From the Department of Radiology (F.L.C., B.T., P.T.F.), Research Imaging Institute (F.L.C., C.G.F., S.D., J.P.G., P.T.F.), Joe R. and Teresa Lozano Long School of Medicine (F.L.C., M.F., R.S.R., B.T., P.T.F.), and Department of Neurology (R.S.R., P.T.F.), The University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Dr, MC7800, San Antonio, TX 78229-3900; Division of Methodology, Measurement and Statistical Analysis, Texas State University, San Marcos, Tex (L.P.); Department of Radiology, Division of Neuroradiology, The University of Texas Southwestern Medical Center, Dallas, Tex (F.F.Y.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Mass (S.Y.H.); Department of Radiology, Division of Neuroradiology, Massachusetts General Hospital, Boston, Mass (S.Y.H.); and South Texas Veteran Health Care System, Research Service, San Antonio, Tex (P.T.F.).
Radiology. 2021 Apr;299(1):159-166. doi: 10.1148/radiol.2021203414. Epub 2021 Feb 2.
Background In multiple sclerosis (MS), gray matter (GM) atrophy exhibits a specific pattern, which correlates strongly with clinical disability. However, the mechanism of regional specificity in GM atrophy remains largely unknown. Recently, the network degeneration hypothesis (NDH) was quantitatively defined (using coordinate-based meta-analysis) as the atrophy-based functional network (AFN) model, which posits that localized GM atrophy in MS is mediated by functional networks. Purpose To test the NDH in MS in a data-driven manner using the AFN model to direct analyses in an independent test sample. Materials and Methods Model fit testing was conducted with structural equation modeling, which is based on the computation of semipartial correlations. Model verification was performed in coordinate-based data of healthy control participants from the BrainMap database (). Model validation was conducted in prospectively acquired resting-state functional MRI in participants with relapsing-remitting MS who were recruited between September 2018 and January 2019. Correlation analyses of model fit indices and volumetric measures with Expanded Disability Status Scale (EDSS) scores and disease duration were performed. Results Model verification of healthy control participants included 80 194 coordinates from 9035 experiments. Model verification in healthy control data resulted in excellent model fit (root mean square error of approximation, 0.037; 90% CI: 0.036, 0.039). Twenty participants (mean age, 36 years ± 9 [standard deviation]; 12 women) with relapsing-remitting MS were evaluated. Model validation in resting-state functional MRI in participants with MS resulted in deviation from optimal model fit (root mean square error of approximation, 0.071; 90% CI: 0.070, 0.072), which correlated with EDSS scores ( = 0.68; = .002). Conclusion The atrophy-based functional network model predicts functional network disruption in multiple sclerosis (MS), thereby supporting the network degeneration hypothesis. On resting-state functional MRI scans, reduced functional network integrity in participants with MS had a strong positive correlation with clinical disability. © RSNA, 2021
在多发性硬化症（MS）中，灰质（GM）萎缩表现出特定的模式，与临床残疾高度相关。然而，GM 萎缩的区域特异性机制在很大程度上仍不清楚。最近，网络退化假说（NDH）被定量定义（使用基于坐标的荟萃分析）为基于萎缩的功能网络（AFN）模型，该模型认为 MS 中的局部 GM 萎缩是由功能网络介导的。目的：使用 AFN 模型以数据驱动的方式在独立测试样本中对 MS 中的 NDH 进行测试。材料和方法：结构方程建模用于模型拟合测试，该模型基于偏相关的计算。在 BrainMap 数据库（）的健康对照参与者的基于坐标的数据中进行模型验证。在 2018 年 9 月至 2019 年 1 月期间招募的复发缓解型 MS 参与者的前瞻性静息态功能磁共振成像中进行模型验证。对模型拟合指数和容积测量值与扩展残疾状况量表（EDSS）评分和疾病持续时间的相关性进行了分析。结果：健康对照组参与者的模型验证包括来自 9035 项实验的 80194 个坐标。健康对照数据中的模型验证得出了极好的模型拟合（近似均方根误差，0.037；90%置信区间：0.036，0.039）。对 20 名（平均年龄，36 岁±9[标准差]；12 名女性）复发缓解型 MS 参与者进行了评估。MS 患者静息态功能磁共振成像中的模型验证偏离了最佳模型拟合（近似均方根误差，0.071；90%置信区间：0.070，0.072），与 EDSS 评分相关（ = 0.68； =.002）。结论：基于萎缩的功能网络模型预测多发性硬化症（MS）中的功能网络破坏，从而支持网络退化假说。在静息态功能磁共振成像扫描中，MS 患者的功能网络完整性降低与临床残疾呈强烈正相关。
