刍议2型糖尿病患者默认网络和视觉网络的独立成分分析

Independent Component Analysis of Default Network and Visual Network in Patients with Type 2 Diabetes

目的目的本研究旨在运用独立成分分析(ICA)技术,探讨2型糖尿病(T2DM)患者默认网络(DMN)和视觉网络(VIS)功能连接的变化特征。方法方法选取2023年2月至2023年8月在甘肃医学院附属医院就诊的60例T2DM患者,其中伴视网膜病变30例、不伴视网膜病变30例,组成T2DM组;同时选取30例健康个体作为对照组。所有参与者均接受静息态功能磁共振扫描,随后进行数据预处理及ICA分析,以提取DMN和VIS网络。通过组间比较,分析脑网络功能连接的差异,并进一步探讨差异脑区功能连接(FC)值与临床参数及认知评分的相关性。结果结果T2DM组患者的DMN和VIS网络功能连接强度相较于健康对照组均显著降低。此外,T2DM组内DMN和VIS网络的功能连接强度与临床参数(如血糖水平、病程等)及认知评分呈现负相关趋势。结论结论本研究表明,2型糖尿病患者存在DMN和VIS网络功能连接的异常变化,这些变化可能与疾病进展和认知功能下降密切相关。独立成分分析作为一种有效手段,有助于揭示糖尿病患者脑网络功能连接的异常模式,为深入理解糖尿病的脑部病理生理机制提供了新的视角。

Objective The purpose of this study was to explore the characteristics of functional connectivity between default network(DMN)and visual network(VIS)in patients with type 2 diabetes mellitus(T2DM)using independent component analysis(ICA).Method Sixty T2DM patients who received treatment at the Affiliated Hospital of Gansu Medical College from February 2023 to August 2023 were selected,including 30 patients with retinal lesions and 30 patients without retinal lesions,to form the T2DM group;Simultaneously select 30 healthy individuals as the control group.All participants underwent resting state functional magnetic resonance imaging scans,followed by data preprocessing and ICA analysis to extract DMN and VIS networks.By comparing between groups,analyze the differences in brain net-work functional connectivity,and further explore the correlation between functional connectivity(FC)values of different brain regions and clinical parameters and cognitive scores.Result The functional connectivity strength of DMN and VIS networks in patients with T2DM was significantly reduced compared to the healthy control group.In addition,the functional connectivity strength of DMN and VIS networks within the T2DM group showed a negative correlation with clinical parameters(such as blood glucose levels,disease duration,etc.)and cog-nitive scores.Conclusion This study shows that there are abnormal changes in DMN and VIS network functional connectivity in patients with type 2 diabetes,which may be closely related to disease progression and cognitive decline.As an effective means,independent compo-nent analysis can help reveal the abnormal patterns of brain network functional connectivity in diabetes patients,and provide a new perspec-tive for in-depth understanding of the brain pathophysiological mechanism of diabetes.