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慢性下腰痛最显著的网络变化是额顶叶、躯体运动网络和丘脑之间增强的连接。

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain.

机构信息

Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China.

Department of Orthopedics, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; Department of Spine Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, PR China; School of Basic Medical Sciences, Anhui Medical University, Hefei, PR China.

出版信息

Neuroimage. 2024 Apr 15;290:120558. doi: 10.1016/j.neuroimage.2024.120558. Epub 2024 Mar 2.

Abstract

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 (n = 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.

摘要

慢性下腰痛 (cLBP) 的持续时间必然会导致认知、注意力、感觉和情感处理脑区的变化。目前,这些变化如何在大脑功能和结构网络的相互作用中表现出来还不清楚。本研究旨在使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著的特征,以帮助区分患者与健康对照者 (HCs)。我们为所有参与者 (n = 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) 方法预测 ODI 评分,利用各种特征选择阈值来识别 dFC 和 SC 结果中最显著的网络变化特征。随后,我们利用这些显著特征进行最佳分类器选择和多模态特征的整合。结果显示,与 HCs 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发生变化。关于分类器的模型选择,我们发现支持向量机 (SVM) 最适合这些显著的网络特征。基于 dFC 和 SC 特征的组合模型显著提高了 cLBP 患者和 HCs 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,并为模型在实际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) 之间的连接增强,为 cLBP 的研究提供了有价值的补充。

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