Multivariate morphological brain signatures predict patients with chronic abdominal pain from healthy control subjects

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 which 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, 80 IBS). Predictive accuracy was assessed in an age matched holdout test set of 52 females (26 health controls, 26 IBS). A two-component classification algorithm comprised of the morphometry of 1) primary somato-sensory 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 non-gastrointestinal somatic symptoms. The findings demonstrate 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. Perspective This article presents the development, optimization, and testing of a classification algorithm for discriminating female IBS patients from healthy controls using only brain morphometry data. The results provide support for utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS.

Neuroimaging-based biomarker discovery and validation

“…Labus et al [16] took an exciting step toward a neuroimaging biomarker for IBS, and more broadly, chronic visceral pain. Taking Labus et al as a starting point, collaborative, multi-site efforts will help facilitate the biomarker development process, particularly focusing on the criteria above.…”

“…We briefly describe several such characteristics (summarized in Table 1 ), and then relate them to the findings of Labus et al [16]. …”

“…In this issue, Labus et al [16] developed a new neuroimaging biomarker for irritable bowel syndrome (IBS) using structural MRI data, based on a relatively large sample of 80 IBS patients and 80 healthy controls. They used sparse Partial Least Squares-Discriminant Analysis (sPLS-DA), a method that allowed them to both develop a classification model based on brain structure and identify the regions that make the most important contributions to the classification.…”

Brain Structure and Response to Emotional Stimuli as Related to Gut Microbial Profiles in Healthy Women

“…Based on the Destrieux and Harvard-Oxford atlases, for each cerebral hemisphere, a set of 74 cortical structures were labeled in addition to 7 subcortical structures, the cerebellum and the brain stem, resulting in a complete set of 165 parcellations for the entire brain. Four representative morphological measures were computed for each cortical parcellation: gray matter volume (GMV), surface area (SA), mean cortical thickness (CT), and mean curvature (MC) (35, 36). For subcortical regions only volume was computed.…”

Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations

“…Nevertheless, efforts are being made to decode pain from brain activity [51][52][53][54] . Similar efforts are being made to predict pain from brain structure, such as grey matter volume and white matter connectivity, but these efforts were beyond the scope of this task force and are discussed elsewhere [55][56][57][58] . Univariate and multivariate approaches have been applied to the analysis of brain imaging data in order to decode pain.…”