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Sun J, Zhao R, He Z, Chang M, Wang F, Wei W, Zhang X, Zhu Y, Xi Y, Yang X, Qin W. Abnormal dynamic functional connectivity after sleep deprivation from temporal variability perspective. Hum Brain Mapp 2022; 43:3824-3839. [PMID: 35524680 PMCID: PMC9294309 DOI: 10.1002/hbm.25886] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 12/25/2022] Open
Abstract
Sleep deprivation (SD) is very common in modern society and regarded as a potential causal mechanism of several clinical disorders. Previous neuroimaging studies have explored the neural mechanisms of SD using magnetic resonance imaging (MRI) from static (comparing two MRI sessions [one after SD and one after resting wakefulness]) and dynamic (using repeated MRI during one night of SD) perspectives. Recent SD researches have focused on the dynamic functional brain organization during the resting-state scan. Our present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity after SD in 55 normal young subjects. We found that sleep-deprived subjects showed increased regional-level temporal variability in large-scale brain regions, and decreased regional-level temporal variability in several thalamus subregions. After SD, participants exhibited enhanced intra-network temporal variability in the default mode network (DMN) and increased inter-network temporal variability in numerous subnetwork pairs. Furthermore, we found that the inter-network temporal variability between visual network and DMN was negative related with the slowest 10% respond speed (β = -.42, p = 5.57 × 10-4 ) of the psychomotor vigilance test after SD following the stepwise regression analysis. In conclusion, our findings suggested that sleep-deprived subjects showed abnormal dynamic brain functional configuration, which provides new insights into the neural underpinnings of SD and contributes to our understanding of the pathophysiology of clinical disorders.
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Affiliation(s)
- Jinbo Sun
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| | - Rui Zhao
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Zhaoyang He
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Mengying Chang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Fumin Wang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Wei Wei
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Xiaodan Zhang
- School of Electronics and Information, Xi'an Polytechnic University, Xi'an, China
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China
| | - Yibin Xi
- Department of Radiology, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.,Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China
| | - Xuejuan Yang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
| | - Wei Qin
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Sciences and Technology, Xidian University, Xi'an, China.,Intelligent Non-invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi'an, China
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