Papers of the Week

Chronic pain has widespread, detrimental effects on the human nervous system. Its prevalence and burden increase with age. Machine learning techniques have been applied on brain images to produce statistical models of brain aging. Specifically, Gaussian process regression is particularly effective at predicting chronological age from neuroimaging data permitting the calculation of a brain age gap estimate (brain-AGE).Pathological biological processes such as chronic pain can influence brain-AGE. As chronic pain disorders can differ in etiology, severity, pain frequency, and sex-linked prevalence, we hypothesize that the expression of brain-AGE may be pain specific and differ between discrete chronic pain disorders.We built a machine learning model using T1-weighted anatomical MRI from 812 healthy controls to extract brain-AGE for 45 trigeminal neuralgia (TN), 52 osteoarthritis (OA), and 50 chronic low-back pain (BP) individuals. False discovery rate corrected Welch's t-tests were conducted to detect significant alterations in brain-AGE between each discrete pain cohort and age- and sex-matched controls.TN and OA, but not BP subjects, have significantly larger brain-AGE. In all 3 pain groups, we observed female-driven elevation in brain-AGE. Furthermore, in TN, a significantly larger brain-AGE is associated with response to Gamma Knife radiosurgery for TN pain and is inversely correlated with the age at diagnosis.Brain-AGE expression differs across distinct pain disorders with a pronounced sex effect for female subjects. In TN, younger females may therefore represent a vulnerable sub-population requiring expedited chronic pain intervention. To this end, brain-AGE holds promise as an effective biomarker of pain treatment response.