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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Bagarinao, E; Johnson, KA; Martucci, KT; Ichesco, E; Farmer, MA; Labus, J; Ness, TJ; Harris, R; Deutsch, G; Apkarian, AV; Mayer, EA; Clauw, DJ; Mackey, S

Published Date

  • December 2014

Published In

Volume / Issue

  • 155 / 12

Start / End Page

  • 2502 - 2509

PubMed ID

  • 25242566

Pubmed Central ID

  • 25242566

Electronic International Standard Serial Number (EISSN)

  • 1872-6623

Digital Object Identifier (DOI)

  • 10.1016/j.pain.2014.09.002

Language

  • eng

Conference Location

  • United States