静息状态功能磁共振成像
功能连接
动态功能连接
额上回
2019年冠状病毒病(COVID-19)
中央前回
医学
默认模式网络
支持向量机
神经科学
功能磁共振成像
听力学
心理学
内科学
计算机科学
人工智能
磁共振成像
放射科
传染病(医学专业)
疾病
作者
Mingxing Han,Chunni He,Tianping Li,Qinglong Li,Tongpeng Chu,Jun Li,Peiyuan Wang
出处
期刊:Neuroreport
[Lippincott Williams & Wilkins]
日期:2024-02-02
卷期号:35 (5): 306-315
被引量:7
标识
DOI:10.1097/wnr.0000000000002009
摘要
This study aimed to investigate the effects of COVID-19 on brain functional activity through resting-state functional MRI (rs-fMRI). fMRI scans were conducted on a cohort of 42 confirmed COVID-19-positive patients and 46 healthy controls (HCs) to assess brain functional activity. A combination of dynamic and static amplitude of low-frequency fluctuations (dALFF/sALFF) and dynamic and static functional connectivity (dFC/sFC) was used for evaluation. Abnormal brain regions identified were then used as feature inputs in the model to evaluate support vector machine (SVM) capability in recognizing COVID-19 patients. Moreover, the random forest (RF) model was employed to verify the stability of SVM diagnoses for COVID-19 patients. Compared to HCs, COVID-19 patients exhibited a decrease in sALFF in the right lingual gyrus and the left medial occipital gyrus and an increase in dALFF in the right straight gyrus. Moreover, there was a decline in sFC between both lingual gyri and the right superior occipital gyrus and a reduction in dFC with the precentral gyrus. The dynamic and static combined ALFF and FC could distinguish between COVID-19 patients and the HCs with an accuracy of 0.885, a specificity of 0.818, a sensitivity of 0.933 and an area under the curve of 0.909. The combination of dynamic and static ALFF and FC can provide information for detecting brain functional abnormalities in COVID-19 patients.
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