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

Authors :
Kun Zhu
Jianchao Chang
Siya Zhang
Yan Li
Junxun Zuo
Haoyu Ni
Bingyong 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
Yongqiang Yu
Fulong Dong
Source :
NeuroImage, Vol 290, Iss , Pp 120558- (2024)
Publication Year :
2024
Publisher :
Elsevier, 2024.

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.

Details

Language :
English
ISSN :
10959572
Volume :
290
Issue :
120558-
Database :
Directory of Open Access Journals
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
edsdoj.2bea132afec430fb5dc65c8b4341e16
Document Type :
article
Full Text :
https://doi.org/10.1016/j.neuroimage.2024.120558