Integrated multimodal network approach to PET and MRI based on multidimensional persistent homology

TL;DR

This paper introduces a novel multimodal network analysis method using multidimensional persistent homology to explore and discriminate brain network differences in ASD children versus controls across PET and MRI data.

Contribution

It extends persistent homology to visualize and analyze integrated brain networks with varying thresholds and modality mixing ratios, revealing disease-related differences.

Findings

ASD children show significant asymmetrical network changes

Weaker connections in ASD within visual cortex and between key brain regions

Multidimensional homology uncovers disease-related network associations

Abstract

Finding the underlying relationships among multiple imaging modalities in a coherent fashion is one of challenging problems in the multimodal analysis. In this study, we propose a novel multimodal network approach based on multidi- mensional persistent homology. In this extension of the previous threshold-free method of persistent homology, we visualize and discriminate the topological change of integrated brain networks by varying not only threshold but also mixing ratios between two different imaging modalities. Moreover, we also pro- pose an integration method for multimodal networks, called one-dimensional projection, with a specific mixing ratio between modalities. We applied the proposed methods to PET and MRI data from 21 autism spectrum disorder (ASD) children and 10 pediatric control subjects. From the results, we found that the brain networks of ASD children and controls differ significantly, with ASD showing asymmetrical changes of connected structures between PET and MRI. The integrated MRI and PET networks showed that ASD children had weaker connections than controls within the visual cortex, between dorsal and ventral parts of the temporal pole, between frontal and parietal regions, and between the left perisylvian and other brain regions. These results provide a multidimensional homological understanding of disease-related PET and MRI networks that discloses the network association with ASD.