The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants ( = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

中文翻译：

---

额顶叶、躯体运动网络和丘脑之间的连通性增强是慢性腰痛最显着的网络变化

慢性腰痛（cLBP）持续时间延长不可避免地会导致认知、注意力、感觉和情绪处理大脑区域的变化。目前，尚不清楚这些改变如何在大脑功能和结构网络之间的相互作用中体现。本研究旨在利用多模态脑磁共振成像 (MRI) 数据预测 cLBP 患者的 Oswestry 残疾指数 (ODI)，并确定多模态网络中最显着的特征，以帮助区分患者与健康对照 (HC)。我们为所有参与者 (= 112) 构建了动态功能连接 (dFC) 和结构连接 (SC) 网络，并采用基于连接组的预测建模 (CPM) 方法来预测 ODI 分数，利用各种特征选择阈值来识别最重要的网络dFC 和 SC 结果的变化特征。随后，我们利用这些重要特征来进行最佳分类器选择和多模态特征的集成。结果显示，与 HC 相比，cLBP 患者的额顶网络 (FPN)、躯体运动网络 (SMN) 和丘脑之间的连接性增强。丘脑通过背外侧前额叶皮层（dlPFC）和初级体感皮层（SI）将与疼痛相关的感觉和情绪传递到皮质区域，导致全脑网络功能和结构的改变。关于分类器的模型选择，我们发现支持向量机（SVM）最适合这些重要的网络特征。基于 dFC 和 SC 特征的组合模型显着提高了 cLBP 患者和 HC 之间的分类性能 (AUC=0.9772)。最后，外部验证集的结果支持我们的假设，并为模型在现实场景中的潜在适用性提供了见解。我们发现丘脑与 dlPFC (FPN) 和 SI (SMN) 之间的连通性增强，为先前的 cLBP 研究提供了宝贵的补充。