已发表论文

糖尿病视网膜病变患者脑网络的异常模块化分离

 

Authors Li HH, Su YN, Huang X

Received 27 March 2024

Accepted for publication 22 August 2024

Published 30 August 2024 Volume 2024:17 Pages 3239—3248

DOI https://doi.org/10.2147/DMSO.S470950

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

Editor who approved publication: Prof. Dr. Juei-Tang Cheng

Heng-Hui Li,1,* Yan-Ni Su,2,* Xin Huang3 

1Department of Ophthalmology, Gaoxin Branch of the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China; 2The First Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, People’s Republic of China; 3Department of Ophthalmology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, 330006, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Xin Huang, Department of Ophthalmology, Jiangxi Provincial People’s Hospital, No. 152, Ai Guo Road, Dong Hu District, Nanchang, 330006, Jiangxi, People’s Republic of China, Tel +86 15879215294, Email 334966891@qq.com

Background: Diabetic retinopathy (DR) is a prevalent ocular manifestation of diabetic microvascular complications and a primary driver of irreversible blindness. Existing studies have illuminated the presence of aberrant brain activity in individuals affected by DR. However, the alterations in the modular segregation of brain networks among DR patients remain inadequately understood. The study aims to explore the modular segregation of brain networks in patients with DR.
Methods: We examined the blood oxygen levels dependent (BOLD) signals using resting-state functional magnetic resonance imaging (R-fMRI) in a cohort of 46 DRpatients and 43 age-matched healthy controls (HC). Subsequently, Modular analysis utilizing graph theory method was applied to quantify the degree of brain network segregation by computing the participation coefficient (PC). Deviations from typical PC values were further elucidated through intra- and inter-module connectivity analyses.
Results: The DR group demonstrated significantly lower mean PC in the frontoparietal network (FPN), sensorimotor network (SMN), and visual network (VN) compared to the HCgroup. Moreover, increased inter-module connections were observed between the default-mode network (DMN) and SMN, as well as between FPN and VN within the DR group. In terms of nodal analysis, higher PC values were detected in the left thalamus, right frontal lobe, and right precentral gyrus in the DR group compared to the HC group.
Conclusion: Patients with DR show impairments in primary sensory networks and higher cognitive networks within their functional brain networks. These changes may provide essential insights into the neurobiological mechanisms of DR by identifying alterations in the brain networks of DR patients and pinpointing sensitive neurobiological markers that could serve as vital imaging references for future treatments of diabetic retinopathy.

Keywords: diabetic retinopathy, brain functional networks, functional connectivity, fMRI