  Effective representation of brain anatomical architecture is fundamental in
understanding brain regularity and variability. Despite numerous efforts, it is
still difficult to infer reliable anatomical correspondence at finer scale,
given the tremendous individual variability in cortical folding patterns. It is
even more challenging to disentangle common and individual patterns when
comparing brains at different neuro-developmental stages. In this work, we
developed a novel learning-based few-shot embedding framework to encode the
cortical folding patterns into a latent space represented by a group of
anatomically meaningful embedding vectors. Specifically, we adopted 3-hinge
(3HG) network as the substrate and designed an autoencoder-based embedding
framework to learn a common embedding vector for each 3HG's multi-hop feature:
each 3HG can be represented as a combination of these feature embeddings via a
set of individual specific coefficients to characterize individualized
anatomical information. That is, the regularity of folding patterns is encoded
into the embeddings, while the individual variations are preserved by the
multi=hop combination coefficients. To effectively learn the embeddings for the
population with very limited samples, few-shot learning was adopted. We applied
our method on adult HCP and pediatric datasets with 1,000+ brains (from 34
gestational weeks to young adult). Our experimental results show that: 1) the
learned embedding vectors can quantitatively encode the commonality and
individuality of cortical folding patterns; 2) with the embeddings we can
robustly infer the complicated many-to-many anatomical correspondences among
different brains and 3) our model can be successfully transferred to new
populations with very limited training samples.
