Abbreviated Title: Oscillation Changes and Chronic Pain Cortical 6-9 Hz Oscillation are a Reliable Biomarker of Persistent Pain in Rats

Neural biomarkers of chronic pain offer a potential tool for improving the speed of diagnosis and delivery of treatment for this devastating disease. Here, we tested the hypothesis that pain states are associated with distinct changed in cortical brain waves. We induced neuropathic orofacial pain in female rats by chronic constriction injury of the infraorbital nerve (CCI-ION). In most animals, this resulted in lasting reductions in mechanical sensitivity thresholds, and in lasting increases in facial grimace scores. We recorded electrocortigraphy (ECoG) signals over the neocortex of these rats, before and after CCI-ION, and analyzed these signals with a novel, spectral modelling approach. Consistent with our hypothesis, power in the 6-9 Hz bandwidth of the ECoG was differentially modulated in animals displaying signs of chronic pain. Specifically, development of mechanical hypersensitivity correlated with a decrease in 6-9 Hz power. Furthermore, we show that changes in the power of this oscillation after injury, obtained at the individual animal level, provide a more sensitive marker of pain presence than do traditional between animal comparisons of post-injury oscillatory power. Identification of animals demonstrating chronic-pain behaviors was more accurate when estimates of post-injury oscillatory power were compared against each animal’s own pre-injury baseline than when compared against postinjury power estimates from animals not developing chronic pain. These results highlight the need for establishing individual-specific, “pain-free” baselines from which oscillation disturbances can be measured and which may constitute a reliable, low-cost approach not only for diagnosing chronic pain, but also for identifying individuals likely to transition from acute to chronic pain. Despite its pervasiveness, afflicting nearly one in four Americans, and resources devoted towards it, with medical costs totaling more than $600 billion annually (Gaskin & Richard, 2012), chronic pain remains exceedingly difficult to diagnose. Indeed, diagnosis can take years and multiple clinical visits, and even then, most patients fail to achieve satisfactory pain reduction (Schulte et al., 2010). One contributing factor to this crisis is that chronic pain often presents long after tissue damage has resolved, or in the complete absence of observable tissue damage. This can place physicians in an uncertain diagnostic landscape, often relying almost exclusively on patient reports, that can become even more uncertain in the face of potential malignant drug-seeking (Crowley-Matoka & True, 2012). This affects patient healthcare both directly and indirectly. Many physicians cite poor pain assessment as a major barrier to patient treatment (Von Roenn et al., 1993), while awareness of clinician distrust or inaction can negatively impact patient opinions of medical providers and dissuade future treatment seeking (Upshur et al., 2010). Development of new diagnostic tools for chronic pain, including brain-derived biomarkers, will positively impact both patients and their medical providers. Chronic pain is associated with changes in the central nervous system that contribute to the disease’s etiology, including alterations to oscillations that are prominent in electrical recordings of the brain, like electroencephalography (EEG) (Llinás et al., 2005). These oscillations, reflecting synaptic-driven fluctuations in the membrane potentials of multiple cells (Buzsaki & Wang, 2012), are often separated into predefined bands, such as the 8-12 Hz “Alpha” rhythm (Berger, 1929) or the 4-8 Hz Theta rhythm (Kahana et al., 2001). In studies of chronic pain, attention has been focused predominantly on Alpha and Theta rhythms. Relative to age matched controls, humans with chronic pain display elevated power in both frequency bands (e.g. Sarnthein et al., 2005), and transition of the peak frequency from the Alpha to Theta range has been reported in several chronic pain conditions (de Vries et al., 2013; Kim et al., 2019). Although intriguing, the correlational nature of these findings makes it difficult to determine whether changes reflect chronic pain per se, whether they reflect processes that predate pain onset, or whether they have anything to do with pain at all. For example, we have recently shown that peak alpha frequency does not slow in response to a model of musculoskeletal pain that persists for multiple weeks (Furman et al., 2019). Instead, sensitivity to this model was better captured by the speed of peak alpha frequency recorded prior to model induction, suggesting that disturbances of peak frequency in chronic pain are unlikely to reflect processes directly attributable to ongoing pain. To overcome the correlational nature of earlier human studies, preclinical rodent studies have investigated how oscillations change in a variety of injury models. Previous studies, focusing on the thalamus and somatosensory cortex, have reported increases in Theta power, without peak slowing, following injury (e.g. LeBlanc et al., 2014). These studies have focused on between-group comparisons amongst injured and sham animals. Given that confounding physiological factors, such as CSF thickness (Rice et al., 2013), can produce substantial between-subject variability in spectral power, establishing reliable ranges for “healthy” and “pathological” power characteristics may be fundamentally untenable. One approach to overcome this limitation is to compare, within the same individual, estimates of EEG activity before and after injury. Here, we tested the hypothesis that oscillatory changes within an individual can be used as a biomarker of chronic pain. Materials and Methods