title: 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.
authors.affiliation: Department of Anesthesiology, Perioperative and Pain Medicine, Division of Pain Medicine, Stanford University Medical Center, Stanford, CA, USA Department of Anesthesiology, Chronic Pain and Fatigue Research Center, University of Michigan, Ann Arbor, MI, USA Department of Physiology, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA Gail and Gerald Oppenheimer Family Center for Neurobiology of Stress, Pain and Interoception Network (PAIN), David Geffen School of Medicine at UCLA, Los Angeles, CA, USA Department of Radiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA Department of Anesthesiology, University of Alabama, Birmingham Medical Center, Birmingham, AL, USA.
authors.forename: Epifanio
authors.initials: E
authors.lastname: Bagarinao
authors.forename: Kevin A
authors.initials: KA
authors.lastname: Johnson
authors.forename: Katherine T
authors.initials: KT
authors.lastname: Martucci
authors.forename: Eric
authors.initials: E
authors.lastname: Ichesco
authors.forename: Melissa A
authors.initials: MA
authors.lastname: Farmer
authors.forename: Jennifer
authors.initials: J
authors.lastname: Labus
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Ness
authors.forename: Richard
authors.initials: R
authors.lastname: Harris
authors.forename: Georg
authors.initials: G
authors.lastname: Deutsch
authors.forename: Vania A
authors.initials: VA
authors.lastname: Apkarian
authors.forename: Emeran A
authors.initials: EA
authors.lastname: Mayer
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Clauw
authors.forename: Sean
authors.initials: S
authors.lastname: Mackey
mesh_terms: D000328:Adult; D001921:Brain / Q000473:pathology*; D059350:Chronic Pain / Q000145:classification* / Q000473:pathology*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D017699:Pelvic Pain / Q000145:classification* / Q000473:pathology*; D011569:Psychiatric Status Rating Scales; D011795:Surveys and Questionnaires
keywords: Gray matter density; Machine learning; SVM; Support vector machine; UCPPS