  Hyperbolic geometry has been successfully applied in modeling brain cortical
and subcortical surfaces with general topological structures. However such
approaches, similar to other surface based brain morphology analysis methods,
usually generate high dimensional features. It limits their statistical power
in cognitive decline prediction research, especially in datasets with limited
subject numbers. To address the above limitation, we propose a novel framework
termed as hyperbolic stochastic coding (HSC). Our preliminary experimental
results show that our algorithm achieves superior results on various
classification tasks. Our work may enrich surface based brain imaging research
tools and potentially result in a diagnostic and prognostic indicator to be
useful in individualized treatment strategies.
