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Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory

Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of bo...

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Autores principales: V. Farahani, Farzad, Fafrowicz, Magdalena, Karwowski, Waldemar, Bohaterewicz, Bartosz, Sobczak, Anna Maria, Ceglarek, Anna, Zyrkowska, Aleksandra, Ostrogorska, Monika, Sikora-Wachowicz, Barbara, Lewandowska, Koryna, Oginska, Halszka, Beres, Anna, Hubalewska-Mazgaj, Magdalena, Marek, Tadeusz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7830976/
https://www.ncbi.nlm.nih.gov/pubmed/33467070
http://dx.doi.org/10.3390/brainsci11010111
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author V. Farahani, Farzad
Fafrowicz, Magdalena
Karwowski, Waldemar
Bohaterewicz, Bartosz
Sobczak, Anna Maria
Ceglarek, Anna
Zyrkowska, Aleksandra
Ostrogorska, Monika
Sikora-Wachowicz, Barbara
Lewandowska, Koryna
Oginska, Halszka
Beres, Anna
Hubalewska-Mazgaj, Magdalena
Marek, Tadeusz
author_facet V. Farahani, Farzad
Fafrowicz, Magdalena
Karwowski, Waldemar
Bohaterewicz, Bartosz
Sobczak, Anna Maria
Ceglarek, Anna
Zyrkowska, Aleksandra
Ostrogorska, Monika
Sikora-Wachowicz, Barbara
Lewandowska, Koryna
Oginska, Halszka
Beres, Anna
Hubalewska-Mazgaj, Magdalena
Marek, Tadeusz
author_sort V. Farahani, Farzad
collection PubMed
description Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks.
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spelling pubmed-78309762021-01-26 Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory V. Farahani, Farzad Fafrowicz, Magdalena Karwowski, Waldemar Bohaterewicz, Bartosz Sobczak, Anna Maria Ceglarek, Anna Zyrkowska, Aleksandra Ostrogorska, Monika Sikora-Wachowicz, Barbara Lewandowska, Koryna Oginska, Halszka Beres, Anna Hubalewska-Mazgaj, Magdalena Marek, Tadeusz Brain Sci Article Significant differences exist in human brain functions affected by time of day and by people’s diurnal preferences (chronotypes) that are rarely considered in brain studies. In the current study, using network neuroscience and resting-state functional MRI (rs-fMRI) data, we examined the effect of both time of day and the individual’s chronotype on whole-brain network organization. In this regard, 62 participants (39 women; mean age: 23.97 ± 3.26 years; half morning- versus half evening-type) were scanned about 1 and 10 h after wake-up time for morning and evening sessions, respectively. We found evidence for a time-of-day effect on connectivity profiles but not for the effect of chronotype. Compared with the morning session, we found relatively higher small-worldness (an index that represents more efficient network organization) in the evening session, which suggests the dominance of sleep inertia over the circadian and homeostatic processes in the first hours after waking. Furthermore, local graph measures were changed, predominantly across the left hemisphere, in areas such as the precentral gyrus, putamen, inferior frontal gyrus (orbital part), inferior temporal gyrus, as well as the bilateral cerebellum. These findings show the variability of the functional neural network architecture during the day and improve our understanding of the role of time of day in resting-state functional networks. MDPI 2021-01-16 /pmc/articles/PMC7830976/ /pubmed/33467070 http://dx.doi.org/10.3390/brainsci11010111 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
V. Farahani, Farzad
Fafrowicz, Magdalena
Karwowski, Waldemar
Bohaterewicz, Bartosz
Sobczak, Anna Maria
Ceglarek, Anna
Zyrkowska, Aleksandra
Ostrogorska, Monika
Sikora-Wachowicz, Barbara
Lewandowska, Koryna
Oginska, Halszka
Beres, Anna
Hubalewska-Mazgaj, Magdalena
Marek, Tadeusz
Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory
title Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory
title_full Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory
title_fullStr Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory
title_full_unstemmed Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory
title_short Identifying Diurnal Variability of Brain Connectivity Patterns Using Graph Theory
title_sort identifying diurnal variability of brain connectivity patterns using graph theory
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7830976/
https://www.ncbi.nlm.nih.gov/pubmed/33467070
http://dx.doi.org/10.3390/brainsci11010111
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