Related Experiment Video
Updated: Apr 14, 2026
3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache
Published on: June 2, 2014
Multivariate morphological brain signatures predict patients with chronic abdominal pain from healthy control
Jennifer S Labus1, John D Van Horn, Arpana Gupta
1Oppenheimer Family Center for the Neurobiology of Stress Departments of Departments of Medicine and Division of Digestive Diseases, UCLA, Los Angeles, CA, USA Physiology, UCLA, Los Angeles, CA, USA, and Psychiatry, UCLA, Los Angeles, CA, USA Pain and Interoception Network (PAIN), Los Angeles, CA, USA The Institute for Neuroimaging and Informatics (INI) and Laboratory of Neuro Imaging (LONI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA Department of Biomedical Engineering, UCLA, Los Angeles, CA, USA VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
Abstract:
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.
