Detection of Discriminative Neurological Circuits Using Hierarchical Graph Convolutional Networks in fMRI Sequences

Abstract

Graph convolutional network (GCN) has shown its potential on modeling functional MRI connectivity and recognizing neurological disease tasks. However, conventional GCN layers generally inherit the original graph topology, without the modeling of hierarchical graph representation. Besides, although the interpretability of GCN has been widely investigated, such studies only identify several independently affected brain regions instead of forming them as neurological circuits, which are more desirable for disease mechanism investigation. In this paper, we propose a hierarchical dynamic GCN (HD-GCN), which combines the information from both low-order graph composed of brain regions and high-order graph composed of brain region clusters. The algorithm learns a consistent dynamic graph pooling, which helps improve the classification accuracy by hierarchical graph representation learning and could identify the affected neurological circuits. We employed two datasets to evaluate the generalizability of the proposed method: ADNI dataset containing 177 AD patients and 115 controls, and Obsessive-Compulsive Disorder (OCD) dataset including 67 patients and 61 controls. The classification accuracy reaches$$89.4\%$$ on ADNI dataset and$$89.1\%$$ on OCD dataset. The affected brain circuits were also identified, which are consistent with previous psychological studies.