Diagnosis of schizophrenia with functional connectome data: a graph-based convolutional neural network approach.

ABSTRACT: Previous deep learning methods have not captured graph or network representations of brain structural or functional connectome data. To address this, we developed the BrainNet-Global Covariance Pooling-Attention Convolutional Neural Network (BrainNet-GA CNN) by incorporating BrainNetCNN and global covariance pooling into the self-attention mechanism. Resting-state functional magnetic resonance imaging data were obtained from 171 patients with schizophrenia spectrum disorders (SSDs) and 161 healthy controls (HCs). We conducted an ablation analysis of the proposed BrainNet-GA CNN and quantitative performance comparisons with competing methods using the nested tenfold cross validation strategy. The performance of our model was compared with competing methods. Discriminative connections were v

SUBMITTER: Oh KH 

PROVIDER: S-EPMC8764800 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature