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Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

Resting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions...

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Autores principales: Szabó, Dóra, Czeibert, Kálmán, Kettinger, Ádám, Gácsi, Márta, Andics, Attila, Miklósi, Ádám, Kubinyi, Enikő
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813298/
https://www.ncbi.nlm.nih.gov/pubmed/31649271
http://dx.doi.org/10.1038/s41598-019-51752-2
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author Szabó, Dóra
Czeibert, Kálmán
Kettinger, Ádám
Gácsi, Márta
Andics, Attila
Miklósi, Ádám
Kubinyi, Enikő
author_facet Szabó, Dóra
Czeibert, Kálmán
Kettinger, Ádám
Gácsi, Márta
Andics, Attila
Miklósi, Ádám
Kubinyi, Enikő
author_sort Szabó, Dóra
collection PubMed
description Resting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained individuals of both sexes and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. In this exploratory study, using resting-state functional magnetic resonance imaging (rs-fMRI), we found several such networks: a network involving prefrontal, anterior cingulate, posterior cingulate and hippocampal regions; sensorimotor (SMN), auditory (AUD), frontal (FRO), cerebellar (CER) and striatal networks. The network containing posterior cingulate regions, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.
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spelling pubmed-68132982019-10-30 Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks Szabó, Dóra Czeibert, Kálmán Kettinger, Ádám Gácsi, Márta Andics, Attila Miklósi, Ádám Kubinyi, Enikő Sci Rep Article Resting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained individuals of both sexes and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. In this exploratory study, using resting-state functional magnetic resonance imaging (rs-fMRI), we found several such networks: a network involving prefrontal, anterior cingulate, posterior cingulate and hippocampal regions; sensorimotor (SMN), auditory (AUD), frontal (FRO), cerebellar (CER) and striatal networks. The network containing posterior cingulate regions, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method. Nature Publishing Group UK 2019-10-24 /pmc/articles/PMC6813298/ /pubmed/31649271 http://dx.doi.org/10.1038/s41598-019-51752-2 Text en © The Author(s) 2019 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/.
spellingShingle Article
Szabó, Dóra
Czeibert, Kálmán
Kettinger, Ádám
Gácsi, Márta
Andics, Attila
Miklósi, Ádám
Kubinyi, Enikő
Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks
title Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks
title_full Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks
title_fullStr Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks
title_full_unstemmed Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks
title_short Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks
title_sort resting-state fmri data of awake dogs (canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6813298/
https://www.ncbi.nlm.nih.gov/pubmed/31649271
http://dx.doi.org/10.1038/s41598-019-51752-2
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