dc.contributor.author | Nau, Matthias | |
dc.date.accessioned | 2020-10-30T08:18:12Z | |
dc.date.available | 2020-10-30T08:18:12Z | |
dc.date.created | 2020-10-29T13:33:19Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 2041-1723 | |
dc.identifier.uri | https://hdl.handle.net/11250/2685796 | |
dc.description.abstract | The brain derives cognitive maps from sensory experience that guide memory formation and behavior. Despite extensive efforts, it still remains unclear how the underlying population activity unfolds during spatial navigation and how it relates to memory performance. To examine these processes, we combined 7T-fMRI with a kernel-based encoding model of virtual navigation to map world-centered directional tuning across the human cortex. First, we present an in-depth analysis of directional tuning in visual, retrosplenial, parahippocampal and medial temporal cortices. Second, we show that tuning strength, width and topology of this directional code during memory-guided navigation depend on successful encoding of the environment. Finally, we show that participants’ locomotory state influences this tuning in sensory and mnemonic regions such as the hippocampus. We demonstrate a direct link between neural population tuning and human cognition, where high-level memory processing interacts with network-wide visuospatial coding in the service of behavior. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Nature Communications | en_US |
dc.relation.uri | https://www.nature.com/articles/s41467-020-17000-2 | |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Behavior-dependent directional tuning in the human visual-navigation network | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | Nature Communications | en_US |
dc.identifier.doi | 10.1038/s41467-020-17000-2 | |
dc.identifier.cristin | 1843281 | |
dc.description.localcode | © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/. | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |