Investigation of the static and dynamic brain network mechanisms in patients with rhegmatogenous retinal detachment based on independent component analysis

•We investigated static and dynamic brain networks alterations in RRD patients.•RRD patients demonstrated alterations in both primary sensory networks and higher-order cognitive networks.•These results offer insights into the neural mechanisms of visual loss in RRD. Previous neuroimaging studies hav...

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Vydáno v:	Neuroscience Ročník 570; s. 84 - 94
Hlavní autoři:	Ji, Yu, Wei, Bin, Dan, Yu-jing, Cheng, Qi, Fu, Wen-wen, Shu, Ben-liang, Huang, Qin-yi, Chai, Hua, Zhou, Lin, Yuan, Hao-yu, Wu, Xiao-rong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Inc 27.03.2025
Témata:	Adult Brain - diagnostic imaging Brain - physiopathology Female Functional connectivity Humans Independent component analysis K-means clustering method Magnetic Resonance Imaging - methods Male Middle Aged Nerve Net - diagnostic imaging Nerve Net - physiopathology Neural Pathways - diagnostic imaging Neural Pathways - physiopathology Resting-state functional magnetic resonance imaging Retinal Detachment - diagnostic imaging Retinal Detachment - physiopathology Rhegmatogenous retinal detachment Support Vector Machine Functional connectivity Support vector machine Rhegmatogenous retinal detachment Resting-state functional magnetic resonance imaging K-means clustering method Independent component analysis
ISSN:	0306-4522, 1873-7544, 1873-7544
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Shrnutí:	•We investigated static and dynamic brain networks alterations in RRD patients.•RRD patients demonstrated alterations in both primary sensory networks and higher-order cognitive networks.•These results offer insights into the neural mechanisms of visual loss in RRD. Previous neuroimaging studies have identified substantial structural and functional abnormalities in the brains of patients with rhegmatogenous retinal detachment (RRD). Nonetheless, there remains a paucity of comprehensive research on the alterations in functional connectivity (FC) within large-scale static and dynamic brain networks in these patients. This study utilized independent component analysis (ICA) to investigate alterations in large-scale brain network FC in patients with RRD. Additionally, it employed support vector machine (SVM) to classify RRD patients and healthy controls (HCs) and examined the relationship between abnormal brain regions and clinical ophthalmic parameters. The RRD patients demonstrated significantly increased FC in the default mode network (DMN) and visual network (VN) compared to the HCs, whereas the FC in the auditory network (AN) and the sensorimotor network (SMN) was significantly decreased. Analysis of dynamic functional network connectivity (dFNC) revealed that the fraction of time (FT) spent in state 4 was significantly greater in RRD patients compared to HCs. SVM analysis showed that the AUC for classification using AN and FNC features reached 0.828 and 0.819, respectively. Additionally, the FC value of the right medial superior frontal gyrus (R-SFGmed) in RRD patients was positively correlated with axial length (r = 0.401, p = 0.038). This study revealed that patients with RRD exhibit both damage and adaptive remodeling in their brain functional networks. Alterations in the AN and FNC may serve as potential neuroimaging biomarkers for distinguishing RRD patients from HCs, providing crucial neuroimaging evidence for understanding the mechanisms underlying visual loss in RRD.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0306-4522 1873-7544 1873-7544
DOI:	10.1016/j.neuroscience.2025.02.032