Topological Data Analysis of Resting-State fMRI Suggests Altered Brain Network Topology in Functional Dyspepsia: A Mapper-Based Parcellation Approach

Abstract

Functional dyspepsia (FD) is a complex condition identified by chronic indigestion without an obvious organic cause, characterized by diverse abdominal symptoms. Recent studies employing resting-state functional magnetic resonance imaging (rs-fMRI) have investigated gut-brain interactions in FD. These studies report altered functional connectivity patterns that are associated with the severity of the disease. The investigation of resting-state functional connectivity patterns involves defining connectivity nodes for subsequent graph-theory analyses, thus emphasizing the importance of brain parcellation. While traditional methods employ predefined brain atlases, fMRI-driven parcellation offers a more specific approach able to extract functionally homogeneous regions. In this study, we applied the Topological Data Analysis (TDA) tool of Mapper algorithm to rs-fMRI data to develop a whole-brain TDA-driven fMRI parcellation pipeline. This functional parcellation, applied in a group of healthy controls (HC), provides a reference for comparing network properties between HC and FD groups. We propose that the TDA Mapper is able to recover structure in rs-fMRI data, showing that topological complexes embedded in fMRI data could be identified and explored using this tool. Based on the brain network thus derived, we highlight the potential of applying graph analysis on rs-fMRI data to assess topological properties of brain connectivity, showing significant differences between groups in the functional parcel located in the frontal pole for nodal strength and degree.