Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: A MAPP network study

Jason J. Kutch*, Jennifer S. Labus, Richard E. Harris, Katherine T. Martucci, Melissa A. Farmer, Sonja Fenske, Connor Fling, Eric Ichesco, Scott Peltier, Bogdan Petre, Wensheng Guo, Xiaoling Hou, Alisa J. Stephens, Chris Mullins, Daniel J. Clauw, Sean C. Mackey, A. Vania Apkarian, J. Richard Landis, Emeran A. Mayer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Chronic pain symptoms often change over time, even in individuals who have had symptoms for years. Studying biological factors that predict trends in symptom change in chronic pain may uncover novel pathophysiological mechanisms and potential therapeutic targets. In this study, we investigated whether brain functional connectivity measures obtained from resting-state functional magnetic resonance imaging at baseline can predict longitudinal symptom change (3, 6, and 12 months after scan) in urologic chronic pelvic pain syndrome. We studied 52 individuals with urologic chronic pelvic pain syndrome (34 women, 18 men) who had baseline neuroimaging followed by symptom tracking every 2 weeks for 1 year as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We found that brain functional connectivity can make a significant prediction of short-term (3 month) pain reduction with 73.1% accuracy (69.2% sensitivity and 75.0% precision). In addition, we found that the brain regions with greatest contribution to the classification were preferentially aligned with the left frontoparietal network. Resting-state functional magnetic resonance imaging measures seemed to be less informative about 6- or 12-month symptom change. Our study provides the first evidence that future trends in symptom change in patients in a state of chronic pain may be linked to functional connectivity within specific brain networks.

Original languageEnglish (US)
Pages (from-to)1069-1082
Number of pages14
JournalPain
Volume158
Issue number6
DOIs
StatePublished - Jun 1 2017

Keywords

  • Chronic pain
  • Neuroimaging
  • Prediction
  • Urologic pain

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology
  • Anesthesiology and Pain Medicine

Fingerprint Dive into the research topics of 'Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: A MAPP network study'. Together they form a unique fingerprint.

Cite this