of maternal health by providing insight into mechanisms underlying maternal well-being, including impl...

Descripción completa

Detalles Bibliográficos
Autores principales: Penner, Francesca, Wall, Kathryn M., Guan, Kathleen W., Huang, Helen J., Richardson, Lietsel, Dunbar, Angel S., Groh, Ashley M., Rutherford, Helena J. V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684773/
https://www.ncbi.nlm.nih.gov/pubmed/36414837
http://dx.doi.org/10.3758/s13415-022-01040-w
_version_ 1784835365720293376
author Penner, Francesca
Wall, Kathryn M.
Guan, Kathleen W.
Huang, Helen J.
Richardson, Lietsel
Dunbar, Angel S.
Groh, Ashley M.
Rutherford, Helena J. V.
author_facet Penner, Francesca
Wall, Kathryn M.
Guan, Kathleen W.
Huang, Helen J.
Richardson, Lietsel
Dunbar, Angel S.
Groh, Ashley M.
Rutherford, Helena J. V.
author_sort Penner, Francesca
collection PubMed
description Racial disparities in maternal health are alarming and persistent. Use of electroencephalography (EEG) and event-related potentials (ERPs) to understand the maternal brain can improve our knowledge of maternal health by providing insight into mechanisms underlying maternal well-being, including implications for child development. However, systematic racial bias exists in EEG methodology—particularly for Black individuals—and in psychological and health research broadly. This paper discusses these biases in the context of EEG/ERP research on the maternal brain. First, we assess the racial/ethnic diversity of existing ERP studies of maternal neural responding to infant/child emotional expressions, using papers from a recent meta-analysis, finding that the majority of mothers represented in this research are of White/European ancestry and that the racially and ethnically diverse samples that are present are limited in terms of geography. Therefore, our current knowledge base in this area may be biased and not generalizable across racially diverse mothers. We outline factors underlying this problem, beginning with the racial bias in EEG equipment that systematically excludes individuals of African descent, and also considering factors specific to research with mothers. Finally, we highlight recent innovations to EEG hardware to better accommodate diverse hairstyles and textures, and other important steps to increase racial and ethnic representativeness in EEG/ERP research with mothers. We urge EEG/ERP researchers who study the maternal brain—including our own research group—to take action to increase racial diversity so that this research area can confidently inform understanding of maternal health and contribute to minimizing maternal health disparities.
format Online
Article
Text
id pubmed-9684773
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-96847732022-11-28 Racial disparities in EEG research and their implications for our understanding of the maternal brain Penner, Francesca Wall, Kathryn M. Guan, Kathleen W. Huang, Helen J. Richardson, Lietsel Dunbar, Angel S. Groh, Ashley M. Rutherford, Helena J. V. Cogn Affect Behav Neurosci Theoretical Review Racial disparities in maternal health are alarming and persistent. Use of electroencephalography (EEG) and event-related potentials (ERPs) to understand the maternal brain can improve our knowledge of maternal health by providing insight into mechanisms underlying maternal well-being, including implications for child development. However, systematic racial bias exists in EEG methodology—particularly for Black individuals—and in psychological and health research broadly. This paper discusses these biases in the context of EEG/ERP research on the maternal brain. First, we assess the racial/ethnic diversity of existing ERP studies of maternal neural responding to infant/child emotional expressions, using papers from a recent meta-analysis, finding that the majority of mothers represented in this research are of White/European ancestry and that the racially and ethnically diverse samples that are present are limited in terms of geography. Therefore, our current knowledge base in this area may be biased and not generalizable across racially diverse mothers. We outline factors underlying this problem, beginning with the racial bias in EEG equipment that systematically excludes individuals of African descent, and also considering factors specific to research with mothers. Finally, we highlight recent innovations to EEG hardware to better accommodate diverse hairstyles and textures, and other important steps to increase racial and ethnic representativeness in EEG/ERP research with mothers. We urge EEG/ERP researchers who study the maternal brain—including our own research group—to take action to increase racial diversity so that this research area can confidently inform understanding of maternal health and contribute to minimizing maternal health disparities. Springer US 2022-11-22 2023 /pmc/articles/PMC9684773/ /pubmed/36414837 http://dx.doi.org/10.3758/s13415-022-01040-w Text en © The Psychonomic Society, Inc. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Theoretical Review
Penner, Francesca
Wall, Kathryn M.
Guan, Kathleen W.
Huang, Helen J.
Richardson, Lietsel
Dunbar, Angel S.
Groh, Ashley M.
Rutherford, Helena J. V.
Racial disparities in EEG research and their implications for our understanding of the maternal brain
title Racial disparities in EEG research and their implications for our understanding of the maternal brain
title_full Racial disparities in EEG research and their implications for our understanding of the maternal brain
title_fullStr Racial disparities in EEG research and their implications for our understanding of the maternal brain
title_full_unstemmed Racial disparities in EEG research and their implications for our understanding of the maternal brain
title_short Racial disparities in EEG research and their implications for our understanding of the maternal brain
title_sort racial disparities in eeg research and their implications for our understanding of the maternal brain
topic Theoretical Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684773/
https://www.ncbi.nlm.nih.gov/pubmed/36414837
http://dx.doi.org/10.3758/s13415-022-01040-w
work_keys_str_mv AT pennerfrancesca racialdisparitiesineegresearchandtheirimplicationsforourunderstandingofthematernalbrain
AT wallkathrynm racialdisparitiesineegresearchandtheirimplicationsforourunderstandingofthematernalbrain
AT guankathleenw racialdisparitiesineegresearchandtheirimplicationsforourunderstandingofthematernalbrain
AT huanghelenj racialdisparitiesineegresearchandtheirimplicationsforourunderstandingofthematernalbrain
AT richardsonlietsel racialdisparitiesineegresearchandtheirimplicationsforourunderstandingofthematernalbrain
AT dunbarangels racialdisparitiesineegresearchandtheirimplicationsforourunderstandingofthematernalbrain
AT grohashleym racialdisparitiesineegresearchandtheirimplicationsforourunderstandingofthematernalbrain
AT rutherfordhelenajv racialdisparitiesineegresearchandtheirimplicationsforourunderstandingofthematernalbrain