Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain

Abstract

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.

Keywords: Acupuncture; Chronic low back pain; Machine learning analysis; Resting-state functional connectivity; Treatment responses.

Fig. 1

Experimental procedures. 50 cLBP patients were included in the analysis. Patients received 4 weeks of longitudinal real or sham acupuncture treatment. Clinical assessments and MRI scans were collected at baseline and after all treatment sessions.

Fig. 2

Predicting the treatment effect of real acupuncture using baseline functional connectivity. Panel A shows the FCs with significantly predictive information (obtained from bootstrap testing), and the size of a node denotes its importance (number of connections) for prediction. Panel B shows an example of the performance of predicting symptom changes following real acupuncture. Different colors of dots come from different folds. The red solid line represents the relationship between the predicted and actual pain severity change, and the blue dashed lines indicate the 95% confidence interval. The prediction errors are indicated by the distance between dots and the diagonal line. Panel C shows the correlation between the strength of five identified mPFC FCs and changes in pain severity. mPFC: medial prefrontal cortex; AG_R: right angular gyrus; AG_L: left angular gyrus. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Predicting the treatment effect of sham acupuncture using baseline functional connectivity. Panel A shows the FCs with significantly predictive information (obtained from bootstrap testing), and the size of a node denotes its importance (number of connections) for prediction. Panel B shows an example of the performance of predicting symptom changes following sham acupuncture. Panel C shows the correlation between the strength of mPFC-dACC FC and changes in pain severity. mPFC: medial prefrontal cortex; dACC: dorsal anterior cingulate cortex.

Fig. 4

Pre- and post-treatment clinical subscores of cLBP patients. Real and sham acupuncture significantly improved symptoms in physical function, pain intensity, pain interference, and social scores.