  Current neuroimaging techniques provide paths to investigate the structure
and function of the brain in vivo and have made great advances in understanding
Alzheimer's disease (AD). However, the group-level analyses prevalently used
for investigation and understanding of the disease are not applicable for
diagnosis of individuals. More recently, deep learning, which can efficiently
analyze large-scale complex patterns in 3D brain images, has helped pave the
way for computer-aided individual diagnosis by providing accurate and automated
disease classification. Great progress has been made in classifying AD with
deep learning models developed upon increasingly available structural MRI data.
The lack of scale-matched functional neuroimaging data prevents such models
from being further improved by observing functional changes in pathophysiology.
Here we propose a potential solution by first learning a
structural-to-functional transformation in brain MRI, and further synthesizing
spatially matched functional images from large-scale structural scans. We
evaluated our approach by building computational models to discriminate
patients with AD from healthy normal subjects and demonstrated a performance
boost after combining the structural and synthesized functional brain images
into the same model. Furthermore, our regional analyses identified the temporal
lobe to be the most predictive structural-region and the parieto-occipital lobe
to be the most predictive functional-region of our model, which are both in
concordance with previous group-level neuroimaging findings. Together, we
demonstrate the potential of deep learning with large-scale structural and
synthesized functional MRI to impact AD classification and to identify AD's
neuroimaging signatures.
