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已发表论文
高度近视患者的杏仁核连接性改变：基于支持向量机分类的静息态功能磁共振成像研究
Authors Dong ZE , Ji Y, Zhou L, Yuan HY, Chai H, Duan YC, Yao LL, Wu XR
Received 31 May 2025
Accepted for publication 18 September 2025
Published 17 October 2025 Volume 2025:19 Pages 3837—3849
DOI https://doi.org/10.2147/OPTH.S543962
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Scott Fraser
Zhuo-Er Dong,* Yu Ji,* Lin Zhou, Hao-Yu Yuan, Hua Chai, Yi-Chong Duan, Li-Li Yao, Xiao-Rong Wu
Ophthalmology Department of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiao-Rong Wu, Ophthalmology Department of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, People’s Republic of China, Tel +86 13617093259, Email wxr98021@126.com
Background: High myopia (HM) is strongly linked to emotional disorders like anxiety and depression. While prior neuroimaging findings in HM are varied, the role of the amygdala—the brain’s core emotion center—remains critically under-explored. Given evidence of limbic system changes in other ophthalmic disorders (eg, glaucoma), we investigated amygdala-specific functional connectivity (FC) in HM.
Methods: We acquired resting-state fMRI data from 82 HM patients and 59 healthy controls (HCs). Using a seed-based approach with the bilateral amygdalae, whole-brain FC was compared between groups. A support vector machine (SVM) then evaluated the classification power of the identified FC alterations.
Results: Compared to HCs, HM patients showed significantly increased FC between the amygdala and key regions in the visual, default mode, and executive control networks, including the calcarine fissure, precuneus, and middle frontal gyrus. SVM models achieved robust classification performance, with an area under the curve (AUC) up to 0.765.
Conclusion: This study provides the first report on amygdala-centric network reorganization in HM. These FC patterns show potential as neuroimaging biomarkers. Our findings offer preliminary evidence for a neural substrate underlying the emotional and cognitive dysregulation in HM, highlighting the need to address mental health in these patients.
Keywords: high myopia, amygdala, functional connectivity, resting-state functional magnetic resonance imaging, support vector machine, machine learning
Ophthalmology Department of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiao-Rong Wu, Ophthalmology Department of the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, People’s Republic of China, Tel +86 13617093259, Email wxr98021@126.com
Background: High myopia (HM) is strongly linked to emotional disorders like anxiety and depression. While prior neuroimaging findings in HM are varied, the role of the amygdala—the brain’s core emotion center—remains critically under-explored. Given evidence of limbic system changes in other ophthalmic disorders (eg, glaucoma), we investigated amygdala-specific functional connectivity (FC) in HM.
Methods: We acquired resting-state fMRI data from 82 HM patients and 59 healthy controls (HCs). Using a seed-based approach with the bilateral amygdalae, whole-brain FC was compared between groups. A support vector machine (SVM) then evaluated the classification power of the identified FC alterations.
Results: Compared to HCs, HM patients showed significantly increased FC between the amygdala and key regions in the visual, default mode, and executive control networks, including the calcarine fissure, precuneus, and middle frontal gyrus. SVM models achieved robust classification performance, with an area under the curve (AUC) up to 0.765.
Conclusion: This study provides the first report on amygdala-centric network reorganization in HM. These FC patterns show potential as neuroimaging biomarkers. Our findings offer preliminary evidence for a neural substrate underlying the emotional and cognitive dysregulation in HM, highlighting the need to address mental health in these patients.
Keywords: high myopia, amygdala, functional connectivity, resting-state functional magnetic resonance imaging, support vector machine, machine learning