Abnormal Low-Frequency Oscillations Reflect Trait-Like Pain Ratings in Chronic Pain Patients Revealed through a Machine Learning Approach.

Measures of moment-to-moment fluctuations in brain activity of an individual at rest have been shown to be a sensitive and reliable metric for studying pathological brain mechanisms across various chronic pain patient populations. However, the relationship between pathological brain activity and clinical symptoms are not well defined. Therefore, we used regional BOLD signal variability/amplitude of low-frequency oscillations (LFOs) to identify functional brain abnormalities in the dynamic pain connectome in chronic pain patients that are related to chronic pain characteristics (i.e., pain intensity). Moreover, we examined whether there were sex-specific attributes of these functional brain abnormalities and whether functional brain abnormalities in patients is related to pain intensity characteristics on different time scales. We acquired resting-state functional MRI and quantified frequency-specific regional LFOs in chronic pain patients with ankylosing spondylitis. We found that patients exhibit frequency-specific aberrations in LFOs. Specifically, lower-frequency (slow-5) abnormalities were restricted to the ascending pain pathway (thalamus and S1), whereas higher-frequency abnormalities also included the default mode (i.e., posterior cingulate cortex; slow-3, slow-4) and salience (i.e., mid-cingulate cortex) networks (slow-4). Using a machine learning approach, we found that these abnormalities, in particular within higher frequencies (slow-3), can be used to make generalizable inferences about patients' average pain ratings (trait-like pain) but not current (i.e., state-like) pain levels. Furthermore, we identified sex differences in LFOs in patients that were not present in healthy controls. These novel findings reveal mechanistic brain abnormalities underlying the longer-lasting symptoms (trait pain intensity) in chronic pain. Measures of moment-to-moment fluctuations in brain activity of an individual at rest have been shown to be a reliable metric for studying functional brain associated with chronic pain. The current results demonstrate that dysfunction in these intrinsic fluctuations/oscillations in the ascending pain pathway, default mode network, and salience network during resting state display sex differences and can be used to make inferences about trait-like pain intensity ratings in chronic pain patients. These results provide robust and generalizable implications for investigating brain mechanisms associated with longer-lasting/trait-like chronic pain symptoms.