Discriminative analysis of the human cortex using spherical CNNs - a study on Alzheimer's disease diagnosis

TL;DR

This paper demonstrates the effectiveness of spherical CNNs in analyzing cortical surface data for Alzheimer's disease diagnosis, achieving superior classification performance using structural MRI measures.

Contribution

First application of spherical CNNs to cortical neuroimaging for Alzheimer's diagnosis, showing improved classification and progression prediction.

Findings

Superior AD vs. normal classification accuracy

Effective prediction of MCI progression within two years

Potential extension to other neurological conditions

Abstract

In neuroimaging studies, the human cortex is commonly modeled as a sphere to preserve the topological structure of the cortical surface. Cortical neuroimaging measures hence can be modeled in spherical representation. In this work, we explore analyzing the human cortex using spherical CNNs in an Alzheimer's disease (AD) classification task using cortical morphometric measures derived from structural MRI. Our results show superior performance in classifying AD versus cognitively normal and in predicting MCI progression within two years, using structural MRI information only. This work demonstrates for the first time the potential of the spherical CNNs framework in the discriminative analysis of the human cortex and could be extended to other modalities and other neurological diseases.