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Intrinsic neural network dynamics in catatonia
Catatonia is a transnosologic psychomotor syndrome with high prevalence in schizophrenia spectrum disorders (SSD). There is mounting neuroimaging evidence that catatonia is associated with aberrant frontoparietal, thalamic and cerebellar regions. Large‐scale brain network dynamics in catatonia have...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596986/ https://www.ncbi.nlm.nih.gov/pubmed/34585808 http://dx.doi.org/10.1002/hbm.25671 |
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author | Sambataro, Fabio Hirjak, Dusan Fritze, Stefan Kubera, Katharina M. Northoff, Georg Calhoun, Vince D. Meyer‐Lindenberg, Andreas Wolf, Robert C. |
author_facet | Sambataro, Fabio Hirjak, Dusan Fritze, Stefan Kubera, Katharina M. Northoff, Georg Calhoun, Vince D. Meyer‐Lindenberg, Andreas Wolf, Robert C. |
author_sort | Sambataro, Fabio |
collection | PubMed |
description | Catatonia is a transnosologic psychomotor syndrome with high prevalence in schizophrenia spectrum disorders (SSD). There is mounting neuroimaging evidence that catatonia is associated with aberrant frontoparietal, thalamic and cerebellar regions. Large‐scale brain network dynamics in catatonia have not been investigated so far. In this study, resting‐state fMRI data from 58 right‐handed SSD patients were considered. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate and test the underlying intrinsic components (ICs) in SSD patients with (NCRS total score ≥ 3; n = 30) and without (NCRS total score = 0; n = 28) catatonia. Functional network connectivity (FNC) during rest was calculated between pairs of ICs and transient changes in connectivity were estimated using sliding windowing and clustering (to capture both static and dynamic FNC). Catatonic patients showed increased static FNC in cerebellar networks along with decreased low frequency oscillations in basal ganglia (BG) networks. Catatonic patients had reduced state changes and dwelled more in a state characterized by high within‐network correlation of the sensorimotor, visual, and default‐mode network with respect to noncatatonic patients. Finally, in catatonic patients according to DSM‐IV‐TR (n = 44), there was a significant correlation between increased within FNC in cortico‐striatal state and NCRS motor scores. The data support a neuromechanistic model of catatonia that emphasizes a key role of disrupted sensorimotor network control during distinct functional states. |
format | Online Article Text |
id | pubmed-8596986 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85969862021-12-02 Intrinsic neural network dynamics in catatonia Sambataro, Fabio Hirjak, Dusan Fritze, Stefan Kubera, Katharina M. Northoff, Georg Calhoun, Vince D. Meyer‐Lindenberg, Andreas Wolf, Robert C. Hum Brain Mapp Research Articles Catatonia is a transnosologic psychomotor syndrome with high prevalence in schizophrenia spectrum disorders (SSD). There is mounting neuroimaging evidence that catatonia is associated with aberrant frontoparietal, thalamic and cerebellar regions. Large‐scale brain network dynamics in catatonia have not been investigated so far. In this study, resting‐state fMRI data from 58 right‐handed SSD patients were considered. Catatonic symptoms were examined on the Northoff Catatonia Rating Scale (NCRS). Group spatial independent component analysis was carried out with a multiple analysis of covariance (MANCOVA) approach to estimate and test the underlying intrinsic components (ICs) in SSD patients with (NCRS total score ≥ 3; n = 30) and without (NCRS total score = 0; n = 28) catatonia. Functional network connectivity (FNC) during rest was calculated between pairs of ICs and transient changes in connectivity were estimated using sliding windowing and clustering (to capture both static and dynamic FNC). Catatonic patients showed increased static FNC in cerebellar networks along with decreased low frequency oscillations in basal ganglia (BG) networks. Catatonic patients had reduced state changes and dwelled more in a state characterized by high within‐network correlation of the sensorimotor, visual, and default‐mode network with respect to noncatatonic patients. Finally, in catatonic patients according to DSM‐IV‐TR (n = 44), there was a significant correlation between increased within FNC in cortico‐striatal state and NCRS motor scores. The data support a neuromechanistic model of catatonia that emphasizes a key role of disrupted sensorimotor network control during distinct functional states. John Wiley & Sons, Inc. 2021-09-29 /pmc/articles/PMC8596986/ /pubmed/34585808 http://dx.doi.org/10.1002/hbm.25671 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles Sambataro, Fabio Hirjak, Dusan Fritze, Stefan Kubera, Katharina M. Northoff, Georg Calhoun, Vince D. Meyer‐Lindenberg, Andreas Wolf, Robert C. Intrinsic neural network dynamics in catatonia |
title | Intrinsic neural network dynamics in catatonia |
title_full | Intrinsic neural network dynamics in catatonia |
title_fullStr | Intrinsic neural network dynamics in catatonia |
title_full_unstemmed | Intrinsic neural network dynamics in catatonia |
title_short | Intrinsic neural network dynamics in catatonia |
title_sort | intrinsic neural network dynamics in catatonia |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596986/ https://www.ncbi.nlm.nih.gov/pubmed/34585808 http://dx.doi.org/10.1002/hbm.25671 |
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