Resting-State Functional MRI Adaptation with Attention Graph Convolution Network for Brain Disorder Identification.

ABSTRACT: Multi-site resting-state functional magnetic resonance imaging (rs-fMRI) data can facilitate learning-based approaches to train reliable models on more data. However, significant data heterogeneity between imaging sites, caused by different scanners or protocols, can negatively impact the generalization ability of learned models. In addition, previous studies have shown that graph convolution neural networks (GCNs) are effective in mining fMRI biomarkers. However, they generally ignore the potentially different contributions of brain regions- of-interest (ROIs) to automated disease diagnosis/prognosis. In this work, we propose a multi-site rs-fMRI adaptation framework with attention GCN (A²GCN) for brain disorder identification. Specifically, the proposed A²GCN consis

SUBMITTER: Chu Y 

PROVIDER: S-EPMC9599902 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature