Key result
Locus coeruleus resting-state functional connectivity patterns differentiated patients with chronic low back pain from healthy controls with up to 0.78 accuracy using machine learning models.
Why the study?
The specific regulatory mechanisms of the locus coeruleus in chronic low back pain are poorly characterized.
Can resting-state functional connectivity of the locus coeruleus combined with machine learning differentiate patients with chronic low back pain from healthy controls?
Case-Control (n=125)
Can resting-state functional connectivity of the locus coeruleus combined with machine learning differentiate patients with chronic low back pain from healthy controls?
Effect estimate: accuracy = 0.78 (SVM) and 0.74 (LR)
p-value: p=< 0.05
Locus coeruleus connectivity patterns combined with machine learning models show potential as objective neuroimaging biomarkers for differentiating patients with chronic low back pain from healthy controls.
Chronic low back pain (cLBP) is often accompanied by emotional disorders. The locus coeruleus (LC) plays an important role in the establishment and perpetuation of chronic pain and is involved in modulating the emotional aspects of pain. However, the specific regulatory mechanisms of LC are poorly characterized in cLBP. In this study, seventy-three patients with cLBP and 52 healthy controls (HCs) were recruited. The resting-state functional connectivity (rsFC) and effective connectivity (EC) of the LC were evaluated. Then support vector machines (SVM) and logistic regression (LR) were used to distinguish the cLBP from the HCs based on the connectivity features. Compared to HCs, patients with cLBP exhibited significantly decreased rsFC of the left LC-superior frontal gyrus (SFG), middle cingulate cortex (MCC), right LC-left cerebellum, right middle frontal gyrus (MFG) pathways, and enhanced regions including left precuneus gyrus and right angular gyrus ( P < 0.05, Family Wise Error corrected). The EC showed increased from the right LC to left cerebellum in cLBP. In addition, SVM and LR achieved high performance in differentiating the patients with cLBP from HC (accuracy = 0.78 and 0.74, respectively) in training cohort and the independent test cohort (accuracy = 0.74 and 0.69, respectively) based on rsFC of the LC. Our results suggest that impaired LC-cerebellar-cortical circuits mediate the modulation of emotions and chronic pain. Critically, Support vector machine and logistic regression models demonstrated the potential of LC connectivity patterns as objective neuroimaging biomarkers for differentiating patients with cLBP from HCs. • Research on the regulatory mechanisms of the locus coeruleus in chronic low back pain remains relatively scarce. We employed functional magnetic resonance imaging combined with machine learning to investigate functional alterations in the locus coeruleus during chronic low back pain, provided neuroimaging evidence for a deeper understanding of the regulatory mechanisms of the locus coeruleus. • We observed abnormal functional connectivity alterations in the locus coeruleus-cerebellar-cortical circuits. These pathways highly overlap with norepinephrine projection areas, suggesting that the locus coeruleus-norepinephrine (LC-Ne) system modulates chronic low back pain. This finding provided a basis for identifying potential therapeutic targets. • The support vector machines and logistic regression achieved high performance of differentiating the patients with chronic low back pain from healthy controls based on rsFC of the locus coeruleus, demonstrated the potential of LC connectivity patterns as objective neuroimaging biomarkers for differentiating patients with cLBP from HCs.
No takes yet. Share an insight, caveat, or question.
Guo et al. (2025) conducted a case-control in Chronic low back pain (n=125). Chronic low back pain vs. Healthy controls was evaluated on Differentiation of cLBP from HCs based on resting-state functional connectivity of the locus coeruleus (accuracy = 0.78 (SVM) and 0.74 (LR), p=< 0.05). Locus coeruleus resting-state functional connectivity patterns differentiated patients with chronic low back pain from healthy controls with up to 0.78 accuracy using machine learning models.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: