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HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis
The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ ap...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812632/ https://www.ncbi.nlm.nih.gov/pubmed/33031496 http://dx.doi.org/10.1093/scan/nsaa141 |
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author | Ayrolles, Anaël Brun, Florence Chen, Phoebe Djalovski, Amir Beauxis, Yann Delorme, Richard Bourgeron, Thomas Dikker, Suzanne Dumas, Guillaume |
author_facet | Ayrolles, Anaël Brun, Florence Chen, Phoebe Djalovski, Amir Beauxis, Yann Delorme, Richard Bourgeron, Thomas Dikker, Suzanne Dumas, Guillaume |
author_sort | Ayrolles, Anaël |
collection | PubMed |
description | The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, ‘hyperscanning’ setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such ‘inter-brain connectivity analysis’, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses. |
format | Online Article Text |
id | pubmed-7812632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-78126322021-01-25 HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis Ayrolles, Anaël Brun, Florence Chen, Phoebe Djalovski, Amir Beauxis, Yann Delorme, Richard Bourgeron, Thomas Dikker, Suzanne Dumas, Guillaume Soc Cogn Affect Neurosci Original Manuscript The bulk of social neuroscience takes a ‘stimulus-brain’ approach, typically comparing brain responses to different types of social stimuli, but most of the time in the absence of direct social interaction. Over the last two decades, a growing number of researchers have adopted a ‘brain-to-brain’ approach, exploring similarities between brain patterns across participants as a novel way to gain insight into the social brain. This methodological shift has facilitated the introduction of naturalistic social stimuli into the study design (e.g. movies) and, crucially, has spurred the development of new tools to directly study social interaction, both in controlled experimental settings and in more ecologically valid environments. Specifically, ‘hyperscanning’ setups, which allow the simultaneous recording of brain activity from two or more individuals during social tasks, has gained popularity in recent years. However, currently, there is no agreed-upon approach to carry out such ‘inter-brain connectivity analysis’, resulting in a scattered landscape of analysis techniques. To accommodate a growing demand to standardize analysis approaches in this fast-growing research field, we have developed Hyperscanning Python Pipeline, a comprehensive and easy open-source software package that allows (social) neuroscientists to carry-out and to interpret inter-brain connectivity analyses. Oxford University Press 2020-10-08 /pmc/articles/PMC7812632/ /pubmed/33031496 http://dx.doi.org/10.1093/scan/nsaa141 Text en © The Author(s) 2020. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Manuscript Ayrolles, Anaël Brun, Florence Chen, Phoebe Djalovski, Amir Beauxis, Yann Delorme, Richard Bourgeron, Thomas Dikker, Suzanne Dumas, Guillaume HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis |
title | HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis |
title_full | HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis |
title_fullStr | HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis |
title_full_unstemmed | HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis |
title_short | HyPyP: a Hyperscanning Python Pipeline for inter-brain connectivity analysis |
title_sort | hypyp: a hyperscanning python pipeline for inter-brain connectivity analysis |
topic | Original Manuscript |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812632/ https://www.ncbi.nlm.nih.gov/pubmed/33031496 http://dx.doi.org/10.1093/scan/nsaa141 |
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