网络损伤预测多发性硬化症的临床恶化：一项 6.4 年的研究。
Network Damage Predicts Clinical Worsening in Multiple Sclerosis: A 6.4-Year Study.
机构信息
From the Neuroimaging Research Unit (M.A.R.), Division of Neuroscience; and Neurology Unit, IRCCS San Raffaele Scientific Institute; Vita-Salute San Raffaele University (M.A.R., M.F.); Neuroimaging Research Unit (P.V., A.M., E.P., Claudio Cordani, Chiara Cervellin), Division of Neuroscience, IRCCS San Raffaele Scientific Institute; and Neuroimaging Research Unit (M.F.), Division of Neuroscience, Neurology Unit, Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.
出版信息
Neurol Neuroimmunol Neuroinflamm. 2021 May 21;8(4). doi: 10.1212/NXI.0000000000001006. Print 2021 Jul.
OBJECTIVE
In multiple sclerosis (MS), clinical impairment is likely due to both structural damage and abnormal brain function. We assessed the added value of integrating structural and functional network MRI measures to predict 6.4-year MS clinical disability deterioration.
METHODS
Baseline 3D T1-weighted and resting-state functional MRI scans were obtained from 233 patients with MS and 77 healthy controls. Patients underwent a neurologic evaluation at baseline and at 6.4-year median follow-up (interquartile range = 5.06-7.51 years). At follow-up, patients were classified as clinically stable/worsened according to disability changes. In relapsing-remitting (RR) MS, secondary progressive (SP) MS conversion was evaluated. Global brain volumetry was obtained. Furthermore, independent component analysis identified the main functional connectivity (FC) and gray matter (GM) network patterns.
RESULTS
At follow-up, 105/233 (45%) patients were clinically worsened; 26/157 (16%) patients with RRMS evolved to SPMS. The treatment-adjusted random forest model identified normalized GM and brain volumes, decreased FC between default-mode networks, increased FC of the left precentral gyrus in the sensorimotor network (SMN), and GM atrophy in the fronto-parietal network (false discovery rate [FDR]-corrected = range 0.01-0.09) as predictors of clinical worsening (out-of-bag [OOB] accuracy = 0.74). An expected contribution of baseline disability was also present (FDR-p = 0.01). Baseline disability, normalized GM volume, and GM atrophy in the SMN (FDR-p = range 0.01-0.09) were independently associated with SPMS conversion (OOB accuracy = 0.84). At receiver operating characteristic analysis, including network MRI variables improved disability worsening ( = 0.05) and SPMS conversion ( = 0.02) prediction.
CONCLUSIONS
Integration of MRI network measures helped determining the relative contributions of global/local GM damage and functional reorganization to clinical deterioration in MS.
目的
在多发性硬化症（MS）中，临床损伤可能既与结构损伤又与脑功能异常有关。我们评估了整合结构和功能网络 MRI 测量值以预测 6.4 年 MS 临床残疾恶化的附加价值。
方法
从 233 名 MS 患者和 77 名健康对照中获得基线 3D T1 加权和静息状态功能 MRI 扫描。患者在基线和 6.4 年中位随访（四分位距= 5.06-7.51 年）时进行神经学评估。在随访时，根据残疾变化将患者分为临床稳定/恶化。在复发缓解型（RR）MS 中，评估继发性进展（SP）MS 转化。获得全脑容积。此外，独立成分分析确定了主要的功能连接（FC）和灰质（GM）网络模式。
结果
在随访时，233 名患者中有 105 名（45%）临床恶化；157 名 RRMS 患者中有 26 名进展为 SPMS。经治疗调整的随机森林模型确定了正常化 GM 和脑体积、默认模式网络之间 FC 降低、感觉运动网络中左中央前回 FC 增加以及额顶网络中的 GM 萎缩（错误发现率 [FDR]-校正范围 0.01-0.09）是临床恶化的预测因子（袋外 [OOB]准确性= 0.74）。基线残疾的预期贡献也存在（FDR-p=0.01）。基线残疾、正常化 GM 体积和感觉运动网络中的 GM 萎缩（FDR-p=范围 0.01-0.09）与 SPMS 转化独立相关（OOB 准确性=0.84）。在接受者操作特征分析中，包括网络 MRI 变量可改善残疾恶化（=0.05）和 SPMS 转化（=0.02）的预测。
结论
整合 MRI 网络测量有助于确定 GM 损害的全局/局部和功能重组对 MS 临床恶化的相对贡献。
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