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.

While self-report pain intensity ratings are the gold standard in clinical pain assessment, they are highly variable, inherently subjective in nature, and significantly influenced by multidimensional factors. The lack of objective biomarkers for pain has contributed to suboptimal chronic pain management (e.g., opioid public health crisis) [26]. Thus, research focused on the development of quantitative, objective biomarkers/predictors alongside selfreport to aid diagnosis, estimate prognosis, and predict treatment efficacy is of increasing importance to combat chronic pain [24,30,32].Growing consensus has suggested that altered central nervous system processing can support and maintain abnormal pain perception in chronic pain, implicating aberrant activity and connectivity of multiple functional brain networks, including default mode, salience, and 2

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altered salience network connectivity. Additionally, the sensorimotor network, which includes primary somatosensory (S1) cortical representations for different body regions, may receive excessive excitatory input under clinical pain and be important for coding location and severity of this pain. Our prior study demonstrated reduced resting connectivity between different S1 cortical representations for fibromyalgia patients [23]. In fact, several resting-state fMRI studies have suggested that both fibromyalgia and chronic back pain patients exhibit increased cross-network connectivity between salience, sensorimotor, and DMN networks[18;30;37]. However, which aspects of chronic pain pathology, such as pain catastrophizing -a psychosocial construct strongly linked with self-referential DMN processing [29], contribute to such cross-network connectivity is unknown. Furthermore, state properties (i.e. stability) of aberrant cross-network connectivity is important to assess, as alterations in connectivity may be relatively immutable and reflect a fairly stable trait (e.g., linked to living with daily chronic pain) or be a more labile, fluctuating state (e.g., linked to spontaneously flaring clinical pain), and researchers have ascribed both trait and state properties to functional connectivity [7]. With regard to state-like properties of pain, our previous neuroimaging studies in both healthy adults [24] and fibromyalgia patients [23] have shown that experimental nociceptive stimuli increase connectivity between salience network regions (e.g., anterior insula), and contralateral somatotopically-specific S1 cortical representations. Whether clinical pain exerts similar state-like alterations remains to be seen.Here, we tested the hypothesis that both location and intensity of chronic, clinical pain are encoded by increased connectivity between DMN or salience processing brain regions and somatotopically-specific S1 subregions. We contrasted a large cohort of patients suffering from chronic low back pain (cLBP), one of the most common chronic pain disorders [20], with healthy adults. To experimentally manipulate clinical pain states and test the stability of cross-network connectivity, we adopted a modified version of our model for clinical back pain exacerbation [46]. We then explored the association between altered connectivity and exacerbation-induced changes in clinical pain intensity, further probing how, and under what conditions, the clinical pain state modulates resting-state brain connectivity. MethodsWhile most of the data came from a single study (N=174; cLBP=135 (78 Female), HC=39(20F)), in order to further bolster the sample size and power of our analyses, we also included data from cLBP patients and healthy control subjects (N=36; cLBP=17(11F), HC=19(12F)) acquired in a prior 3.0T fMRI study [26], which used similar inclusion and exclusion criteria and the same cLBP phenotype and similar study design. Collectively, resting-state fMRI (rs-fMRI) data from 152 cLBP patients and age-and sex-matched healthy contr...

Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility of distinguishing cLBP patients from healthy controls using machine learning methods. cLBP ( n = 90) and control individuals ( n = 74) were enrolled and underwent resting-state BOLD fMRI scans. Primary, dorsal, and ventral visual networks derived from independent component analysis were used as regions of interest to compare resting state functional connectivity changes between the cLBP patients and healthy controls. We then applied a support vector machine classifier to distinguish the cLBP patients and control individuals. These results were further verified in a new cohort of subjects. We found that the functional connectivity between the primary visual network and the somatosensory/motor areas were significantly enhanced in cLBP patients. The rsFC between the primary visual network and S1 was negatively associated with duration of cLBP. In addition, we found that the rsFC of the visual network could achieve a classification accuracy of 79.3% in distinguishing cLBP patients from HCs, and these results were further validated in an independent cohort of subjects (accuracy = 66.7%). Our results demonstrate significant changes in the rsFC of the visual networks in cLBP patients. We speculate these alterations may represent an adaptation/self-adjustment mechanism and cross-model interaction between the visual, somatosensory, motor, attention, and salient networks in response to cLBP. Elucidating the role of the visual networks in cLBP may shed light on the pathophysiology and development of the disorder.

Accumulating evidence has shown that complicated brain systems are involved in the development and maintenance of chronic low back pain (cLBP), but its underlying mechanisms, particularly the association between brain functional changes and clinical outcomes, remain unclear. Here we used resting-state fMRI and multivariate pattern analysis (MVPA) to identify abnormal functional connectivity (FC) between the default mode, sensorimotor, salience and central executive brain networks in cLBP and tested whether abnormal FCs are related to pain and comorbid symptoms. Fifty cLBP patients and 44 matched healthy controls (HCs) underwent an fMRI scan, from which brain networks were identified by independent component analysis. MVPA, graph theory approaches, and correlation analyses were applied to find abnormal FCs that were associated with clinical symptoms. Findings were validated on a second cohort of 30 cLBP patients and 30 matched HCs. Results showed the medial prefrontal cortex (mPFC) had abnormal FCs with brain regions within the DMN and with other brain networks in cLBP patients. These altered FCs were also correlated with pain duration, pain severity, and pain interference. Lastly, we found restingstate FC could discriminate cLBP patients from HCs with 91% accuracy in the first cohort and 78% accuracy in the validation cohort. Our findings suggest the mPFC may be an important hub for linking the default mode network with the other three networks in cLBP patients. Elucidating the altered FCs and their association with clinical outcomes will enhance our understanding of the pathophysiology of cLBP and may facilitate the development of pain management approaches.

Chronic low back pain (cLBP) is a common disorder with unsatisfactory treatment options. Acupuncture has emerged as a promising method for treating cLBP. However, the mechanism underlying acupuncture remains unclear. In this study, we investigated the modulation effects of acupuncture on resting state functional connectivity (rsFC) of the periaqueductal gray (PAG) and ventral tegmental area (VTA) in patients with cLBP. Seventy-nine cLBP patients were recruited and assigned to four weeks of real or sham acupuncture. Resting state functional magnetic resonance imaging data were collected before the first and after the last treatment. Fifty patients completed the study. We found remission of pain bothersomeness in all treatment groups after four weeks, with greater pain relief after real acupuncture compared to sham acupuncture. We also found that real acupuncture can increase VTA/PAG rsFC with the amygdala, and the increased rsFC was associated with decreased pain bothersomeness scores. Baseline PAG-amygdala rsFC could predict four-week treatment response. Our results suggest that acupuncture may simultaneously modulate the rsFC of key regions in the descending pain modulation (PAG) and reward systems (VTA), and the amygdala may be a key node linking the two systems to produce antinociceptive effects. Our findings highlight the potential of acupuncture for chronic low back pain management.

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