  Mapping of human brain structural connectomes via diffusion MRI offers a
unique opportunity to understand brain structural connectivity and relate it to
various human traits, such as cognition. However, the presence of motion
artifacts during image acquisition can compromise the accuracy of connectome
reconstructions and subsequent inference results. We develop a generative model
to learn low-dimensional representations of structural connectomes that are
invariant to motion artifacts, so that we can link brain networks and human
traits more accurately, and generate motion-adjusted connectomes. We applied
the proposed model to data from the Adolescent Brain Cognitive Development
(ABCD) study and the Human Connectome Project (HCP) to investigate how our
motion-invariant connectomes facilitate understanding of the brain network and
its relationship with cognition. Empirical results demonstrate that the
proposed motion-invariant variational auto-encoder (inv-VAE) outperforms its
competitors in various aspects. In particular, motion-adjusted structural
connectomes are more strongly associated with a wide array of cognition-related
traits than other approaches without motion adjustment. Open source code is
available at https://github.com/yzhang511/inv-vae.
