Chronic pain conditions represent a significant health and economic burden and are leading contributors to disability worldwide. While such conditions can impose substantial suffering, they are often resistant to conventional therapies and can be difficult to diagnose. It is estimated that between one-third and one-half of the UK population is affected by chronic pain. This high prevalence makes the development of safe and effective treatments an important area of research.
Chronic pain is commonly measured using patients’ subjective reports which can present difficulties in quantitation, reliability, and between-patient comparability, adding to difficulties in diagnosis and treatment. Therefore, the identification of objective biomarkers could facilitate not only the diagnosis of chronic pain conditions and classification of pain pathophysiology, but also assist with disease prognostication or prediction of therapy response, and aid in the development of safe and effective therapies.
While most previous attempts to identify pain biomarkers have used healthy participants and adopted experimental thermal pain, it is unclear to what extent findings from healthy human participants or mixed pain syndromes translated to patients with chronic neuropathic pain. As such, the authors of a recent article focused on the first in-human long-term, ambulatory, intracranial recordings in four participants with chronic, neuropathic pain in order to identify objective biomarkers that could predict spontaneous, chronic pain state in patients.
Methods of the Study
Four participants were implanted with intracranial electrodes in the anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC). The researchers conducted longitudinal pain-state tracking whereby each participant provided pain score reports multiple times daily over several months. All four participants reported 11-point pain intensity using a numerical rating score (NRS), while two also provided pain intensity via a visual analogue score (VAS), the short-form McGill Pain Questionnaire (SF-MPQ) and pain unpleasantness (NRS and VAS).
The researchers used ambulatory intracranial recordings of electrical signals from the anterior cingulate cortex and orbitofrontal cortex to predict various measures of chronic pain severity, with high sensitivity utilising machine learning.
Results
Overall, chronic pain states could be identified due to electrical activity in the contralateral orbitofrontal cortex to the side in which the participant experienced pain. This biomarker in three out of the four participants remained stable over months of recordings.
Next, the researchers aimed to compare with neural mechanisms supporting chronic pain by identifying biomarkers of acute, experimental thermal pain state. To this end, brain recordings and pain ratings were collected while participants were subjected repeated heat stimuli between 32oC (painless) and 48oC (painful) in the area of the body affected by chronic pain and the same area of the body on the opposite side.
In two participants, the researchers were able to predict acute, evoked thermal pain during the experimental heat task. It was observed that, while chronic pain decoding tended to rely more on activity in the orbitofrontal cortex, acute pain decoding was supported more by anterior cingulate cortex activity. Furthermore, the time course of neural activity supporting chronic pain decoding reflected sustained increases or decreases in power in the order of seconds, while acute pain decoding was associated with more frequent and transient changes in power. This demonstrates that spontaneous, chronic pain states can be predicted longitudinally from direct brain activity detected by implanted intracranial electrodes.
Conclusions
Previous neuroimaging studies of chronic back pain have identified the importance of the anterior cingulate cortex and medial front cortex, however, have failed to acknowledge the role of the orbitofrontal cortex. This study found that activity from either anterior cingulate cortex or orbitofrontal cortex alone was sufficient to track pathological electrical activity corresponding to changes in underlying chronic pain.
These findings suggest that signals in the orbitofrontal cortex could track current chronic pain severity for neuropathic syndromes, such as central post-stroke pain or phantom limb pain. However, the researchers note that “given the small sample size of the present study, and the idiosyncratic decoding observations in acute pain from 1-2 participants, caution must be used to avoid overinterpretation”. It is recommended that further studies are conducted to establish greater confidence in the specificity of the orbitofrontal cortex for chronic pain prediction across larger groups.
The development of personalised biomarkers will be central to the accurate diagnosis and tracking prognosis of chronic pain conditions and in the development of future treatments and therapies.
