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Topological Regularization of Networks in Adult Patients with Moderate-to-Severe Obstructive Sleep Apnea-Hypopnea Syndrome: A Structural MRI Study

Authors Liu W, Cao C, Hu B, Li D, Sun Y, Wu J, Zhang Q

Received 6 February 2020

Accepted for publication 11 May 2020

Published 8 June 2020 Volume 2020:12 Pages 333—345

DOI https://doi.org/10.2147/NSS.S248643

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Sutapa Mukherjee


Wanqing Liu,1 Chuanlong Cao,2 Bing Hu,1 Danyang Li,1 Yumei Sun,1 Jianlin Wu,1 Qing Zhang1

1Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian 116001, People’s Republic of China; 2Department of Radiology, Affiliated Xinhua Hospital of Dalian University, Dalian 116001, People’s Republic of China

Correspondence: Qing Zhang
Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, No. 6, Jiefang Street, Zhongshan District, Dalian, Liaoning, People’s Republic of China
Email zhangqingsmile@163.com

Objective: Patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) exhibit neurocognitive impairments; however, the neuroimaging mechanism of neurocognitive impairments remains unclear. The aim of this study was to understand the neuroimaging mechanism in adult patients with moderate-to-severe OSAHS, from the perspective of the connectome.
Patients and Methods: Thirty-one untreated patients with moderate-to-severe OSAHS (mean age: 41.23± 8.22) were compared with 26 good sleepers (GS) (mean age: 39.50± 7.92) matched according to age, gender, handedness, and education level. All subjects underwent thin-slice T1WI scanning of the skull using a 3.0T MRI. Then, a large-scale structural covariance network was constructed based on the gray matter volume extracted from the structural MRI. Graph theory was then used to determine the topological changes in the structural covariance network of OSAHS patients.
Results: Although small-world networks were retained,the structural covariance network exhibited topological irregularities in regular architecture as evidenced by an increase in the clustering coefficient (p=0.009), transfer coefficient (p=0.029) and local efficiency (p=0.031), and a local increase in the shortest path length (p< 0.05) compared with the GS group. Locally, OSAHS patients showed a decrease in nodal betweenness and degree in the left inferior parietal gyrus, left angular gyrus and right anterior cingulate cortex compared with the GS group (p< 0.05, uncorrected). In addition, the resistance of structural covariance networks in OSAHS patients to random fault is significantly lower than that of the GS group (p=0.044).
Conclusion: Structural covariance networks are abnormal in terms of multiple network parameters, which provide network-level insight into the neuroimaging mechanism of cognitive impairments in adult OSAHS patients.

Keywords: obstructive sleep apnea-hypopnea syndrome, cognitive impairment, connectome, graph theory, gray matter volume


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