InVA: Integrative Variational Autoencoder for Harmonization of
  Multi-modal Neuroimaging Data
Bowen Lei Rajarshi Guhaniyogi Krishnendu Chandra Aaron Scheffler Bani Mallick
There is a significant interest in exploring non-linear associations among
multiple images derived from diverse imaging modalities. While there is a
growing literature on image-on-image regression to delineate predictive
inference of an image based on multiple images, existing approaches have
limitations in efficiently borrowing information between multiple imaging
modalities in the prediction of an image. Building on the literature of
Variational Auto Encoders (VAEs), this article proposes a novel approach,
referred to as Integrative Variational Autoencoder (\texttt{InVA}) method,
which borrows information from multiple images obtained from different sources
to draw predictive inference of an image. The proposed approach captures
complex non-linear association between the outcome image and input images,
while allowing rapid computation. Numerical results demonstrate substantial
advantages of \texttt{InVA} over VAEs, which typically do not allow borrowing
information between input images. The proposed framework offers highly accurate
predictive inferences for costly positron emission topography (PET) from
multiple measures of cortical structure in human brain scans readily available
from magnetic resonance imaging (MRI).