Introduction

Previous research to identify neuroimaging signatures of chronic pain has led to varied results. While morphological brain changes may contribute to the persistence and evolution of chronic pain, numerous reports have observed decreases while others observe increases in GM density as a function of chronic pain [1,2]. Similarly, the use of MRS to characterize neurochemical abnormalities, such as the dysregulation of neurotransmitters including glutamate, is an increasingly common approach. However, both increases and decreases in brain metabolites have been reported, resulting in a limited understanding of the role of brain metabolites in pain perception. Taken together, there is a clear need for improved non-invasive MR biomarkers for prognostication and treatment monitoring to further explore the underlying mechanisms that drive chronic centralized pain. Here, we present a proof-of-concept approach using a generalized linear model to characterize changes in brain metabolites in a cohort of temporomandibular disorder (TMD) patients with chronic pain.

Methods

A schematic of the study design is shown in Figure 1. After providing written informed consent, MR data and quantitative sensory testing (QST) metrics were acquired in TMD patients (n=16) and healthy controls (HCs; n=12). MR data was acquired on a 3T MR scanner (GE Signa) using a 32-channel receive array head coil. T1-weighted SPGR (TR/TE = 650/3.7 ms, flip angle = 8°, voxel resolution = 0.5x0.5x0.4 mm³, NEX = 1) and single-voxel PRESS MRS (TR/TE = 1500/35 ms, flip angle = 90°, complex data points = 4096, 2 cm isotropic voxel in the anterior cingulate cortex, ACC) were acquired. MR spectra were analyzed with LCModel (v6.3-1L) [3]. Tissue metabolite concentrations were corrected for cerebrospinal fluid (CSF) after segmentation with FSL-FAST [4]. FSL-VBM (http://www.fmri-b.ox.ac.uk/fsl/) [5-7] was used to quantify GM morphometry in the ACC region using the following steps: 1) Brain tissue was extracted using BET [8]. 2) A study-specific template was formed using a randomly chosen subset of subjects (11 HCs and 11 TMD patients) and nonlinear registration using FNIRT, image averaging, tissue segmentation, and affine registration to the MNI152 template using FLIRT [9]. 3) Individual GM images were nonlinearly registered to the template and modulated with a Jacobian of the warp field. 4) These images were then smoothed with a Gaussian kernel with sigma of 7 mm. 6) Finally, an ACC region of interest (ROI) mask was generated and used to calculate regional GM density (mm³) for each subject using the individual images that were registered to the study-specific template. Quantitative sensory testing (QST) metrics were used to quantify pain perception and included: current face pain as a marker for chronic pain symptoms; symptom severity using the widespread pain index and fibromyalgia-ness scores; fibromyalgia determined with the American College of Rheumatology’s 2011 Fibromyalgia Criteria; pressure algometry in the trapezius and knee muscles; and cuff algometry using a blood pressure cuff inflated to 160 mm Hg. Statistical analysis was performed in SPSS v26 (IBM) using generalized linear models. Significance was determined at p<0.05.

Results and Discussion

Descriptive statistics are shown in Table 1. Significant differences in metrics of centralized pain including current face pain, fibromyalgia score, and symptom severity were observed in TMD patients compared to HCs. Age and sex were not significantly different between groups. Initial analysis included linear regressions of glutamate+glutamine (Glx) with QST metrics in TMD and HCs (Tables 2,3). Significant associations of Glx with QST metrics were observed when accounting for GM density in TMD patients (GLM regressions, Table 2). Inclusion of GM density in the ACC ROI as an additional linear covariate in our model resulted in improved model fits for TMD patients. In comparison, Glx was not significantly associated with most QST metrics in healthy controls (Table 3). Brain metabolite concentrations are known to vary as a function of GM volume within and between subjects [10]. As a result, GM volume or density must be considered as a potential covariate when characterizing changes in metabolites, particularly as GM volumes have been observed to significantly vary with symptom severity. Significant model associations between Glx and pain sensitivity in the knee and trapezius muscles using algometry were observed, suggesting that dysregulation of brain metabolites may be associated with heightened pain perception. While some studies that account for differences in GM use average GM concentrations as a correction factor, or MRS voxel segmentation to quantify GM fractions, the use of morphometry is an alternative correction approach presented here. The benefit of incorporating GM density from morphometry, rather than GM fractions from voxel segmentation, is that morphometry accounts for differences in brain size and local concentration variations between subjects and does not assume GM metabolite concentrations a priori. Importantly, previous discrepancies in reported metabolite concentrations may be attributed to variations in GM morphometry. Explicitly including GM density as a covariate in the model can account for tissue differences at the subject level.

Conclusion

Improved predictions of the association between brain metabolites and pain perception was facilitated with a generalized linear model that accounts for differences in GM density. Morphometry may be a promising alternative to voxel segmentation to perform tissue composition corrections when quantifying brain metabolite changes in chronic pain.

Acknowledgements

This work was supported in part by NIH K99DE026810.