  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.
