Abstract
Neuroimaging studies have suggested the presence of alterations in the anatomo-functional properties of the brain of patients with chronic pain. However, investigation of the brain circuitry supporting the perception of clinical pain presents significant challenges, particularly when using traditional neuroimaging approaches. While potential neuroimaging markers for clinical pain have included resting brain connectivity, these cross-sectional studies have not examined sensitivity to within-subject exacerbation of pain. We used the dual regression probabilistic Independent Component Analysis approach to investigate resting-state connectivity on arterial spin labeling data. Brain connectivity was compared between patients with chronic low back pain (cLBP) and healthy controls, before and after the performance of maneuvers aimed at exacerbating clinical pain levels in the patients. Our analyses identified multiple resting state networks, including the default mode network (DMN). At baseline, patients demonstrated stronger DMN connectivity to the pregenual anterior cingulate cortex (pgACC), left inferior parietal lobule, and right insula (rINS). Patients’ baseline clinical pain correlated positively with connectivity strength between the DMN and right insula (DMN–rINS). The performance of calibrated physical maneuvers induced changes in pain, which were paralleled by changes in DMN–rINS connectivity. Maneuvers also disrupted the DMN–pgACC connectivity, which at baseline was anticorrelated with pain. Finally, baseline DMN connectivity predicted maneuver-induced changes in both pain and DMN–rINS connectivity. Our results support the use of arterial spin labeling to evaluate clinical pain, and the use of resting DMN connectivity as a potential neuroimaging biomarker for chronic pain perception.
Highlights
Neuroimaging studies have provided considerable evidence indicating that chronic pain is associated with structural, functional and neurochemical alterations distributed across multiple brain networks [50]
Following probabilistic Independent Component Analysis (pICA) on the concatenated Arterial Spin Labeling (ASL) data, we were able to identify the majority of resting state networks (RSNs) reported in previous BOLD fMRI resting state studies, including the default mode, medial and lateral visual, salience, right and left fronto-parietal control and dorsal attention networks (Figure 1)
Default Mode Network (DMN) encoded the intensity of clinical pain, both at baseline and in response to maneuvers aimed at exacerbating clinical pain levels, and predicted post-maneuver lingering pain in chronic low back pain (cLBP) patients
Summary
Neuroimaging studies have provided considerable evidence indicating that chronic pain is associated with structural, functional and neurochemical alterations distributed across multiple brain networks [50]. In spite of such progress, the identification of neural measures underlying the perception of clinical pain itself presents methodological hurdles. Unlike experimental pain (e.g., exogenous heat stimulus applied to the skin), clinical pain (e.g., endogenous pain in a patient suffering from low back pain) is difficult to elicit in a controlled manner This fact makes it challenging to probe its neural correlates using classical ‘two-state subtraction’ (i.e., block- and event-related) neuroimaging designs [3]. Our study builds on the growing evidence supporting altered brain processing within the DMN in chronic pain patients [5,6,7; 14; 33; 49]
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