The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain
Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:290 |
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| Enthalten in: |
NeuroImage - 290(2024) vom: 15. Apr., Seite 120558 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhu, Kun [VerfasserIn] |
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| Links: |
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| Themen: |
Chronic low back pain |
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| Anmerkungen: |
Date Completed 01.04.2024 Date Revised 01.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.neuroimage.2024.120558 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Chronic low back pain | |
| 650 | 4 | |a Classification modeling | |
| 650 | 4 | |a Dynamic functional connectivity network | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Predictive modeling | |
| 650 | 4 | |a Structural connectivity network | |
| 700 | 1 | |a Chang, Jianchao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Siya |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Zuo, Junxun |e verfasserin |4 aut | |
| 700 | 1 | |a Ni, Haoyu |e verfasserin |4 aut | |
| 700 | 1 | |a Xie, Bingyong |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Jiyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Zhibin |e verfasserin |4 aut | |
| 700 | 1 | |a Bian, Sicheng |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Tingfei |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Xianyong |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Senlin |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Weiming |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Peng |e verfasserin |4 aut | |
| 700 | 1 | |a Song, Peiwen |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Cailiang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Jiajia |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Yongqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Dong, Fulong |e verfasserin |4 aut | |
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