Abstract
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.
Original language | English |
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Pages (from-to) | 72-83 |
Number of pages | 12 |
Journal | Social Cognitive and Affective Neuroscience |
Volume | 16 |
Issue number | 1-2 |
DOIs | |
State | Published - 18 Jan 2021 |
Bibliographical note
Publisher Copyright:© 2020 The Author(s) 2020. Published by Oxford University Press.
Funding
This project is supported by the Institut Pasteur, INSERM, FondaMental Foundation, Ecole Normale Supérieure from Lyon, APHP, DHU Protect, Bettencourt-Schueller Foundation, Cognacq Jay Foundation, Conny-Maeva Foundation, Fondation de France, Labex BioPsy, a grant from Pierre Deniker Foundation, a grant from Congrès Français de Psychiatrie CFP-BM-2019-03, National Science Foundation Award #1661016, Netherlands Organisation for Scientific Research Award #406.18.GO.024. GD is funded by the Institute for Data Valorization (IVADO), Montreal, and the Fonds de recherche du Québec (FRQ).
Funders | Funder number |
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Congrès Français de Psychiatrie | CFP-BM-2019-03 |
DHU Protect | |
Labex BioPsy | |
National Science Foundation | 406.18, 1661016 |
École normale supérieure | |
Fondation FondaMental | |
Fondation Cognacq-Jay | |
Institut national de la santé et de la recherche médicale | |
Institut Pasteur | |
Fondation de France | |
Fondation Bettencourt Schueller | |
Fondation Pierre Deniker pour la Recherche et la Prévention en Santé Mentale | |
Institut de Valorisation des Données | |
Conny-Maeva Charitable Foundation | |
Fonds de recherche du Québec |
Keywords
- Analysis pipeline
- Hyperscanning
- Inter-brain connectivity
- Non-parametric statistics
- Python