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

丘脑 功能磁共振成像 连接体 背外侧前额叶皮质 神经科学 体感系统 心理学 支持向量机 特征选择 认知 人类连接体项目 物理医学与康复 计算机科学 功能连接 前额叶皮质 医学 人工智能
作者
Kun Zhu,Jianchao Chang,Siya Zhang,Yan Li,Junxun Zuo,Haoyu Ni,Bing Xie,Jiyuan Yao,Zhibin Xu,Sicheng Bian,Tingfei Yan,Xianyong Wu,Senlin Chen,Weiming Jin,Ying Wang,Peng Xu,Peiwen Song,Yuanyuan Wu,Cailiang Shen,Jiajia Zhu
出处
期刊:NeuroImage [Elsevier]
卷期号:290: 120558-120558 被引量:18
标识
DOI:10.1016/j.neuroimage.2024.120558
摘要

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.
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