Abstract
As a non-invasive brain imaging technology, functional magnetic
resonance imaging (fMRI) provides a basic tool for brain functional
network modeling and brain disease diagnosis. Problems, such as large
number of parameters, low training efficiency, and poor
interpretability, are encountered in mainstream models because of the
high complexity of fMRI and brain networks. To solve these problems, a
novel structure feature combined graph neural network (SFC-GNN) with a
low number of parameters is proposed. In particular, SFC-GNN is composed
of 1) the graph convolution layer of the brain region perception and 2)
the node pooling layer of the graph structure feature (GSF). It also
receives the sparse brain graph modeled by each subject’s fMRI as input.
Especially, the GSF layer can select brain regions that are important
for classification, thereby localizing all active regions related to
brain disease. Moreover, a group network is constructed according to the
correlation among subjects, and SFC-GNN can be extended further to a
node classification model to achieve better diagnosis performance. The
proposed method has been validated on the ABIDE and ADNI datasets,
thereby showing the effectiveness of our proposed method in various
experiments.