Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ayrolles, Anaël, Brun, Florence, Chen, Phoebe, Djalovski, Amir, Beauxis, Yann, Delorme, Richard, Bourgeron, Thomas, Dikker, Suzanne, Dumas, Guillaume
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
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
_version_ 1783637709324025856
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
work_keys_str_mv AT ayrollesanael hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis
AT brunflorence hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis
AT chenphoebe hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis
AT djalovskiamir hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis
AT beauxisyann hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis
AT delormerichard hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis
AT bourgeronthomas hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis
AT dikkersuzanne hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis
AT dumasguillaume hypypahyperscanningpythonpipelineforinterbrainconnectivityanalysis