Predicting Future Cognitive Decline with Hyperbolic Stochastic Coding

TL;DR

This paper introduces hyperbolic stochastic coding, a new framework that leverages hyperbolic geometry to improve the prediction of cognitive decline from brain surface data, especially in small datasets.

Contribution

The paper proposes a novel hyperbolic stochastic coding framework that reduces feature dimensionality and enhances predictive power in brain morphology analysis.

Findings

Achieves superior classification results on brain imaging tasks.

Enriches surface-based brain imaging analysis tools.

Potentially useful for individualized treatment strategies.

Abstract

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.