Abstract
Using positron emission tomography, we recently demonstrated elevated brain levels of the 18 kDa translocator protein (TSPO), a glial activation marker, in chronic low back pain (cLBP) patients, compared to healthy controls (HCs). Here, we first sought to replicate the original findings in an independent cohort (15 cLBP, 37.8 ± 12.5 y/o; 18 HC, 48.2 ± 12.8 y/o). We then trained random forest machine learning algorithms based on TSPO imaging features combining discovery and replication cohorts (totaling 25 cLBP, 42.4 ± 13.2 y/o; 27 HC, 48.9 ± 12.6 y/o), to explore whether image features other than the mean contain meaningful information that might contribute to the discrimination of cLBP patients and HC. Feature importance was ranked using SHapley Additive exPlanations values, and the classification performance (in terms of area under the curve values) of classifiers containing only the mean, other features, or all features was compared using the DeLong test. Both region-of-interest and voxelwise analyses replicated the original observation of thalamic TSPO signal elevations in cLBP patients compared to HC (P < 0.05). The random forest-based analyses revealed that although the mean is a discriminating feature, other features demonstrate similar level of importance, including the maximum, kurtosis, and entropy. Our observations suggest that thalamic neuroinflammatory signal is a reproducible and discriminating feature for cLBP, further supporting a role for glial activation in human cLBP, and the exploration of neuroinflammation as a therapeutic target for chronic pain. This work further shows that TSPO signal contains a richness of information that the simple mean might fail to capture completely.