학술논문
Abnormal functional connectivity strength in age-related macular degeneration patients: a fMRI study.
Document Type
Academic Journal
Author
Source
Publisher: Lippincott Williams & Wilkins Country of Publication: England NLM ID: 9100935 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1473-558X (Electronic) Linking ISSN: 09594965 NLM ISO Abbreviation: Neuroreport Subsets: MEDLINE
Subject
Language
English
Abstract
Objective: Age-related macular degeneration (AMD) is a serious blinding eye disease. Previous neuroimaging studies reported that AMD were accompanied by abnormalities of the brain. However, whether AMD patients were associated with functional connectivity strength (FCS) or not remains unknown. In our study, the purpose of the study was to assess FCS changes in AMD patients.
Methods: In our study, 20 AMD patients and 20 healthy controls (HCs), matched closely by sex, age, and educational level were underwent MRI scanning. FCS method and seed-based functional connectivity (FC) method were applied to investigate the functional network changes between two groups. Moreover, support vector machine (SVM) method was applied to assess the FCS maps as a feature to classification of AMD diseases.
Results: Our study reported that AMD patients showed decreased FCS values in the bilateral calcarine, left supplementary motor area, left superior parietal lobule and left paracentral lobule (ParaL) relative to the HC group. Meanwhile, our study found that the AMD patients showed abnormal FC within visual network, sensorimotor network and default mode network. Moreover, the SVM method showed that FCS maps as machine learning features shows good classification efficiency (area under curve = 0.82) in the study.
Conclusion: Our study demonstrated that AMD patients showed abnormal FCS with the visual network, sensorimotor network and default mode network, which might reflect the impaired vision, cognition and motor function in AMD patients. In addition, FCS indicator can be used as an effective biological marker to assist the clinical diagnosis of AMD.
(Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)
Methods: In our study, 20 AMD patients and 20 healthy controls (HCs), matched closely by sex, age, and educational level were underwent MRI scanning. FCS method and seed-based functional connectivity (FC) method were applied to investigate the functional network changes between two groups. Moreover, support vector machine (SVM) method was applied to assess the FCS maps as a feature to classification of AMD diseases.
Results: Our study reported that AMD patients showed decreased FCS values in the bilateral calcarine, left supplementary motor area, left superior parietal lobule and left paracentral lobule (ParaL) relative to the HC group. Meanwhile, our study found that the AMD patients showed abnormal FC within visual network, sensorimotor network and default mode network. Moreover, the SVM method showed that FCS maps as machine learning features shows good classification efficiency (area under curve = 0.82) in the study.
Conclusion: Our study demonstrated that AMD patients showed abnormal FCS with the visual network, sensorimotor network and default mode network, which might reflect the impaired vision, cognition and motor function in AMD patients. In addition, FCS indicator can be used as an effective biological marker to assist the clinical diagnosis of AMD.
(Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)