Preliminary structural MRI based brain classification of chronic pelvic pain: A MAPP network study

Abstract

Neuroimaging studies have shown that changes in brain morphology often accompany chronic pain conditions. However, brain biomarkers that are sensitive and specific to chronic pelvic pain (CPP) have not yet been adequately identified. Using data from the Trans-MAPP Research Network, we examined the changes in brain morphology associated with CPP. We used a multivariate pattern classification approach to detect these changes and to identify patterns that could be used to distinguish participants with CPP from age-matched healthy controls. In particular, we used a linear support vector machine (SVM) algorithm to differentiate gray matter images from the 2 groups. Regions of positive SVM weight included several regions within the primary somatosensory cortex, pre-supplementary motor area, hippocampus, and amygdala were identified as important drivers of the classification with 73% overall accuracy. Thus, we have identified a preliminary classifier based on brain structure that is able to predict the presence of CPP with a good degree of predictive power. Our regional findings suggest that in individuals with CPP, greater gray matter density may be found in the identified distributed brain regions, which are consistent with some previous investigations in visceral pain syndromes. Future studies are needed to improve upon our identified preliminary classifier with integration of additional variables and to assess whether the observed differences in brain structure are unique to CPP or generalizable to other chronic pain conditions.

Keywords: Gray matter density; Machine learning; SVM; Support vector machine; UCPPS.

Figure 1

The distance in arbitrary units of each MR image from the trained SVM’s decision boundary (distance = 0) for individual participants with chronic pelvic pain (CPP) and individual healthy controls (HC). SVM prediction: positive distance is classified as CPP, while negative distance as HC. Grayed out data points are misclassified images.

Figure 2

Regions of positive SVM weight indicative of greater GM density in CPP as compared with HCs. Regions included the L. superior frontal gyrus / pre-supplementary motor area (A), L. postcentral gyrus / paracentral lobule (B), R. primary somatosensory cortex (C), L. primary somatosensory cortex (D) and R. lateral primary somatosensory cortex (E), L. parahippocampal gyrus / amygdala (F), R. parahippocampal gyrus / hippocampus (G), and L. parahippocampal gyrus / hippocampus (H). All regions were FDR-corrected for multiple comparisons (q < 0.05); minimum cluster size = 20. (See Table 2 for details.)

Figure 3

Scatter plots showing correlation of GM density with pain duration in different stages of chronic pain (left S1 and right S1 for duration > 5 years, left hippocampus for duration < 10 years, and left amygdala for duration < 7.5 years, see Table 3). Gray points are outside the range of symptom duration where correlation was observed.