  We propose a novel matrix autoencoder to map functional connectomes from
resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor
Imaging (DTI), as guided by subject-level phenotypic measures. Our specialized
autoencoder infers a low dimensional manifold embedding for the rs-fMRI
correlation matrices that mimics a canonical outer-product decomposition. The
embedding is simultaneously used to reconstruct DTI tractography matrices via a
second manifold alignment decoder and to predict inter-subject phenotypic
variability via an artificial neural network. We validate our framework on a
dataset of 275 healthy individuals from the Human Connectome Project database
and on a second clinical dataset consisting of 57 subjects with Autism Spectrum
Disorder. We demonstrate that the model reliably recovers structural
connectivity patterns across individuals, while robustly extracting predictive
and interpretable brain biomarkers in a cross-validated setting. Finally, our
framework outperforms several baselines at predicting behavioral phenotypes in
both real-world datasets.
