Dynamic Sub-graph Learning for Patch-Based Cortical Folding Classification

Abstract

Surface mapping techniques have been commonly used for the alignment of cortical anatomy and the detection of gray matter thickness changes in Alzheimer’s disease (AD) imaging research. Two major hurdles exist in further advancing the accuracy in cortical analysis. First, high variability in the topological arrangement of gyral folding patterns makes it very likely that sucal area in one brain will be mapped to the gyral area of another brain. Second, the considerable differences in the thickness distribution of the sulcal and gyral area will greatly reduce the power in atrophy detection if misaligned. To overcome these challenges, it will be desirable to identify brains with cortical regions sharing similar folding patterns and perform anatomically more meaningful atrophy detection. To this end, we propose a patch-based classification method of folding patterns by developing a novel graph convolutional neural network (GCN). We focus on the classification of the precuneus region in this work because it is one of the early cortical regions affected by AD and considered to have three major folding patterns. Compared to previous GCN-based methods, the main novelty of our model is the dynamic learning of sub-graphs for each vertex of a surface patch based on distances in the feature space. Our proposed network dynamically updates the vertex feature representation without overly smoothing the local folding structures. In our experiments, we use a large-scale dataset with 980 precuneus patches and demonstrate that our method outperforms five other neural network models in classifying precuneus folding patterns.