Wibral, Michael

[["dc.bibliographiccitation.journal","eLife"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Schaum, Michael"],["dc.contributor.author","Pinzuti, Edoardo"],["dc.contributor.author","Sebastian, Alexandra"],["dc.contributor.author","Lieb, Klaus"],["dc.contributor.author","Fries, Pascal"],["dc.contributor.author","Mobascher, Arian"],["dc.contributor.author","Jung, Patrick"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Tüscher, Oliver"],["dc.date.accessioned","2021-06-01T09:42:45Z"],["dc.date.available","2021-06-01T09:42:45Z"],["dc.date.issued","2021"],["dc.description.abstract","Motor inhibitory control implemented as response inhibition is an essential cognitive function required to dynamically adapt to rapidly changing environments. Despite over a decade of research on the neural mechanisms of response inhibition, it remains unclear, how exactly response inhibition is initiated and implemented. Using a multimodal MEG/fMRI approach in 59 subjects, our results reliably reveal that response inhibition is initiated by the right inferior frontal gyrus (rIFG) as a form of attention-independent top-down control that involves the modulation of beta-band activity. Furthermore, stopping performance was predicted by beta-band power, and beta-band connectivity was directed from rIFG to pre-supplementary motor area (pre-SMA), indicating rIFG’s dominance over pre-SMA. Thus, these results strongly support the hypothesis that rIFG initiates stopping, implemented by beta-band oscillations with potential to open up new ways of spatially localized oscillation-based interventions."],["dc.identifier.doi","10.7554/eLife.61679"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85341"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.eissn","2050-084X"],["dc.title","Right inferior frontal gyrus implements motor inhibitory control via beta-band oscillations in humans"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e1008526"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Pinzuti, Edoardo"],["dc.contributor.author","Wollstadt, Patricia"],["dc.contributor.author","Gutknecht, Aaron"],["dc.contributor.author","Tüscher, Oliver"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.editor","Marinazzo, Daniele"],["dc.date.accessioned","2021-04-14T08:31:52Z"],["dc.date.available","2021-04-14T08:31:52Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1371/journal.pcbi.1008526"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83737"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1553-7358"],["dc.title","Measuring spectrally-resolved information transfer"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]