Yang Lili, Vigotsky Andrew D, Wu Binbin, Shen Bangli, Yan Zhihan, Apkarian A Vania, Huang Lejian
Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Departments of Biomedical Engineering and Statistics, Northwestern University, Evanston, IL, United States.
Front Netw Physiol. 2022 Oct 25;2:992662. doi: 10.3389/fnetp.2022.992662. eCollection 2022.
We used a recently advanced technique, morphometric similarity (MS), in a large sample of lumbar disc herniation patients with chronic pain (LDH-CP) to examine morphometric features derived from multimodal MRI data. To do so, we evenly allocated 136 LDH-CPs to exploratory and validation groups with matched healthy controls (HC), randomly chosen from the pool of 157 HCs. We developed three MS-based models to discriminate LDH-CPs from HCs and to predict the pain intensity of LDH-CPs. In addition, we created analogous models using resting state functional connectivity (FC) to perform the above discrimination and prediction of pain, in addition to comparing the performance of FC- and MS-based models and investigating if an ensemble model, combining morphometric features and resting-state signals, could improve performance. We conclude that 1) MS-based models were able to discriminate LDH-CPs from HCs and the MS networks (MSN) model performed best; 2) MSN was able to predict the pain intensity of LDH-CPs; 3) FC networks constructed were able to discriminate LDH-CPs from HCs, but they could not predict pain intensity; and 4) the ensemble model neither improved discrimination nor pain prediction performance. Generally, MSN is sensitive enough to uncover brain morphology alterations associated with chronic pain and provides novel insights regarding the neuropathology of chronic pain.
我们运用了一种最近发展起来的技术——形态测量相似性（MS），对大量患有慢性疼痛的腰椎间盘突出症患者（LDH-CP）进行研究，以检验从多模态MRI数据中得出的形态测量特征。为此，我们将136名LDH-CP患者平均分配到探索性组和验证组，并为其匹配了从157名健康对照者（HC）中随机选取的健康对照组。我们开发了三个基于MS的模型，用于区分LDH-CP患者与健康对照者，并预测LDH-CP患者的疼痛强度。此外，我们还创建了使用静息态功能连接（FC）的类似模型，以进行上述疼痛的区分和预测，同时比较基于FC和基于MS的模型的性能，并研究结合形态测量特征和静息态信号的集成模型是否能提高性能。我们得出以下结论：1）基于MS的模型能够区分LDH-CP患者与健康对照者，且MS网络（MSN）模型表现最佳；2）MSN能够预测LDH-CP患者的疼痛强度；3）构建的FC网络能够区分LDH-CP患者与健康对照者，但无法预测疼痛强度；4）集成模型既未提高区分能力，也未改善疼痛预测性能。总体而言，MSN足够敏感，能够揭示与慢性疼痛相关的脑形态改变，并为慢性疼痛的神经病理学提供新的见解。
