Literature DB >> 33295117

Altered brain network centrality in middle-aged patients with retinitis pigmentosa: A resting-state functional magnetic resonance imaging study.

Qi Lin1, Fei-Ying Zhu1, Yong-Qiang Shu2, Pei-Wen Zhu1, Lei Ye1, Wen-Qing Shi1, You-Lan Min1, Biao Li1, Qing Yuan1, Yi Shao1.   

Abstract

OBJECTIVE: The purpose of this study is to explore the underlying functional network brain activity changes of patients in middle-aged with retinitis pigmentosa (RP) and the relationships with clinical features such as depression scale and visual functioning using voxel-wise degree centrality (DC) method.
METHODS: We included 16 patients with RP (11 men, 5 women) and 16 healthy controls (HCs; 11 men, 5 women). Participants were matched in terms of age, weight, gender and handedness (age and weight between the two groups were compared using independent sample t-tests, gender and handedness were compared using chi-square test). We use the voxel-wise DC method to assess spontaneous brain activity. Receiver operating characteristic (ROC) curve analysis was performed to distinguish between RP patients and HCs. Correlation analysis was used to examine the relationships between mean DC values in various brain regions and clinical features (such as depression scale and visual functioning) in RP patients.
RESULTS: Compared with HCs, the DC values of patients with RP were reduced in the right medial frontal gyrus, bilateral cuneus, bilateral precuneus, and bilateral superior frontal gyrus, and increased in the right cerebellum posterior lobe, left inferior temporal gyrus, and right fusiform gyrus. The mean DC values in the bilateral cuneus negatively correlated with the depression scale, and those in the bilateral precuneus positively correlated with the Visual Functioning Questionnaire-25.
CONCLUSIONS: Middle-aged patients with RP exhibit abnormal brain network activity in various brain regions, and this may underlie the pathological mechanism of RP.
© 2020 The Authors. Brain and Behavior published by Wiley Periodicals LLC.

Entities:  

Keywords:  functional magnetic resonance imaging; network centrality; resting state; retinitis pigmentosa

Mesh:

Year:  2020        PMID: 33295117      PMCID: PMC7882161          DOI: 10.1002/brb3.1983

Source DB:  PubMed          Journal:  Brain Behav            Impact factor:   3.405


  47 in total

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1.  Structure can predict function in the human brain: a graph neural network deep learning model of functional connectivity and centrality based on structural connectivity.

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2.  Altered brain network centrality in middle-aged patients with retinitis pigmentosa: A resting-state functional magnetic resonance imaging study.

Authors:  Qi Lin; Fei-Ying Zhu; Yong-Qiang Shu; Pei-Wen Zhu; Lei Ye; Wen-Qing Shi; You-Lan Min; Biao Li; Qing Yuan; Yi Shao
Journal:  Brain Behav       Date:  2020-12-09       Impact factor: 3.405

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