Interpretable Graph Convolutional Network of Multi-Modality Brain Imaging for Alzheimer's Disease Diagnosis

TL;DR

This paper introduces an interpretable GCN framework that leverages multi-modality brain imaging data to identify biomarkers and improve classification of Alzheimer's disease, providing insights into disease-related brain regions.

Contribution

The study extends Grad-CAM to GCNs for brain connectivity analysis, enabling identification of disease-specific biomarkers across multiple imaging modalities.

Findings

Effective ROI features for AD classification and score prediction

Successful identification of AD and MCI biomarkers

Enhanced diagnostic performance using multi-modality data

Abstract

Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting the difference of features between regions in healthy control (HC), mild cognitive impairment (MCI), and AD groups. We conducted the experiments on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and showed that the ROI features learned by our method were effective for enhancing the performances of both clinical score prediction and disease status identification. It also successfully identified biomarkers associated with AD and MCI.