Multivariate morphological brain signatures predict patients with chronic abdominal pain from healthy control subjects.
Irritable bowel syndrome (IBS) is the most common chronic visceral pain disorder. The pathophysiology of IBS is incompletely understood; however, evidence strongly suggests dysregulation of the brain-gut axis. The aim of this study was to apply multivariate pattern analysis to identify an IBS-relate...
| Publicado en: | PAIN Vol. 156; no. 8; pp. 1545 - 1555 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
2015 Aug
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109598674&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109598674 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03043959 22N jtl: PAIN issn: 03043959 maglogo: N pubinfo: dt: 2015 Aug vid: 156 iid: 8 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 109598674 NLM25906347 2013095720 10.1097/j.pain.0000000000000196 NLM25906347 PMC4504800 109598674 ppf: 1545 ppct: 10 formats: tig: atl: Multivariate morphological brain signatures predict patients with chronic abdominal pain from healthy control subjects. aug: au: Labus, Jennifer S Van Horn, John D Gupta, Arpana Alaverdyan, Mher Torgerson, Carinna Ashe-McNalley, Cody Irimia, Andrei Hong, Jui-Yang Naliboff, Bruce Tillisch, Kirsten Mayer, Emeran A sug: ab: Irritable bowel syndrome (IBS) is the most common chronic visceral pain disorder. The pathophysiology of IBS is incompletely understood; however, evidence strongly suggests dysregulation of the brain-gut axis. The aim of this study was to apply multivariate pattern analysis to identify an IBS-related morphometric brain signature that could serve as a central biological marker and provide new mechanistic insights into the pathophysiology of IBS. Parcellation of 165 cortical and subcortical regions was performed using FreeSurfer and the Destrieux and Harvard-Oxford atlases. Volume, mean curvature, surface area, and cortical thickness were calculated for each region. Sparse partial least squares discriminant analysis was applied to develop a diagnostic model using a training set of 160 females (80 healthy controls and 80 patients with IBS). Predictive accuracy was assessed in an age-matched holdout test set of 52 females (26 healthy controls and 26 patients with IBS). A 2-component classification algorithm comprising the morphometry of (1) primary somatosensory and motor regions and (2) multimodal network regions explained 36% of the variance. Overall predictive accuracy of the classification algorithm was 70%. Small effect size associations were observed between the somatosensory and motor signature and nongastrointestinal somatic symptoms. The findings demonstrate that the predictive accuracy of a classification algorithm based solely on regional brain morphometry is not sufficient, but they do provide support for the utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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