  This article focuses on a multi-modal imaging data application where
structural/anatomical information from gray matter (GM) and brain connectivity
information in the form of a brain connectome network from functional magnetic
resonance imaging (fMRI) are available for a number of subjects with different
degrees of primary progressive aphasia (PPA), a neurodegenerative disorder (ND)
measured through a speech rate measure on motor speech loss. The
clinical/scientific goal in this study becomes the identification of brain
regions of interest significantly related to the speech rate measure to gain
insight into ND patterns. Viewing the brain connectome network and GM images as
objects, we develop an integrated object response regression framework of
network and GM images on the speech rate measure. A novel integrated prior
formulation is proposed on network and structural image coefficients in order
to exploit network information of the brain connectome while leveraging the
interconnections among the two objects. The principled Bayesian framework
allows the characterization of uncertainty in ascertaining a region being
actively related to the speech rate measure. Our framework yields new insights
into the relationship of brain regions associated with PPA, offering a deeper
understanding of neuro-degenerative patterns of PPA. The supplementary file
adds details about posterior computation and additional empirical results.
