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Enregistrement W4313585849 · doi:10.3389/fnsys.2022.1105923

Editorial: Magnetoencephalography for social science

2023· editorial· en· W4313585849 sur OpenAlexaff
Jonathan Lévy, Iiro P. Jääskeläinen, Margot J. Taylor

Notice bibliographique

RevueFrontiers in Systems Neuroscience · 2023
Typeeditorial
Langueen
DomaineNeuroscience
ThématiqueFunctional Brain Connectivity Studies
Établissements canadiensHospital for Sick ChildrenUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMagnetoencephalographyFront (military)Volume (thermodynamics)NeuroscienceCognitive sciencePsychologyPolitical sciencePhysicsOceanographyGeology

Résumé

récupéré en direct d'OpenAlex

The past two decades have seen the growth of the field of social and affective neuroscience. By using a range of neuroimaging tools, research in this field has progressed in uncovering the neural substrates and systems involved in social and affective processes (Cacioppo and Decety, 2011;Singer, 2012;Stanley and Adolphs, 2013). Yet, despite half a century of invaluable contribution to various aspects in neuroscience, magnetoencephalography (MEG) (Hämäläinen et al., 1993;Baillet, 2017;Hari and Puce, 2017;Gross, 2019) has only recently been applied to investigating social and affective processes: From probing neural dynamics, through quantifying connectivity patterns of large-scale brain systems, to representing neural generators of multiple rhythms and oscillations and how they relate to social and affective behaviours. Examples range from studies on interpersonal interaction (Hari et al., 2015;Levy et al., 2017), developmental emotion inhibition (Vandewouw et al., 2021), watching social movies (Chang et al., 2015;Levy et al., 2016b), theory of mind (Mossad et al., 2021(Mossad et al., , 2017Vistoli et al., 2011Yuk et al., 2020; intergroup empathy (Levy et al., 2016a;Zhou et al., 2020), emotional face processing (Safar et al., 2021;Roesmann et al., 2020;Im et al., 2021), intergroup bias and interventions (Levy et al., 2021(Levy et al., , 2022. These recent works revealed the potential of MEG in uncovering complex neural dynamics underlying psychological processes in the social world (Levy et al., 2020). In this special topic of "Magnetoencephalography for Social Science" we aimed at expanding this literature by editing four MEG studies that investigate various social processes: from morality, through the neural development of social-cognitive functioning and of face processing, to surprise. These studies, which are summarized below, relied on the uniqueness of MEG data to reveal complex neural dynamics underlying these social processes.In one study, Hiraishi and colleagues (Hiraishi et al., 2021) used MEG to distinguish between morally good and bad judgments. They addressed a gap in the literature on the neuroscience of morality that has mainly focused on brain areas using fMRI neuroimaging, while rarely examining the temporal and connectivity patterns related to moral processes.Using MEG, the study revealed differences at regional, temporal and functional connectivity levels.Another study by Sato and colleagues (Sato et al., 2022) investigated a vulnerable population if individuals who were born with very low birth weight. They investigated their functional connectivity at rest and its associations with early nutrition and IQ and behavioural problems. The authors found that at preschool-age, these individuals show altered resting-state connectivity despite IQ and behaviour being in the average range, possibly reflecting functional reorganisation of networks to support social-cognitive and behavioural functioning. The authors emphasized the importance of early postnatal nutrition in the development of resting-state networks that support social functions in childhood.Another paper by Mousavi and colleagues (Mousavi et al., 2022) who analyzed MEG data in an oddball task to investigate the phenomenon of surprise from a detailed neurodynamic outlook. Although surprise has been examined in many studies, the authors set forth an information-theoretical model to describe and predict the surprise level of an external stimulus in MEG data. The results of their analysis found that middle temporal components and the right and left fronto-central regions offer the strongest power for decoding surprise. The authors conclude that this is a practical and rigorous method for evaluating the interactive and social effects of surprising events on the brain.Finally, Chen and colleagues (Chen et al 2022) studied face processing during the developmental period between 1-4 years. Despite this period being characterized by rapid changes in the ability to encode facial information, there are very few studies which investigate the neural processes of face encoding during this age, and this study sought to fill the gap between prior studies in infants and school-aged children. While implementing a longitudinal MEG approach across the first 4 years of life, they examined the maturation of the MEG responses in the fusiform gyri, which are primary nodes in the face-encoding network. The findings reported face-sensitive maturational changes, providing foundational data to the literature.Overall, although MEG studies on social processes are increasing, the contribution of this research topic has been to consolidate this budding literature and increase exposure to this growing field of research. We contend that this has the potential to motivate social neuroscientists to use MEG, on the one hand, and on the other hand motivate MEG scientists to explore social processes. Our ambition is to support the use of this non-invasive method for exploring new horizons that would expand the understanding of social phenomena and their underlying neural mechanisms. Finally, we note that recent technological progress in wearable-MEG, optically-pumped magnetometers, dual-MEG and multiple-brain analyses will certainly yield novel ecologically-valid paradigms that would enhance MEG's ability to emulate real-life validity in the near future (Hari et al., 2015;Brookes et al., 2022).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,129
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesÉtudes des sciences et des technologies
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,126
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,129
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0020,007
Études des sciences et des technologies0,0020,004
Communication savante0,0010,001
Science ouverte0,0030,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,290
Écart entre enseignants0,261 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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