Liang Xiao, Zeng Qingwen, Zhu Yanyan, Wang Yao, He Ting, Wu Lin, Huang Muhua, Zhou Fuqing
Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006, Jiangxi, China.
Sci Rep. 2025 Jan 14;15(1):1909. doi: 10.1038/s41598-024-84508-8.
The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlighting the need for improved diagnostic efficacy. In this study, multilevel functional metrics including resting state functional connectivity, amplitude of low frequency fluctuation (ALFF), and regional homogeneity (ReHo) were calculated and extracted from 116 regions of interest in the anatomical automatic labeling atlas. Subsequently, classifiers were developed using different combinations of these selected features to distinguish between MS and NMOSD. Compared to models based on individual MRI features, support vector machine (SVM) and logistic regression (LR) models that integrated multilevel functional features such as RSFC, ALFF, and ReHo demonstrated the highest levels of performance on the testing cohorts (SVM, AUC = 0.857; LR, AUC = 0.929). Adding structural features of gray matter volume (GMV) data did not notably improve the classification performance of the machine learning models using multilevel rs-fMRI features. Notably, similar trends were observed across different brain templates, with models based on RSFC, ALFF, and ReHo yielding optimal performance. These findings suggest that utilizing multilevel fMRI features can effectively differentiate between MS and NMOSD patients.
用于分析静息态功能磁共振成像（rs-fMRI）数据的传统统计方法难以准确区分多发性硬化症（MS）患者和视神经脊髓炎谱系障碍（NMOSD）患者，这凸显了提高诊断效能的必要性。在本研究中，从解剖自动标记图谱的116个感兴趣区域计算并提取了包括静息态功能连接性、低频波动幅度（ALFF）和局部一致性（ReHo）在内的多水平功能指标。随后，使用这些选定特征的不同组合开发分类器，以区分MS和NMOSD。与基于个体MRI特征的模型相比，整合了诸如静息态功能连接性（RSFC）、ALFF和ReHo等多水平功能特征的支持向量机（SVM）和逻辑回归（LR）模型在测试队列中表现出最高水平的性能（SVM，AUC = 0.857；LR，AUC = 0.929）。添加灰质体积（GMV）数据的结构特征并没有显著提高使用多水平rs-fMRI特征的机器学习模型的分类性能。值得注意的是，在不同的脑模板中观察到了类似的趋势，基于RSFC、ALFF和ReHo的模型表现出最佳性能。这些发现表明，利用多水平功能磁共振成像特征可以有效区分MS和NMOSD患者。
