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Finding the underlying relationships among multiple imaging modalities in acoherent fashion is one of challenging problems in the multimodal analysis. Inthis study, we propose a novel multimodal network approach based on multidi-mensional persistent homology. In this extension of the previous threshold-freemethod of persistent homology, we visualize and discriminate the topologicalchange of integrated brain networks by varying not only threshold but alsomixing ratios between two different imaging modalities. Moreover, we also pro-pose an integration method for multimodal networks, called one-dimensionalprojection, with a specific mixing ratio between modalities. We applied theproposed methods to PET and MRI data from 21 autism spectrum disorder (ASD)children and 10 pediatric control subjects. From the results, we found that thebrain networks of ASD children and controls differ significantly, with ASDshowing asymmetrical changes of connected structures between PET and MRI. Theintegrated MRI and PET networks showed that ASD children had weaker connectionsthan controls within the visual cortex, between dorsal and ventral parts of thetemporal pole, between frontal and parietal regions, and between the leftperisylvian and other brain regions. These results provide a multidimensionalhomological understanding of disease-related PET and MRI networks thatdiscloses the network association with ASD.

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