Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66–0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy.

慢性下腰痛（LBP）是世界范围内非常普遍的健康问题，也是造成残疾的主要原因。但是，缺乏可作为临床决策依据的量化指标会导致治疗方法不精确，不必要的手术并降低患者预后。尽管LBP的重点主要集中在脊柱上，但文献显示在LBP的情况下人脑的强大重组。脑神经影像学有望发现生物标记物，从而改善慢性LBP的治疗。在这项研究中，我们报告了大脑皮层厚度（CT）和静止状态功能连接（rsFC）措施的形态变化，作为LBP的潜在脑生物标志物。结构MRI扫描 从24名LBP患者和27名年龄匹配的健康对照（HC）中收集了静息状态功能性MRI扫描和自我报告的临床评分。结果表明，相对于HC，LBP患者CT的广泛差异。CT的这些差异与自我报告的临床摘要评分，身体成分摘要和精神成分摘要评分相关。主要视觉，次要视觉和默认模式网络在LBP患者中显示出年龄校正的与多个网络的连通性显着增加。根据其特征向量中心度（EC）归类为集线器的皮质区域在运动和视觉处理区域内显示出拓扑结构上的差异。最终，使用CT训练的支持向量机对HC中的LBP受试者进行分类，平均分类精度达到74.51％，AUC = 0。787（95％CI：0.66-0.91）。这项研究的发现表明LBP患者CT和rsFC发生了广泛变化，而使用CT训练的机器学习算法可以预测患者群体。综上，这些发现表明CT和rsFC可能充当LBP指导治疗的潜在生物标志物。