Population-based GCN method for diagnosis of Alzheimer's disease using brain metabolic or volumetric features.

• In this study, we constructed a population-based GCN framework by expressing the subject population as adjacency matrix in graph to achieve the diagnosis of AD. The nodes in graph are associated with individual features, and edges are weighted by combining the phenotypic information. • To compensate for the low specificity of ROI features from sMRI or PET image in graph analysis, we acquired individual features by constructing brain network of subject via health group. • Compared with the method of acquiring the ROI features of the brain regions as the input features of GCN, our proposed method remarkably improved the prediction accuracy by about 5 to 10 percentage based on both sMRI and PET images. • We further discussed the influence of phenotypic information on the classification performance of GCN model. Through our testing and experimental analysis on the ADNI dataset, our method achieved improved performance for AD diagnosis and mild cognitive impairment conversion prediction tasks. • Our proposed method also provides technical support for AD diagnosis of GCN method based on three-dimensional brain images. In addition, GCN can provide better flexibility for integrating multimodal features, which also informs AD diagnosis methods using multimodal data. As a deep learning method, graph convolution network (GCN) has the advantage of dealing with non-Euclidean domain problems and is constantly applied in the research of computer-aided diagnosis of Alzheimer's disease (AD). In graph-based methods for AD diagnosis, nodes can represent potential subjects with a set of vectors, and edges combine the interaction and similarity between subjects. However, for the three-dimensional neuroimage like structural Magnetic Resonance Imaging (sMRI) or Positron Emission Tomography (PET), due to the non-sequential of ROI (Region of Interest) features (compared with four-dimensional neuroimage), which makes the graph-based analysis approach more difficult. In this study, we obtained individual features by constructing brain network via health group indirectly, and then constructed a population-based GCN framework by expressing the subject population as adjacency matrix in graph to achieve the diagnosis of AD. The nodes in graph are associated with individual features, and edges are weighted by combining the phenotypic information, we further discuss the influence of phenotypic information on the classification performance of GCN. Compared with acquiring the ROI features of the brain regions as the input features of GCN, our proposed method remarkably improved the prediction accuracy based on both sMRI and PET images by about 5 to 10 percentage. Through our testing and experimental analysis on the public ADNI dataset, our method achieved improved performance for AD diagnosis and mild cognitive impairment conversion prediction tasks. Our proposed method also provides technical support for AD diagnosis using GCN method based on three-dimensional brain images. [ABSTRACT FROM AUTHOR]